Pharmacogenetics of Breast Cancer - JOHN J. HADDAD, Ph.D.
Pharmacogenetics of Breast Cancer - JOHN J. HADDAD, Ph.D.
Pharmacogenetics of Breast Cancer - JOHN J. HADDAD, Ph.D.
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Translational Medicine Series 7<br />
<strong><strong>Ph</strong>armacogenetics</strong><br />
<strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong><br />
Towards the<br />
Individualization <strong>of</strong> Therapy<br />
Edited by<br />
Brian Leyland-Jones
TRANSLATIONAL MEDICINE SERIES<br />
1. Prostate <strong>Cancer</strong>: Translational and Emerging Therapies, edited by<br />
Nancy A. Dawson and W. Kevin Kelly<br />
2. <strong>Breast</strong> <strong>Cancer</strong>: Translational Therapeutic Strategies, edited by Gary<br />
Lyman and Harold Burstein<br />
3. Lung <strong>Cancer</strong>: Translational and Emerging Therapies, edited by<br />
Kishan J. Pandya, Julie R. Brahmer, and Manuel Hidalgo<br />
4. Multiple Myeloma: Translational and Emerging Therapies, edited by<br />
Kenneth C. Anderson and Irene Ghobrial<br />
5. <strong>Cancer</strong> Supportive Care: Advances in Therapeutic Strategies,<br />
edited by Gary H. Lyman and Jeffrey Crawford<br />
6. <strong>Cancer</strong> Vaccines: Challenges and Opportunities in Translation,<br />
edited by Adrian Bot and Mihail Obrocea<br />
7. <strong><strong>Ph</strong>armacogenetics</strong> <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong>: Towards the Individualization<br />
<strong>of</strong> Therapy, edited by Brian Leyland-Jones
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Library <strong>of</strong> Congress Cataloging-in-Publication Data<br />
<strong><strong>Ph</strong>armacogenetics</strong> <strong>of</strong> breast cancer : towards the individualization <strong>of</strong> therapy / edited by<br />
Brian Leyland-Jones.<br />
p. ; cm. — (Translational medicine series ; 7)<br />
Includes bibliographical references and index.<br />
ISBN-13: 978-1-4200-5293-0 (hardcover : alk. paper)<br />
ISBN-10: 1-4200-5293-4 (hardcover : alk. paper)<br />
1. <strong>Breast</strong>—<strong>Cancer</strong>—Genetic aspects. 2. <strong>Breast</strong>—<strong>Cancer</strong>—Chemotherapy.<br />
3. <strong><strong>Ph</strong>armacogenetics</strong>. I. Leyland-Jones, Brian. II. Series.<br />
[DNLM: 1. <strong>Breast</strong> Neoplasms—genetics. 2. <strong>Breast</strong> Neoplasms—drug therapy.<br />
WP 870 P536 2008]<br />
RC280.B8P43 2008<br />
616.99 0 449—dc22<br />
2008001013<br />
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Preface<br />
I write this preface on Christmas Day, 2007, in Costa Rica. Christmas is always a<br />
special time for me, since my parents were married on Christmas Day sixty<br />
nine years ago. It is almost unimaginable to think how far we have progressed<br />
since 1938. My parents were married in the year before the Second World<br />
War. The nitrogen mustards, from which arose the first chemotherapies, were<br />
developed during the war. I was fortunate during my training at Sloan-Kettering<br />
to work in the laboratories <strong>of</strong> Fred <strong>Ph</strong>illips, Joe Burchenal, and Jack Fox, which<br />
conducted some <strong>of</strong> the pivotal work on these compounds. Indeed, the first task<br />
force for childhood leukemia was set up in Washington in the year <strong>of</strong> my birth.<br />
However, we all have to remember how slowly treatments are disseminated to<br />
the community; I can remember, as a child, my next-door neighbor having a leg<br />
amputated for metastatic lung cancer since no other treatments were accessible<br />
locally!<br />
The rest is history, but again it is difficult to believe that the Nobel Prize<br />
for the discovery <strong>of</strong> the cellular origin <strong>of</strong> retroviral oncogenes was awarded to<br />
Bishop and Varmus only in 1989.<br />
Now we face the challenge <strong>of</strong> too few patients eligible for clinical trials to<br />
adequately test the myriad <strong>of</strong> targeted therapies available as well as the availability<br />
<strong>of</strong> high-quality starting material with associated clinical and phenotypic<br />
data. Various platforms are available for the identification <strong>of</strong> biomarkers: early<br />
technologies have focused on reverse transcriptase polymerase chain reaction<br />
and expression arrays. Now single nucleotide polymorphisms, array comparative<br />
genomic hybridization, methylation signatures, microRNAs, and proteomics are<br />
increasingly included in biomarker pr<strong>of</strong>iling; nanotechnology, molecular imaging,<br />
and the use <strong>of</strong> circulating tumor cells are advancing at a tremendous pace.<br />
By the time this book is released, the specimen collection guidelines for breast<br />
cancer, representing a joint effort between the North American cooperative<br />
iii
iv<br />
Preface<br />
groups and those comprising the <strong>Breast</strong> International Group (BIG), will have<br />
been published and be available for open Web access. We have attempted to<br />
cover the breadth <strong>of</strong> these diverse areas in this book.<br />
I am especially proud <strong>of</strong> three aspects <strong>of</strong> the book. The first is the timeliness,<br />
as so many new approaches to individualization <strong>of</strong> therapy have emerged, and<br />
when two very large randomized trials are aggressively accruing on the basis <strong>of</strong><br />
Oncotype and mammoprint. Second, the diversity <strong>of</strong> the topics covered, ranging<br />
from pathology, tissue handling, DNA-, expression-, and epigenetic-based<br />
approaches, nanotechnology, stem cells, circulating tumor cells, endocrine, and<br />
chemotherapeutic through to pharmacologic aspects and the key pivotal trials.<br />
Third, I am proud that so many <strong>of</strong> my most distinguished friends and colleagues<br />
from around the world have been so generous with their time and devotion to<br />
make this book outstanding.<br />
Finally, in writing this preface from one <strong>of</strong> the most beautiful places on<br />
earth, I am deeply reminded <strong>of</strong> the disparities in access to healthcare. We are all<br />
immensely grateful that my friends and colleagues John Seffrin and Otis<br />
Brawley at the American <strong>Cancer</strong> Society have been leaders in bringing this<br />
critical issue to the forefront <strong>of</strong> public consciousness, but we must never fail to<br />
remember that severe disparities in access to care remain, not only in the United<br />
States but globally. Individualized therapy is not only meant to direct therapy to<br />
those who will benefit most but also intended to reduce waste and improve cure<br />
rates dramatically, so that this therapy is available to all who suffer. Let us, the<br />
privileged, never move our focus from the most disadvantaged in our world and<br />
aim at using individualized therapy as a means to make our most sophisticated<br />
treatment available and affordable to all.<br />
Brian Leyland-Jones, M.D., <strong>Ph</strong>.D.
Acknowledgments<br />
Many thanks are owed on a personal level, especially to all my friends and<br />
coauthors who have been immensely generous in dedicating their nonexistent<br />
spare moments to contributing the various chapters. I am deeply grateful to the<br />
publisher, Informa Healthcare, who not only had the insight to partner with<br />
me on this book but also had the patience to wait for the “nonexistent spare<br />
moments” <strong>of</strong> the contributing authors to materialize. I must also pr<strong>of</strong>oundly<br />
apologize to several colleagues who were approached early on to contribute<br />
chapters but who were unable to because <strong>of</strong> the original time lines and now must<br />
be frustrated because the time lines were forced to be extended considerably.<br />
Most especially, I must thank my dear, dear colleague, Dr. Brian R. Smith, who<br />
was relentless in organization, follow-up, and advice, a man whose wisdom I<br />
rely upon at so many critical moments and to whose structured approach I aspire.<br />
I am deeply, deeply grateful to Charles Bronfman, whose chair in the memory <strong>of</strong><br />
his dear sister, Minda, sustained me over the past 17 years and who now has<br />
inspirationally established his own Institute <strong>of</strong> Personalized <strong>Cancer</strong> Therapy in<br />
New York. I am grateful also to the Flanders family who have shown me a depth<br />
<strong>of</strong> kindness, support, and friendship that is without equal. Finally, I am humbled<br />
by my research and clinical colleagues who instruct and guide me constantly,<br />
and especially by each and every patient who inspires us and teaches, in each and<br />
every encounter, just how personalized breast cancer therapy really is.<br />
v
Contents<br />
Preface . . . . . . . . . . iii<br />
Acknowledgments . . . . v<br />
Contributors . . . . . . . . xi<br />
1. <strong><strong>Ph</strong>armacogenetics</strong> <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong>: Toward the Individualization<br />
<strong>of</strong> Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1<br />
Jenny C. Chang, Susan G. Hilsenbeck, and Suzanne A. W. Fuqua<br />
2. <strong>Ph</strong>armacology, <strong><strong>Ph</strong>armacogenetics</strong>, and <strong>Ph</strong>armacoepidemiology:<br />
Three P’s <strong>of</strong> Individualized Therapy . . . . . . . . . . . . . . . . . . . . 11<br />
Shaheenah Dawood<br />
3. Role <strong>of</strong> Genetic Variability in <strong>Breast</strong> <strong>Cancer</strong> Treatment<br />
Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br />
Kandace L. Amend, Ji-Yeob Choi, and Christine B. Ambrosone<br />
4. The Role <strong>of</strong> Epigenetics in <strong>Breast</strong> <strong>Cancer</strong>: Implications for<br />
Diagnosis, Prognosis, and Treatment . . . . . . . . . . . . . . . . . . . . 45<br />
Amy M. Dworkin, Tim H.-M. Huang, and Amanda E. Toland<br />
5. Comparative Genomic Hybridization and Copy Number<br />
Abnormalities in <strong>Breast</strong> <strong>Cancer</strong> . . . . . . . . . . . . . . . . . . . . . . . . 61<br />
Nicholas Wang and Joe Gray<br />
vii
viii<br />
Contents<br />
6. Gene Expression Pr<strong>of</strong>iling as an Emerging Diagnostic Tool<br />
to Personalize Chemotherapy Selection for Early Stage<br />
<strong>Breast</strong> <strong>Cancer</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77<br />
Cornelia Liedtke and Lajos Pusztai<br />
7. Gene Expression–Based Predictors <strong>of</strong> Prognosis and Response<br />
to Chemotherapy in <strong>Breast</strong> <strong>Cancer</strong> . . . . . . . . . . . . . . . . . . . . . 97<br />
Soonmyung Paik<br />
8. Utilization <strong>of</strong> Genomic Signatures for Personalized<br />
Treatment <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> . . . . . . . . . . . . . . . . . . . . . . . . . . 107<br />
P. Kelly Marcom, Carey K. Anders, Ge<strong>of</strong>frey S. Ginsburg,<br />
Joseph R. Nevins, and Anil Potti<br />
9. Personalized Medicine by the Use <strong>of</strong> Microarray Gene<br />
Expression Pr<strong>of</strong>iling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123<br />
Stella Mook, Laura J. van ’t Veer, and Fatima Cardoso<br />
10. Predictive Markers for Targeted <strong>Breast</strong> <strong>Cancer</strong> Treatment . . . 135<br />
Hans Christian B. Pedersen and John M. S. Bartlett<br />
11. Circulating Tumor Cells in Individualizing <strong>Breast</strong> <strong>Cancer</strong><br />
Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151<br />
James M. Reuben and Massimo Crist<strong>of</strong>anilli<br />
12. Individualization <strong>of</strong> Endocrine Therapy in <strong>Breast</strong> <strong>Cancer</strong> . . . . 167<br />
Amelia B. Zelnak, Ruth M. O’Regan, and Clodia Osipo<br />
13. TAILORx: Rationale for the Study Design . . . . . . . . . . . . . . . 185<br />
Joseph A. Sparano<br />
14. Taking Prognostic Signatures to the Clinic . . . . . . . . . . . . . . . 197<br />
Michail Ignatiadis and Christos Sotiriou<br />
15. Tissue is the Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213<br />
Adekunle Raji<br />
16. Acquisition and Preservation <strong>of</strong> Tissue for Microarray<br />
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231<br />
Brian R. Untch and John A. Olson
Contents<br />
ix<br />
17. Tapping into the Formalin-Fixed Paraffin-Embedded (FFPE)<br />
Tissue Gold Mine for Individualization <strong>of</strong> <strong>Breast</strong><br />
<strong>Cancer</strong> Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243<br />
Mark Abramovitz and Brian Leyland-Jones<br />
18. <strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in<br />
Lethal Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259<br />
Danuta Balicki, Brian Leyland-Jones, and Max S. Wicha<br />
19. Molecular Imaging in Individualized <strong>Cancer</strong> Management . . . 291<br />
David M. Schuster and Diego R. Martin<br />
20. <strong>Cancer</strong> Nanotechnology for Molecular Pr<strong>of</strong>iling and<br />
Individualized Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309<br />
May Dongmei Wang, Jonathan W. Simons, and Shuming Nie<br />
Index . . . . . . . 323
Contributors<br />
Mark Abramovitz<br />
VM Institute <strong>of</strong> Research, Montreal, Québec, Canada<br />
Christine B. Ambrosone Department <strong>of</strong> <strong>Cancer</strong> Prevention and Control,<br />
Roswell Park <strong>Cancer</strong> Institute, Buffalo, New York, U.S.A.<br />
Kandace L. Amend Department <strong>of</strong> Oncological Sciences, Mount Sinai School<br />
<strong>of</strong> Medicine, New York, New York, U.S.A.<br />
Carey K. Anders Duke Institute for Genome Sciences & Policy, Department<br />
<strong>of</strong> Medicine, Duke University Medical Center, Durham, North Carolina, U.S.A.<br />
Danuta Balicki Department <strong>of</strong> Medicine and Research Centre, Centre<br />
Hospitalier de l’Université de Montréal; Department <strong>of</strong> Medicine, Université de<br />
Montréal; and <strong>Breast</strong> <strong>Cancer</strong> Prevention and Genetics and <strong>Breast</strong> <strong>Cancer</strong> Risk<br />
Assessment Clinic, Ville Marie Multidisciplinary <strong>Breast</strong> <strong>Cancer</strong> Centre,<br />
Montréal, Québec, Canada<br />
John M. S. Bartlett Endocrine <strong>Cancer</strong> Group, <strong>Cancer</strong> Research Centre,<br />
Western General Hospital, Edinburgh, United Kingdom<br />
Fatima Cardoso<br />
Brussels, Belgium<br />
Department <strong>of</strong> Medical Oncology, Institute Jules Bordet,<br />
Jenny C. Chang <strong>Breast</strong> Center, Dan L. Duncan <strong>Cancer</strong> Center, Baylor College<br />
<strong>of</strong> Medicine, Houston, Texas, U.S.A.<br />
Ji-Yeob Choi Department <strong>of</strong> <strong>Cancer</strong> Prevention and Control, Roswell Park<br />
<strong>Cancer</strong> Institute, Buffalo, New York, U.S.A.<br />
xi
xii<br />
Contributors<br />
Massimo Crist<strong>of</strong>anilli Department <strong>of</strong> <strong>Breast</strong> Medical Oncology, The<br />
University <strong>of</strong> Texas M. D. Anderson <strong>Cancer</strong> Center, Houston, Texas, U.S.A.<br />
Shaheenah Dawood Department <strong>of</strong> Medical Oncology, Dubai Hospital,<br />
Dubai, United Arab Emirates<br />
Amy M. Dworkin Human <strong>Cancer</strong> Genetics Program, Department <strong>of</strong><br />
Molecular Virology, Immunology, and Medical Genetics, The Ohio State<br />
University, Columbus, Ohio, U.S.A.<br />
Suzanne A. W. Fuqua <strong>Breast</strong> Center, Dan L. Duncan <strong>Cancer</strong> Center, Baylor<br />
College <strong>of</strong> Medicine, Houston, Texas, U.S.A.<br />
Ge<strong>of</strong>frey S. Ginsburg Duke Institute for Genome Sciences & Policy,<br />
Department <strong>of</strong> Medicine, Duke University Medical Center, Durham,<br />
North Carolina, U.S.A.<br />
Joe Gray Life Sciences Division, Lawrence Berkeley National Laboratory,<br />
Berkeley, California, U.S.A.<br />
Susan G. Hilsenbeck <strong>Breast</strong> Center, Dan L. Duncan <strong>Cancer</strong> Center, Baylor<br />
College <strong>of</strong> Medicine, Houston, Texas, U.S.A.<br />
Tim H.-M. Huang Human <strong>Cancer</strong> Genetics Program, Department <strong>of</strong><br />
Molecular Virology, Immunology, and Medical Genetics, The Ohio State<br />
University, Columbus, Ohio, U.S.A.<br />
Michail Ignatiadis Department <strong>of</strong> Medical Oncology, Translational Research<br />
Unit, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium<br />
Brian Leyland-Jones Winship <strong>Cancer</strong> Institute, Emory University School <strong>of</strong><br />
Medicine, Atlanta, Georgia, U.S.A.<br />
Cornelia Liedtke Department <strong>of</strong> <strong>Breast</strong> Medical Oncology, The University <strong>of</strong><br />
Texas, Anderson <strong>Cancer</strong> Center, Houston, Texas, U.S.A.<br />
P. Kelly Marcom Duke Institute for Genome Sciences & Policy, Department<br />
<strong>of</strong> Medicine, Duke University Medical Center, Durham, North Carolina, U.S.A.<br />
Diego R. Martin<br />
Georgia, U.S.A.<br />
Department <strong>of</strong> Radiology, Emory University, Atlanta,
Contributors<br />
xiii<br />
Stella Mook Department <strong>of</strong> Pathology, The Netherlands <strong>Cancer</strong> Institute,<br />
Amsterdam, The Netherlands<br />
Joseph R. Nevins Duke Institute for Genome Sciences & Policy, Department<br />
<strong>of</strong> Molecular Genetics and Microbiology, Duke University Medical Center,<br />
Durham, North Carolina, U.S.A.<br />
Shuming Nie Departments <strong>of</strong> Biomedical Engineering and Chemistry and<br />
the Winship <strong>Cancer</strong> Institute, Emory University and Georgia Institute <strong>of</strong><br />
Technology, Atlanta, Georgia, U.S.A.<br />
Ruth M. O’Regan<br />
Emory Winship <strong>Cancer</strong> Institute, Atlanta, Georgia, U.S.A.<br />
John A. Olson Institute for Genome Sciences and Policy and the Department<br />
<strong>of</strong> Surgery, Duke University Medical Center, Durham, North Carolina, U.S.A.<br />
Clodia Osipo<br />
Loyola University, Maywood, Illinois, U.S.A.<br />
Soonmyung Paik Division <strong>of</strong> Pathology, NSABP Foundation, Pittsburgh,<br />
Pennsylvania, U.S.A.<br />
Hans Christian B. Pedersen Endocrine <strong>Cancer</strong> Group, <strong>Cancer</strong> Research<br />
Centre, Western General Hospital, Edinburgh, United Kingdom<br />
Anil Potti Duke Institute for Genome Sciences & Policy, Department <strong>of</strong><br />
Medicine, Duke University Medical Center, Durham, North Carolina, U.S.A.<br />
Lajos Pusztai Department <strong>of</strong> <strong>Breast</strong> Medical Oncology, The University <strong>of</strong><br />
Texas, Anderson <strong>Cancer</strong> Center, Houston, Texas, U.S.A.<br />
Adekunle Raji Eastern Cooperative Oncology Group Pathology Coordinating<br />
Office, Northwestern University, Evanston, Illinois, U.S.A.<br />
James M. Reuben Department <strong>of</strong> Hematopathology, The University <strong>of</strong> Texas<br />
M. D. Anderson <strong>Cancer</strong> Center, Houston, Texas, U.S.A.<br />
David M. Schuster Division <strong>of</strong> Nuclear Medicine and Molecular Imaging,<br />
Department <strong>of</strong> Radiology, Emory University, Atlanta, Georgia, U.S.A.<br />
Jonathan W. Simons<br />
California, U.S.A.<br />
The Prostate <strong>Cancer</strong> Foundation, Santa Monica,
xiv<br />
Contributors<br />
Christos Sotiriou Department <strong>of</strong> Medical Oncology, Translational Research<br />
Unit, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium<br />
Joseph A. Sparano Albert Einstein College <strong>of</strong> Medicine, Montefiore Medical<br />
Center, Bronx, New York, U.S.A.<br />
Amanda E. Toland Human <strong>Cancer</strong> Genetics Program, Department <strong>of</strong><br />
Molecular Virology, Immunology, and Medical Genetics, The Ohio State<br />
University, Columbus, Ohio, U.S.A.<br />
Brian R. Untch Institute for Genome Sciences and Policy and the Department<br />
<strong>of</strong> Surgery, Duke University Medical Center, Durham, North Carolina, U.S.A.<br />
Laura J. van ’t Veer Department <strong>of</strong> Pathology, The Netherlands <strong>Cancer</strong><br />
Institute, Amsterdam, The Netherlands<br />
May Dongmei Wang Departments <strong>of</strong> Biomedical Engineering and Electrical<br />
and Computer Engineering, Georgia Institute <strong>of</strong> Technology and Emory<br />
University, Atlanta, Georgia, U.S.A.<br />
Nicholas Wang Life Sciences Division, Lawrence Berkeley National<br />
Laboratory, Berkeley, California, U.S.A.<br />
Max S. Wicha Comprehensive <strong>Cancer</strong> Centre, Department <strong>of</strong> Internal<br />
Medicine, Division <strong>of</strong> Hematology-Oncology, University <strong>of</strong> Michigan, Ann<br />
Arbor, Michigan, U.S.A.<br />
Amelia B. Zelnak<br />
Emory Winship <strong>Cancer</strong> Institute, Atlanta, Georgia, U.S.A.
1<br />
<strong><strong>Ph</strong>armacogenetics</strong> <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong>:<br />
Toward the Individualization <strong>of</strong> Therapy<br />
Jenny C. Chang, Susan G. Hilsenbeck, and Suzanne A. W. Fuqua<br />
<strong>Breast</strong> Center, Dan L. Duncan <strong>Cancer</strong> Center,<br />
Baylor College <strong>of</strong> Medicine, Houston, Texas, U.S.A.<br />
INTRODUCTION<br />
Mortality from breast cancer results from the ability <strong>of</strong> some tumors to metastasize<br />
to distant sites. Selecting patients with micrometastases at diagnosis is<br />
crucial for clinicians in deciding who should, and who should not, receive toxic<br />
and expensive adjuvant chemotherapy to eradicate these metastatic cells.<br />
Although many individual biomarkers were originally attractive, over the years<br />
most have failed to become clinically useful. In addition, the management <strong>of</strong><br />
breast cancer has changed, with the majority <strong>of</strong> node-negative patients now<br />
undergoing systemic adjuvant therapy because we cannot precisely determine an<br />
individual’s risk <strong>of</strong> recurrence. A majority <strong>of</strong> node-negative patients are being<br />
unnecessarily overtreated because if left systemically untreated, only about 25%<br />
<strong>of</strong> node-negative patients would ever develop recurrence. There is therefore a<br />
critical need to identify patients with sufficiently low risk <strong>of</strong> breast cancer<br />
recurrence so as to avoid further treatment. In addition, in patients at risk <strong>of</strong><br />
recurrence and in need <strong>of</strong> therapy, optimal therapeutic selection is an increasingly<br />
important objective. Recent developments in applying microarray technologies<br />
to breast tumor samples suggest that these new techniques may provide<br />
for the transition <strong>of</strong> molecular biological discoveries to clinical application and<br />
1
2 Chang et al.<br />
will generate clinically useful genomic pr<strong>of</strong>iles that more accurately predict the<br />
long-term outcome <strong>of</strong> individual breast cancer patients.<br />
BACKGROUND<br />
Until recently, evaluations <strong>of</strong> prognostic and predictive factors have considered<br />
one factor at a time or have used small panels <strong>of</strong> markers. However, with the<br />
advent <strong>of</strong> new genomic technologies such as microarrays, capable <strong>of</strong> simultaneously<br />
measuring thousands <strong>of</strong> genes or gene products, we are beginning to<br />
construct molecular fingerprints <strong>of</strong> individual tumors so that accurate prognostic<br />
and predictive assessments <strong>of</strong> each cancer may be made. Clinicians may one day<br />
base clinical management on each woman’s personal prognosis and predict the<br />
best individual therapies according to the genetic fingerprint <strong>of</strong> each individual<br />
cancer.<br />
<strong>Breast</strong> cancer is characterized by a very heterogeneous clinical course. A<br />
major goal <strong>of</strong> recent studies is to determine whether RNA microarray expression<br />
pr<strong>of</strong>iling or DNA array gene amplification or gene loss patterns can accurately<br />
predict an individual’s long-term potential for recurrence <strong>of</strong> breast cancer, so that<br />
appropriate treatment decisions can be made. Microarrays can be used to measure<br />
the mRNA expression <strong>of</strong> thousands <strong>of</strong> genes at one time or survey genomic<br />
alterations that may distinguish molecular phenotypes associated with long-term,<br />
recurrence-free survival or clinical response to treatment. These new technologies<br />
have been successfully applied to primary breast cancers and may<br />
eventually outperform currently used clinical parameters in predicting disease<br />
outcome.<br />
Since RNA expression microarray technology provided a method for<br />
monitoring the RNA expression <strong>of</strong> many thousands <strong>of</strong> human genes at a time,<br />
there was considerable anticipation that it would quickly and easily revolutionize<br />
our approaches to cancer diagnosis, prognosis, and treatment. The reality<br />
remains extremely promising but is also complex. A potential complication in<br />
the application <strong>of</strong> microarray technology to primary human breast tumor samples<br />
is the presence <strong>of</strong> variable numbers <strong>of</strong> normal cells, such as stroma, blood<br />
vessels, and lymphocytes, in the tumor. Indeed, it has been demonstrated using<br />
gross analysis <strong>of</strong> human breast cancer specimens compared with breast cancer<br />
cell lines that the tumors expressed sets <strong>of</strong> genes in common not only with these<br />
cell lines but also with cells <strong>of</strong> hematopoietic lineage and stromal origin (1,2).<br />
Laser capture microdissection has also been successfully used to isolate pure-cell<br />
populations from primary breast cancers for array pr<strong>of</strong>iling (3). Sgroi et al. (3)<br />
utilized laser capture microdissection to isolate morphologically “normal” breast<br />
epithelial cells, invasive breast cancer cells, and metastatic lymph node cancer<br />
cells from one patient and were able to demonstrate the feasibility <strong>of</strong> using<br />
microdissected samples for array pr<strong>of</strong>iling as well as following potential progression<br />
<strong>of</strong> cancer in this patient. However, with the emerging data supporting<br />
important roles for the surrounding stroma in breast cancer progression and the
<strong><strong>Ph</strong>armacogenetics</strong> <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> 3<br />
labor-intensive and technically challenging nature <strong>of</strong> laser capture technology<br />
with subsequent amplification <strong>of</strong> RNA for quantitation, most published investigations<br />
to date have evaluated total gene expression to identify prognostic<br />
pr<strong>of</strong>iles, as will be described in the next section.<br />
MOLECULAR CLASSIFICATION OF BREAST CANCER<br />
A study <strong>of</strong> sporadic breast tumor samples by Perou et al. (2) was the first to show<br />
that breast tumors could be classified into subtypes distinguished by differences<br />
in their expression pr<strong>of</strong>iles. Using 40 breast tumors and 20 matched pairs<br />
<strong>of</strong> samples before and after doxorubicin treatment, an “intrinsic gene set” <strong>of</strong><br />
476 genes was selected that was more variably expressed between the 40 sporadic<br />
tumors than between the paired samples. This intrinsic gene set was then used to<br />
cluster and segregate the tumors into four major subgroups: a “luminal cell-like”<br />
group expressing the estrogen receptor (ER); a “basal cell-like” group expressing<br />
keratins 5 and 17, integrinb4, and laminin, but lacking ER expression; an<br />
“Erb-B2-positive” group; and a “normal” epithelial group (Fig. 1).<br />
In a subsequent study with 38 additional cancers, the investigators found<br />
the same subgroups as before (4), except that the luminal, ER-positive group was<br />
further subdivided into subsets with distinctive gene expression pr<strong>of</strong>iles. In<br />
univariate survival analysis, performed on the 49 patients diagnosed with locally<br />
advanced disease but without evidence <strong>of</strong> distant metastasis, ER positivity was<br />
not a significant prognostic factor on its own, but the luminal-type group enjoyed<br />
a more favorable survival compared with the other groups. Conversely, the<br />
basal-like group had a significantly poorer prognosis. Although small and<br />
exploratory, this study suggests that important differences in outcome can be<br />
ascertained from microarray expression pr<strong>of</strong>iling.<br />
An interesting study was reported by Gruvberger et al. (5), who pr<strong>of</strong>iled 58<br />
grossly dissected primary invasive breast tumors and used artificial neural network<br />
analysis to predict the ER status <strong>of</strong> the tumors on the basis <strong>of</strong> their gene expression<br />
patterns. They then determined which specific genes were the most important for ER<br />
classification. By comparing to Serial Analysis <strong>of</strong> Gene Expression (SAGE) data<br />
from estradiol-stimulated breast cancer cells, they determined that only a few genes<br />
<strong>of</strong> the many genes that were associated with ER expression in tumors were indeed<br />
estrogen responsive in cell culture. This observation lent further support to the<br />
hypothesis developed by Perou et al. (1) that basic cell lineages, such as the luminal<br />
ER-positive cell type, can be partly explained by observed genomic gene expression<br />
patterns rather than downstream effectors <strong>of</strong> only one pathway, such as the ER.<br />
PROGNOSTIC IMPLICATIONS<br />
Microarrays have also been used to predict lymph node status and very shortterm<br />
relapse-free survival in two groups (n ¼ 37 and 52, respectively) <strong>of</strong> heterogeneously<br />
treated patients (6). Although prediction <strong>of</strong> nodal status is <strong>of</strong>
4 Chang et al.<br />
Figure 1 Supervised classification <strong>of</strong> prognosis signatures (van ’t Veer). The 78 tumors<br />
are listed vertically, and the 70 “prognostic” genes horizontally. (A) Study design. (B)Heatmap<br />
<strong>of</strong> prognostic signature.<br />
limited interest clinically, the study uses innovative statistical methods, rigorously<br />
generates estimates <strong>of</strong> future classifier performance, and further demonstrates<br />
the feasibility <strong>of</strong> accurate prediction <strong>of</strong> tumor biology using expression<br />
arrays. In a more focused and somewhat more clinically relevant study, van ’t Veer<br />
et al. (7) used RNA expression microarray analyses to identify a 70-gene<br />
prognostic gene signature (“classifier”) in young, untreated, axillary lymph<br />
node–negative patients using a training set <strong>of</strong> 44 good (disease free for more than<br />
5 years) and 34 poor (distant relapse in less than 5 years) outcome tumors and<br />
then tested the classifier in a validation set <strong>of</strong> 19 tumors. The same group (8) has<br />
now extended the study to a total <strong>of</strong> 295 young (
<strong><strong>Ph</strong>armacogenetics</strong> <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> 5<br />
70-gene classifier (7). The microarray-based predictions are consistent with, and<br />
perhaps better than, estimates that can be obtained with current prognostic<br />
indices.<br />
GENETIC SUSCEPTIBILITY<br />
A few studies have utilized new genomic approaches for the study <strong>of</strong> inherited<br />
breast cancer (8). There is accumulating evidence, both epidemiological and<br />
histological, that tumors arising as a result <strong>of</strong> mutations in the two breast cancer<br />
susceptibility gene families (BRCA1 and BRCA2) are biologically distinct. For<br />
instance, BRCA1 breast cancers are most <strong>of</strong>ten ER and PR negative, but BRCA2<br />
cancers more <strong>of</strong>ten tend to be positive for these receptors (9). In a seminal paper<br />
published by Hedenfalk et al. (10), seven tumors each from BRCA1 and BRCA2<br />
gene mutation carriers or sporadic breast cancers were compared by expression<br />
microarray analysis. They found that the gene expression pr<strong>of</strong>iles <strong>of</strong> the three<br />
tumor groups differed significantly from each other, underscoring the fundamental<br />
differences between BRCA1 and BRCA2 mutation-associated tumors. Of<br />
course a potential confounding issue was the differential distribution <strong>of</strong> ER<br />
between the BRCA1 and BRCA2 tumors. However, even after removal <strong>of</strong> ERor<br />
PR-associated genes from the analysis, the two inherited tumor groups were<br />
still discernable. Thus, ER status alone does not fully explain the observed<br />
differences in gene expression pr<strong>of</strong>iles. Although this study is obviously very<br />
small, and other confounding issues such as tumor stage, grade, and treatment<br />
cannot be considered, it does set a foundation for larger validation studies to<br />
confirm differential genes, which could then provide important clues to the<br />
etiology <strong>of</strong> inheritable breast cancer.<br />
Microarrays are also being studied as a way to predict response to systemic<br />
therapy. The neoadjuvant setting is especially attractive for these studies for<br />
several reasons, including early assessment <strong>of</strong> response to therapy, biopsiable<br />
access to the primary tumor, and considerable reduced sample sizes compared to<br />
those required in the adjuvant setting.<br />
PREDICTIVE IMPLICATIONS<br />
Methods for assessing response in neoadjuvant trials remain problematic. Clinical<br />
response to neoadjuvant chemotherapy is a validated surrogate marker for<br />
improved survival (11,12). Women who achieve pathologic complete response<br />
are most likely to have the best clinical outcome, although survival is still<br />
improved in those who clinically respond but do not achieve pathologic complete<br />
response.<br />
In an early study, Buchholz et al. (13) obtained sufficient RNA from core<br />
biopsies <strong>of</strong> five patients to perform serial microarray expression pr<strong>of</strong>iles and<br />
showed that despite differences in therapy, patients with good pathological
6 Chang et al.<br />
responses to neoadjuvant treatment appeared to have gene pr<strong>of</strong>iles that clustered<br />
distinctly than those <strong>of</strong> patients who were poor responders to treatment. More<br />
recently, Chang et al. (14) have shown that gene pr<strong>of</strong>iling can be used to<br />
accurately predict response to neoadjuvant docetaxel. The study enrolled<br />
24 subjects, extracted sufficient RNA from all core needle biopsies, and constructed<br />
a 92-gene predictor <strong>of</strong> response. In a complete cross-validation analysis, which<br />
gives an unbiased estimate <strong>of</strong> performance on future samples, the classifier<br />
correctly identified 10 <strong>of</strong> 11 responders and 11 <strong>of</strong> 13 nonresponders with an<br />
overall accuracy <strong>of</strong> 88%. In a small validation set, this 92-gene classifier successfully<br />
predicted response in six patients (Fig. 2). This compares very favorably<br />
with the best existing predictive factors for response to specific therapy and<br />
strongly suggests that after appropriately extensive validation, microarray<br />
pr<strong>of</strong>iling will be useful for treatment selection. A second neoadjuvant study was<br />
recently published that used cDNA arrays to develop predictors for paclitaxel,<br />
fluorouracil, doxorubicin, and cyclophosphamide, involving 24 samples. A<br />
classifier with 74 markers was developed, with 78% accuracy, suggesting that<br />
transcriptional pr<strong>of</strong>iling has the potential to identify a gene expression pattern in<br />
breast cancer that may lead to clinically useful predictors <strong>of</strong> chemotherapy<br />
response (15).<br />
In vitro drug sensitivity data in NCI60 cell lines sensitive and resistant to<br />
specific chemotherapeutic agents (adriamycin, cyclophosphamide, docetaxel,<br />
etoposide, 5-fluoruracil, paclitaxel, topotecan) were recently published (16).<br />
Gene set enrichment analysis was performed, and gene expression signatures<br />
predictive <strong>of</strong> sensitivity to individual chemotherapeutic drugs were reported.<br />
Each signature was validated with response data from an independent set <strong>of</strong> cell<br />
line studies. These signatures were then used to predict clinical response in<br />
individuals treated with these drugs. Notably, signatures developed to predict<br />
response to individual agents, when combined, were found to also predict<br />
response to multidrug regimens (16). These pr<strong>of</strong>iles are being validated in<br />
prospective clinical trials.<br />
However, we acknowledge that studies to construct and validate arraybased<br />
prognostic and predictive “markers” are complex. These studies must<br />
address all <strong>of</strong> the concerns associated with ordinary, single-gene markers as well<br />
as a number <strong>of</strong> considerations unique to array studies. Recommendations for<br />
development <strong>of</strong> array-based prognostic classifiers have recently been enunciated<br />
by Simon et al. (17). Among the most important points, they recommend that<br />
studies should include (1) adequately large sample sizes in both training and<br />
validation sets, (2) complete iteration <strong>of</strong> the entire classifier construction process<br />
in estimating cross-validated prediction rates, (3) head-to-head comparison <strong>of</strong><br />
alternative classifiers on the same dataset, and (4) the full diversity <strong>of</strong> cases in<br />
any validation set. In addition, gene expression patterns may be confounded by<br />
several other factors, including ovarian ablation in premenopausal, ER-positive<br />
patients and a different mechanism <strong>of</strong> action <strong>of</strong> combination chemotherapies.
<strong><strong>Ph</strong>armacogenetics</strong> <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> 7<br />
Figure 2 Hierarchical clustering <strong>of</strong> genes correlated with docetaxel response. Sensitive<br />
tumors (S) are defined as 25% residual disease, and resistant tumors (R) are defined as<br />
greater than 25% residual disease.<br />
Groups <strong>of</strong> patients with different characteristics, such as menopausal and ER<br />
status or human epidermal growth factor receptor 2 (HER2) overexpression, may<br />
be necessary to definitively determine classifying patterns in these subsets <strong>of</strong><br />
patients.
8 Chang et al.<br />
CONCLUSIONS<br />
The goal <strong>of</strong> comprehensive, genomewide approaches is to identify clinically<br />
useful genetic pr<strong>of</strong>iles that will accurately predict the outcome to therapy and<br />
prognosis <strong>of</strong> patients with breast cancer. Despite improvements in technology,<br />
complex mechanisms driving breast cancer evolution continue to present challenges<br />
for the use <strong>of</strong> genomic approaches to better understand breast and other<br />
cancers. This, combined with our use <strong>of</strong> different markers, methods, tumors (e.g.,<br />
differing ER and HER2), and measurements <strong>of</strong> clinical outcomes, impedes the<br />
development <strong>of</strong> a consensus regarding predictive and prognostic markers for<br />
breast cancer.<br />
As this field matures, genomic studies examining identical breast tumor<br />
sets with multiple complementary technologies (e.g., loss <strong>of</strong> heterozygosity,<br />
comparative genomic hybridization, gene expression array analysis) will prove<br />
essential to unraveling the genetic heterogeneity characteristic <strong>of</strong> this disease. A<br />
combined genomic approach is necessary to define the underlying heterogeneous<br />
complexity characteristic <strong>of</strong> breast cancer. These data should lead to the identification<br />
and characterization <strong>of</strong> breast cancer subtypes, define the malignant<br />
potential <strong>of</strong> a given lesion, and predict its sensitivity to specific therapies. These<br />
multidisciplinary approaches should contribute to a better biological understanding,<br />
and therefore improved clinical management, <strong>of</strong> breast cancer.<br />
ACKNOWLEDGMENTS<br />
This work was supported by funding provided by National Institutes <strong>of</strong> Health<br />
(NIH) grants P50 CA58183 and P01 CA30195, the <strong>Breast</strong> <strong>Cancer</strong> Research<br />
Foundation, the Emma Jacobs Clinical <strong>Breast</strong> <strong>Cancer</strong> Fund, and the U.S. Army<br />
Medical Research and Materiel Command DAMD17-01-0132 and W81XWH-<br />
04-1-0468.<br />
REFERENCES<br />
1. Perou CM, Jeffrey SS, van de Run M, et al. Distinctive gene expression patterns in<br />
human mammary epithelial cells and breast cancers. Proc Natl Acad Sci USA 1999;<br />
96:9212–9217.<br />
2. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits <strong>of</strong> human breast tumours.<br />
Nature 2000; 406:747–752.<br />
3. Sgroi DC, Teng S, Robinson G, et al. In vivo gene expression pr<strong>of</strong>ile analysis <strong>of</strong><br />
human breast cancer progression. <strong>Cancer</strong> Res 1999; 59:5656–5661.<br />
4. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns <strong>of</strong> breast carcinomas<br />
distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001;<br />
98:10869–10874.<br />
5. Gruvberger S, Ringner M, Chen Y, et al. Estrogen receptor status in breast cancer is<br />
associated with remarkably distinct gene expression patterns. <strong>Cancer</strong> Res 2001; 61:<br />
5979–5984.
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6. Huang E, Cheng SH, Dressman H, et al. Gene expression predictors <strong>of</strong> breast cancer<br />
outcomes. Lancet 2003; 361:1590–1596.<br />
7. van ’t Veer LJ, Dai H, van De Vijver MJ, et al. Gene expression pr<strong>of</strong>iling predicts<br />
clinical outcome <strong>of</strong> breast cancer. Nature 2002; 415:530–536.<br />
8. van de Vijver MJ, He YD, van ’t Veer LJ, et al. A gene-expression signature as a<br />
predictor <strong>of</strong> survival in breast cancer. N Engl J Med 2002; 347:1999–2009.<br />
9. Verhoog LG, Brekelmans CTM, Seynaeve C, et al. Survival in hereditary breast cancer<br />
associated with germline mutations <strong>of</strong> BRCA2. J Clin Oncol 1999; 17:3396–3402.<br />
10. Hedenfalk I, Duggan D, Chen Y, et al. Gene-expression pr<strong>of</strong>iles in hereditary breast<br />
cancer. N Engl J Med 2001; 344:539–548.<br />
11. Fisher B, Bryant J, Wolmark N, et al. Effect <strong>of</strong> preoperative chemotherapy on the<br />
outcome <strong>of</strong> women with operable breast cancer. J Clin Oncol 1998; 16:2672–2685.<br />
12. Chang J, Powles TJ, Allred DC, et al. Biologic markers as predictors <strong>of</strong> clinical<br />
outcome from systemic therapy for primary operable breast cancer. J Clin Oncol<br />
1999; 17:3058–3063.<br />
13. Buchholz TA, Stivers DN, Stec J, et al. Global gene expression changes during<br />
neoadjuvant chemotherapy for human breast cancer. <strong>Cancer</strong> J 2002; 8:461–468.<br />
14. Chang JC, Wooten EC, Tsimelzon A, et al. Gene expression pr<strong>of</strong>iling predicts<br />
therapeutic response to docetaxel (Taxotere TM ) in breast cancer patients. Lancet<br />
2003; 362:280–287.<br />
15. Ayers M, Symmans WF, Stec J, et al. Gene expression pr<strong>of</strong>iles predict complete<br />
pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and<br />
cyclophosphamide chemotherapy in breast cancer. J Clin Oncol 2004; 22:2284–2293.<br />
16. Potti A, Dressman HK, Bild A, et al. Genomic signatures to guide the use <strong>of</strong> chemotherapeutics.<br />
Nat Med 2006; 12:1294–1300.<br />
17. Simon R, Radmacher MD, Dobbin K, et al. J Natl <strong>Cancer</strong> Inst 2003; 95:14–18.
2<br />
<strong>Ph</strong>armacology, <strong><strong>Ph</strong>armacogenetics</strong>,<br />
and <strong>Ph</strong>armacoepidemiology: Three<br />
P’s <strong>of</strong> Individualized Therapy<br />
Shaheenah Dawood<br />
Department <strong>of</strong> Medical Oncology, Dubai Hospital,<br />
Dubai, United Arab Emirates<br />
If it were not for the great variability among individuals medicine might as<br />
well be a science and not an art.<br />
Sir William Osler, 1892<br />
INTRODUCTION<br />
Over the last decade significant advances have been made in the management <strong>of</strong><br />
breast cancer. This is exemplified by the development <strong>of</strong> several new agents and<br />
significantly improved survival rates. However, despite our best efforts breast<br />
cancer remains the second most common cause <strong>of</strong> cancer-related death in the<br />
United States with approximately 40,910 women expected to die <strong>of</strong> this disease<br />
in 2007 (1). Unfortunately, such trends are not restricted to only breast cancer but<br />
have been observed across a range <strong>of</strong> solid tumors. A large portion <strong>of</strong> the<br />
underlying problem is attributed to the fact that the choice <strong>of</strong> a chemotherapeutic<br />
agent is <strong>of</strong>ten empirical based on guidelines that assumes all patients with a<br />
particular malignancy to be homogeneous. The unfortunate outcome <strong>of</strong> this<br />
11
12 Dawood<br />
erroneous belief is that approximately 40% <strong>of</strong> patients may be receiving the<br />
wrong drug (2).<br />
Numerous studies have consistently observed that the efficacy and toxicity<br />
<strong>of</strong> chemotherapeutic agents are heterogeneous rather than homogeneous across<br />
populations. Despite a multitiered approach to determining the efficacy and safety<br />
pr<strong>of</strong>iles <strong>of</strong> chemotherapeutic agents, spanning from in vitro assays, animal studies<br />
through to large human population studies, the current practice <strong>of</strong> administering<br />
the same dose <strong>of</strong> a given anticancer drug to a population <strong>of</strong> patients results in<br />
considerable variation in clinical outcome. These effects cover a spectrum <strong>of</strong><br />
possibilities ranging from success to failure when considering treatment outcome<br />
as an endpoint, and from no effect to a lethal event when treatment-associated<br />
toxicity is the variable under study (3). Complicating the problem further is the<br />
fact that currently available anticancer drugs show limited efficacy in up to 70% <strong>of</strong><br />
patients with adverse drug reactions accounting for as many as 100,000 patient<br />
deaths and two million hospitalizations in the United States every year (4).<br />
The key is to be able to, at the individual level, prospectively identify<br />
patients at risk for severe toxicity and those likely to attain maximum benefit<br />
from a given chemotherapeutic agent. Understanding that an individual does not<br />
live in isolation but is in fact a product <strong>of</strong> the gene-environment interaction is<br />
also vital to our understanding <strong>of</strong> the concept <strong>of</strong> individualized therapy (Fig. 1).<br />
Figure 1<br />
Sources <strong>of</strong> pharmacologic and pharmacogenetic variability.
<strong>Ph</strong>armacology, <strong><strong>Ph</strong>armacogenetics</strong>, and <strong>Ph</strong>armacoepidemiology 13<br />
Designing treatment strategies based on individual efficacy and toxicity predictions<br />
will shift clinical practice from simply an “art” to a “science” by<br />
incorporating a combination <strong>of</strong> physiological variables, genetic characteristics,<br />
and environmental factors (all known to alter the dose-concentration time pr<strong>of</strong>ile)<br />
as well as population epidemiological elements resulting in a practice<br />
paradigm <strong>of</strong> “personalized medicine.”<br />
THE COMPONENTS OF INDIVIDUALIZED THERAPY: THE THREE P’s<br />
When we administer a drug to a patient, our goal is to deliver a dose that will<br />
achieve maximum benefit with little, if any, associated toxicity. Seldom is this<br />
goal achieved. Individual differences in tumor response and normal tissue toxicity<br />
have been consistently observed with most anticancer agents. These differences<br />
are due to a variety <strong>of</strong> clinical variables (e.g., gender, age, diet, and<br />
drug-drug interactions) as well as variations in drug metabolism and transport.<br />
It is believed that 30% <strong>of</strong> patients treated with a particular drug will exhibit<br />
beneficial effects from the drug, 30% will not attain any form <strong>of</strong> beneficial<br />
effect, 10% will experience only side effects from the drug, and 30% are noncompliant<br />
with the drug <strong>of</strong>ten as a result <strong>of</strong> drug-related side effects or perceived<br />
insufficient effect (5). This results in a waste <strong>of</strong> allocated resources with large<br />
proportions <strong>of</strong> patients being unnecessarily exposed to adverse side effects. Here<br />
we will describe three components that aim to explain the observed differences<br />
in responses to individual drugs: pharmacology, pharmacoepidemiology, and<br />
pharmacogenetics.<br />
Current dosing <strong>of</strong> anticancer drugs is based on either a fixed quantity <strong>of</strong><br />
the drug or on a dose that is normalized to the individual body surface area.<br />
This method makes the assumption that within a group <strong>of</strong> individuals there<br />
will be a uniform degree <strong>of</strong> systemic exposure to the drug. With studies<br />
reporting 2- to 10-fold variations in drug clearance, this assumption is clearly<br />
not valid (6). With drugs used in the field <strong>of</strong> oncology having narrow therapeutic<br />
indices, it thus becomes imperative that we understand the mechanisms<br />
behind the observed variability in drug response and toxicity when treating a<br />
patient with cancer. At the clinical level, variability in drug response can be<br />
explained by its pharmacology that describes the pharmacokinetic and pharmacodynamic<br />
pr<strong>of</strong>iles <strong>of</strong> the drug. <strong>Ph</strong>armacokinetics explain “what the body<br />
does to the drug” by describing the relationship between time and plasma<br />
concentrations <strong>of</strong> the drug metabolites. Thisrelationshipisaffectedbyvariables<br />
such as absorption, distribution, metabolism, and excretion <strong>of</strong> the drug.<br />
The underlying objective <strong>of</strong> pharmacodynamics is to describe “what the drug<br />
does to the body” by correlating drug concentration to drug effect (both beneficial<br />
and adverse effects). Both the pharmacokinetic and pharmacodynamic<br />
components <strong>of</strong> a drug are not independent, but represent a spectrum <strong>of</strong> continuous<br />
events starting with the ingestion <strong>of</strong> the drug and culminating in the<br />
observed clinical effect.
14 Dawood<br />
Variability in any <strong>of</strong> the pharmacokinetic components will affect the<br />
observed drug effect. This is typically observed in patients with organ dysfunction<br />
where the inability to either metabolize or excrete the drug will lead to<br />
exaggerated drug effects. For example, the use <strong>of</strong> carboplatin in patients with<br />
renal dysfunction exaggerates the thrombocytopenic effect <strong>of</strong> this drug. Dosing<br />
formulas using creatinine clearance are used to avoid this problem while maximizing<br />
dose intensity (7). Another example is the use <strong>of</strong> taxanes, which are<br />
primarily metabolized and excreted by the liver. When administered to patients<br />
with hepatic dysfunction, prolonged bouts <strong>of</strong> severe neutropenia are observed.<br />
These examples illustrate how clinical variables can affect the pharmacokinetic<br />
pr<strong>of</strong>ile <strong>of</strong> a drug leading to disturbances in effect. Good clinical assessment <strong>of</strong><br />
the patients is therefore the first and most important step in designing an individual<br />
treatment regimen that will maximize drug benefit.<br />
<strong>Ph</strong>armacoepidemiology is a sub discipline <strong>of</strong> epidemiology that focuses on<br />
understanding why individuals respond differently to drug therapy. It is defined<br />
“as the study <strong>of</strong> the distribution and determinants <strong>of</strong> drug-related events in<br />
populations and the application <strong>of</strong> this study to efficacious drug treatments” (8).<br />
These drug-related events pertain to both beneficial and adverse events. <strong>Ph</strong>armacoepidemology,<br />
through the use <strong>of</strong> observational methods, aims to quantify<br />
drug exposure and to describe, as well as predict, the effectiveness and safety <strong>of</strong><br />
the drug within defined populations at specific places and time. Factors<br />
important in interpreting the variability in drug outcome include a patient’s<br />
health pr<strong>of</strong>ile, sex, age, diet, associated comorbidities, disease severity and<br />
prognosis, drug compliance, drug prescribing and dispensing quality, and genetic<br />
pr<strong>of</strong>ile <strong>of</strong> the patient and tumor (9,10). Depending on drug effect there are four<br />
main groups <strong>of</strong> patients: (i) responders, (ii) non-responders, (iii) toxic responders,<br />
and (iv) toxic non-responders (Fig. 2).<br />
<strong><strong>Ph</strong>armacogenetics</strong>, a third and important component <strong>of</strong> individualized<br />
therapy, focuses on describing the extent to which an individual’s genetic<br />
makeup is responsible for the observed differences in drug efficacy and toxicity<br />
pr<strong>of</strong>iles (11,12). This information is then used to make predictions about the<br />
safety, toxicity, and efficacy <strong>of</strong> drugs in individual patients. Inherited variability<br />
<strong>of</strong> drug targets, drug metabolizing enzymes, and drug transporters may all have a<br />
major impact on overall drug response, disposition, and associated adverse side<br />
effects by altering the pharmacokinetic and pharmacodynamic properties <strong>of</strong><br />
drugs (Table 1).<br />
Genetic variations include nucleotide repeats, insertions, deletions, and<br />
single nucleotide polymorphisms (SNPs). SNPs are the simplest and most<br />
commonly studied DNA polymorphism that occur once every 1900 base pairs in<br />
the 3 billion bases in the human genome (13) and account for more than 90% <strong>of</strong><br />
the genetic variation observed. More than 1.4 million SNPs have been identified<br />
in the human genome. Of these more than 60,000 SNPs occur in the coding<br />
region <strong>of</strong> genes with some SNPs being associated with variations in drug<br />
metabolism and effects (13). When polymorphisms occur in the promoter region
<strong>Ph</strong>armacology, <strong><strong>Ph</strong>armacogenetics</strong>, and <strong>Ph</strong>armacoepidemiology 15<br />
Figure 2 A group <strong>of</strong> individuals with the same diagnosis do not respond homogeneously<br />
to a single drug. Through the elements <strong>of</strong> phamacology, pharmacoepidemiology, and<br />
pharmacogenetics, the aim is to identify patients at risk for toxicity or reduced response to<br />
therapy prior to medication initiation.<br />
Table 1<br />
Examples <strong>of</strong> Genetic Variations and Their Associated <strong>Ph</strong>enotypic Changes<br />
Enzymes Substrate <strong>Ph</strong>enotype<br />
CYP3A4/3A5 D, P, E, T Interindividual variability in<br />
pharmacokinetics<br />
DPD 5-FU Increased toxicity<br />
P-glycoprotein Various anticancer agents Drug resistance<br />
TS 5-FU and antifolates Drug resistance<br />
TPMT 6-MP Increased hematological toxicity<br />
UGT1A1 Irinotecan Increased diarrhea and neutropenia<br />
Abbreviations: D, docetaxel, P, paclitaxel; E, etoposide, T, tamoxifen; 6-MP, mercaptopurine, TPMT,<br />
thiopurine methyltransferases, UGT1A1, UDP-glucuronosyltrasferase 1A1, DPD, dihydropyrimidine<br />
dehydrogenase.<br />
it can effect the transcription <strong>of</strong> the gene. When the polymorphism occurs in the<br />
exon (and intron) region or 3 0 UTR <strong>of</strong> the gene, it can affect translation and RNA<br />
stability, respectively, resulting in either reduced or enhanced activity <strong>of</strong> the<br />
encoded protein (3).<br />
Two approaches have been used to identify clinically relevant polymorphisms.<br />
In the reverse genetics approach, a drug is first given to a group<br />
individuals and the variability in response to and disposition <strong>of</strong> the drug is<br />
observed. <strong>Ph</strong>enotypes <strong>of</strong> clinical importance are prioritized and then experiments
16 Dawood<br />
are conducted to determine if a genetic component is present to explain the<br />
observed variability. Finally, the biochemical and molecular nature <strong>of</strong> the variability<br />
is determined through extensive resequencing techniques that will link the<br />
polymorphisms with the observed phenotypes. The second and more cost effective<br />
method is the forward genetics approach. This method involves first determining<br />
the presence <strong>of</strong> genetic variability at specific loci in a group <strong>of</strong> individuals. A<br />
hypothesis is then formulated regarding these polymorphisms causing phenotypic<br />
variability. Finally, an experiment is performed to investigate the clinical consequences<br />
<strong>of</strong> the genetic variability. Because <strong>of</strong> the multitude <strong>of</strong> polymorphisms,<br />
the challenge today is to be able to identify polymorphisms that occur in gene<br />
regulatory or coding regions that have clinical relevance. The field <strong>of</strong> oncology<br />
adds another level <strong>of</strong> complexity as issues such as clinically relevant tumor<br />
genome variations not found in the germline and somatic mutations acquired<br />
before or after chemotherapy can affect drug efficacy and toxicity. Moreover,<br />
different polymorphisms <strong>of</strong> certain genes may exhibit different phenotypes among<br />
different malignancies. Polymorphisms <strong>of</strong> glutathione S-transferases (GSTs) are a<br />
common example. GSTs facilitate the conjugation <strong>of</strong> glutathione to xenobiotics,<br />
including anticancer agents (e.g., cyclophosphamide and anthracyclines), environmental<br />
carcinogens, and reactive oxygen products (14). A variety <strong>of</strong> functionally<br />
important polymorphisms <strong>of</strong> GSTs have been described. The presence <strong>of</strong><br />
the GSTT1 polymorphism has been shown to predispose children treated intensively<br />
for acute myelogenous leukemia to greater toxicity and death during<br />
remission (15), while women with breast cancer treated with cyclophosphamide<br />
carrying the GSTP1 polymorphism are more likely to survive (16). This example<br />
illustrates how the phenotypic effect <strong>of</strong> polymorphisms depends on the underlying<br />
tumor as well as the intensity <strong>of</strong> regimen used to treat the malignancy. However,<br />
as we begin to unravel and accurately identify the mechanism <strong>of</strong> action <strong>of</strong> many<br />
anticancer drugs, polymorphisms in candidate genes likely to influence drug<br />
efficacy and associated adverse events can be identified. Indeed the field <strong>of</strong><br />
pharmacogenetics has transitioned from one that determined the genetic basis <strong>of</strong><br />
observed inter patient phenotypic variability to one that determines the phenotypic<br />
consequence <strong>of</strong> an observed genotypic variability.<br />
Linking the information obtained from the pharmacology and pharmacoepidemiology<br />
<strong>of</strong> a drug and the pharmacogenetics that describes the genetic<br />
makeup <strong>of</strong> the individual being treated with the drug is thus vitally important in<br />
developing an optimal approach to individualized therapy. The use <strong>of</strong> isoniazid<br />
in the treatment <strong>of</strong> tuberculosis is a good example. Epidemiological evidence<br />
shows that polymorphisms differ in frequency among ethnic and racial groups.<br />
This phenomenon was shown early on when interindividual and interethnic<br />
variations <strong>of</strong> efficacy and toxicity associated with isoniazid treatment was<br />
observed in a cohort <strong>of</strong> individuals with tuberculosis. The enzyme responsible<br />
for the metabolism <strong>of</strong> isoniazid is N-acetyltransferase, a phase II conjugating<br />
liver enzyme. Early studies <strong>of</strong> isoniazid revealed a bimodal distribution in its<br />
plasma concentration among individuals. High plasma concentrations were
<strong>Ph</strong>armacology, <strong><strong>Ph</strong>armacogenetics</strong>, and <strong>Ph</strong>armacoepidemiology 17<br />
observed among individuals with the slow acetylator phenotype <strong>of</strong>ten resulting<br />
in peripheral nerve damage, while low plasma concentrations <strong>of</strong> isoniazid were<br />
observed among individuals with fast acetylator phenotype and were not affected<br />
(17). Studies have shown large variations in the incidence <strong>of</strong> the slow acetylator<br />
phenotype (inherited as an autosomal recessive trait) among different ethnic<br />
groups (40–70% <strong>of</strong> Caucasians and African Americans, 10–20% <strong>of</strong> Japanese, and<br />
more than 80% <strong>of</strong> Egyptians) (18,19). Amonafide (20), a topoisomerase II<br />
inhibitor, is another substrate <strong>of</strong> N-acetyltransferase whose clinical development<br />
has been hampered by highly variable and unpredictable toxicity caused, at least,<br />
in part, by interindividual differences in N-acetylation with fast acetylators<br />
experiencing greater myelosuppression than slow acetylators. This example<br />
illustrates both the importance <strong>of</strong> pharmacoepidemiological evidence in identifying<br />
and characterizing phenotypic variance to individual drugs and how the<br />
pharmacological effects <strong>of</strong> these drugs depend on pharmacogenetic determinants<br />
that define sensitivity to the drug, and ultimately describes the drug’s behavior.<br />
THE LEUKEMIA STORY: WHAT DID WE LEARN?<br />
<strong>Ph</strong>armacological and pharmacogenetic studies are increasingly being used to<br />
optimize existing drug therapy with the goal <strong>of</strong> reducing toxicity while maximizing<br />
efficacy, leading to improved survival. By utilizing this method<br />
remarkable progress has been observed in the treatment <strong>of</strong> acute lymphoblastic<br />
leukemia (ALL) with cure rates increasing from a mere 10% (four decades ago)<br />
to nearly 80% in children and 40% in adults (21). This has been achieved<br />
through optimization <strong>of</strong> use <strong>of</strong> a variety <strong>of</strong> commonly used antileukemic drugs<br />
such as mercaptopurine and methotrexate.<br />
Thiopurine methyltransferase (TPMT) is an enzyme that converts thiopurine<br />
prodrugs, such as mercaptopurine, into inactive methylated metabolites.<br />
Prior to inactivation, mercaptopurine is converted into a variety <strong>of</strong> active<br />
nucleotide metabolites that are incorporated into DNA and RNA, thereby<br />
inducing an antileukemic effect. Patients with an inherited TPMT deficiency<br />
suffer severe, potentially fatal hematopoietic toxicity when exposed to standard<br />
doses <strong>of</strong> mercaptopurine (and other thiopurine prodrugs such as azathioprine and<br />
thioguanine). More than 95% <strong>of</strong> the clinically relevant TPMT mutations are<br />
accounted for by three nonsynonymous SNPs (TPMT*2, TPMT*3A, and<br />
TPMT*3C) (22). TPMT activity is inherited as an autosomal codominant trait<br />
with approximately 95% <strong>of</strong> the population homozygous for the wild-type allele<br />
TPMT*1 (that have full enzyme activity), 10% heterozygous for the polymorphism<br />
(that have intermediate levels <strong>of</strong> enzyme activity), and 1 in 300<br />
individuals will carry two mutant TPMT alleles (that do not express functional<br />
TPMT) (23). Using a pharmacogenetic test, developed at St. Jude Hospital,<br />
patients are <strong>of</strong>ten classified according to their levels <strong>of</strong> TPMT activity into<br />
normal, intermediate, and deficient. This test has a concordance rate <strong>of</strong> 100%<br />
between genotype and phenotype with studies revealing no long- term outcome
18 Dawood<br />
Figure 3 Individualization <strong>of</strong> dosing based on TPMT status does not affect cumulative<br />
incidence <strong>of</strong> relapse. Source: From Ref. 24.<br />
changes related to dosage individualization based on the TPMT status <strong>of</strong> a<br />
patient (24) (Fig. 3). This illustrates how the clinical application <strong>of</strong> pharmacogenetics<br />
can be used to minimize toxicity without compromising efficacy.<br />
Methyltetrahydr<strong>of</strong>olate reductase (MTHFR), a crucial enzyme in the<br />
metabolism <strong>of</strong> folic acid, catalyses the reduction <strong>of</strong> 5,10-methylenetetrahydr<strong>of</strong>olate<br />
to 5-methyltetrahydr<strong>of</strong>olate (the predominant circulatory form <strong>of</strong> folate<br />
and the carbon donor for the demethylation <strong>of</strong> homocysteine to methionine). As<br />
methotrexate increases serum levels <strong>of</strong> homocysteine and the active polyglutamate<br />
metabolites <strong>of</strong> methotrexate inhibit MTHFR function, patients with a<br />
low level <strong>of</strong> MTHFR function are at an increased risk <strong>of</strong> methotrexate-induced<br />
toxicities such as oral mucosities. Two polymorphisms associated with reduced<br />
MTHFR activity have been described (25): (i) 677C > T polymorphism that is<br />
homozygous in 10% <strong>of</strong> the population where it encodes the enzyme with 30% <strong>of</strong><br />
the wild-type enzyme activity and heterozygous in 40% <strong>of</strong> the population where<br />
it encodes an enzyme that has 60% <strong>of</strong> the wild type-enzyme activity and (ii)<br />
1298A > C (less common than 677C > T). Recent findings have also suggested<br />
that MTHFR polymorphisms may protect against the development <strong>of</strong> both adult<br />
and pediatric ALL (26).<br />
5-FLUOROURACIL<br />
5-Fuorouracil (5-FU) is a uracil analog widely used to treat solid tumors such as<br />
breast and colorectal cancer (27). 5-FU is inactivated by dihydropyrimidine<br />
dehydrogenase (DPD), an enzyme that exhibits up to 20-fold variation in activity<br />
among individuals (28). Mutations that result in low DPD activity predispose to<br />
potentially life-threatening gastrointestinal, hematopoietic, and neurological<br />
toxicities (29). Up to 20 mutations (30) have been identified that cause inactivation<br />
<strong>of</strong> DPD. In the general population it has been estimated that 3% to 5% <strong>of</strong><br />
individuals are heterozygous and 0.1% are homozygous carriers for the mutation<br />
that inactivates DPD, with DPYD*2A being the most inactivating allele (31).<br />
Although polymorphisms associated with DPD inactivation have been identified,<br />
a number <strong>of</strong> patients still exhibit severe 5-FU toxicity with no detectable
<strong>Ph</strong>armacology, <strong><strong>Ph</strong>armacogenetics</strong>, and <strong>Ph</strong>armacoepidemiology 19<br />
mutations in the coding region <strong>of</strong> the DPD gene explaining the difficulty in<br />
implementation <strong>of</strong> DPD pharmacogenetics in the prospective identification <strong>of</strong><br />
high-risk patients for severe 5-FU toxicity.<br />
One <strong>of</strong> the primary mechanisms <strong>of</strong> action <strong>of</strong> 5-FU is inhibition, via fluorodeoxyuridine<br />
monophosphate (an active metabolite <strong>of</strong> 5-FU), <strong>of</strong> thymidylate<br />
synthase (TS), a critical enzyme required for de novo thymidylate synthesis,<br />
which is required for DNA synthesis and repair (32). Polymorphisms in the gene<br />
encoding TS are one <strong>of</strong> the few examples <strong>of</strong> a polymorphism that is associated<br />
with the genetic makeup <strong>of</strong> the tumor with clinical studies showing an inverse<br />
relationship between TS levels and antitumor response (33). The TSER polymorphism<br />
<strong>of</strong> TS is characterized by tandem repeats in the TS promoter enhancer<br />
region <strong>of</strong> the gene. Higher levels <strong>of</strong> TS expression and enzyme activity have been<br />
observed with increasing copies <strong>of</strong> tandem repeats, which result in lower tumor<br />
response rates to 5-FU (34). In a study by Marsh et al (35), it was observed that in<br />
a cohort <strong>of</strong> patients with metastatic colorectal tumors receiving 5-FU-based chemotherapy<br />
2 tandem repeats was nearly twice as common in responders than in<br />
non-responders with longer median survival observed in patients with 2 tandem<br />
repeats compared to those with 3 tandem repeats (16 months vs. 12 months) (35).<br />
Polymorphisms <strong>of</strong> TS that result in 5-FU variability <strong>of</strong> its pharmacodynamic<br />
and pharmacokinetic properties illustrate the important fact that cancer<br />
being a polygenic disease will need a polygenic solution to guide therapy. To<br />
accurately select patients that are most likely to tolerate and respond to 5-FU<br />
therapy, one needs to use combined genotyping <strong>of</strong> DPYD and TSER functional<br />
variants as well as taking into account nongenetic factors.<br />
TAXANES<br />
Paclitaxel and Docetaxel are the two most commonly used taxanes in the<br />
treatment <strong>of</strong> both early and advanced stage breast cancer (36). They exert their<br />
cytotoxic effects by binding to B-tubulin that subsequently results in the stabilization<br />
<strong>of</strong> microtubules and disruption <strong>of</strong> mitotic spindle formation during cell<br />
division ultimately resulting in cell death. Genes involved in drug transport,<br />
metabolism, and drug target all play an important role in overall taxane efficacy,<br />
with their polymorphisms partly explaining the variability observed in toxicity<br />
and response to these drugs.<br />
The multidrug-resistant transporter, P-glycoprotein, encoded by ABCB1<br />
(MDR1) and expressed in multiple cell types, has been shown to mediate both<br />
paclitaxel and docetaxel influx (37,38). Three polymorphisms in ABCB1 have<br />
been commonly studied (3435C>T, 1236C>Y, and 2677G>T/A) (39), with<br />
most studies showing inconsistent results when trying to establish an association<br />
between these polymorphisms and drug efficacy/toxicity. Studies focusing on<br />
looking at ABCB1 polymorphism in combination may yield more useful clinical<br />
information. Some <strong>of</strong> the recent studies have reported an association <strong>of</strong> the<br />
various ABCB1 polymorphisms with tumor response to paclitaxel (ABCB1
20 Dawood<br />
2677G>T/A) (40), risk <strong>of</strong> neutropenia with docetaxel (ABCB1 3435C>T) (41),<br />
and risk <strong>of</strong> neuropathy with paclitaxel (ABCB1 3435C>T) (42). Oxidative<br />
taxane metabolism occurs via the CYP450 pathway, with CYP2C8 and CYP3A4<br />
primarily responsible for paclitaxel metabolism while docetaxel is mainly<br />
metabolized by CYP3A4 (43,44). The functional significance <strong>of</strong> the polymorphisms<br />
<strong>of</strong> these enzymes has not been established with associations seen in<br />
in vivo studies (45) not observed in studies <strong>of</strong> cancer patients (46).<br />
TAMOXIFEN<br />
Tamoxifen is a selective estrogen receptor modulator (SERM) that is widely<br />
used in the treatment and prevention <strong>of</strong> breast cancer. When given in the<br />
adjuvant setting, it reduces the annual risk <strong>of</strong> recurrence <strong>of</strong> breast cancer by half<br />
and the breast cancer mortality rate by one-third (47). The pharmacology and<br />
metabolism <strong>of</strong> tamoxifen is complex and undergoes extensive phase I and II<br />
metabolism with the production <strong>of</strong> endoxifen and to a lesser extent 4-hydroxytamoxifen<br />
(48). These metabolites play an important role in the anticancer<br />
effect <strong>of</strong> tamoxifen through their high-affinity binding to estrogen receptors and<br />
suppression <strong>of</strong> estradiol-stimulated cell proliferation (49).<br />
The clinical effects <strong>of</strong> tamoxifen are highly variable (50) due to the variability<br />
observed in the plasma levels <strong>of</strong> the more potent metabolites <strong>of</strong> tamoxifen.<br />
Tamoxifen is converted to endoxifen (4-hydroxy-N-desmethyltamoxifen) primarily<br />
by CYP2D6-mediated oxidation <strong>of</strong> N-desmethyl tamoxifen, while the<br />
conversion <strong>of</strong> tamoxifen to 4-hydroxy tamoxifen is catalyzed by multiple enzymes<br />
(51). Plasma concentrations <strong>of</strong> endoxifen is thus sensitive to variants <strong>of</strong> the<br />
CYP2D6 genotype with the most common variant making the enzyme inactive<br />
(common allele associated with CYP2D6 poor metabolizer phenotype is<br />
CYP2D6*4). One prospective trial revealed that women treated with tamoxifen<br />
who either carried the genetic variants associated with low or absent CYP2D6<br />
activity or were treated concomitantly with drugs that inhibited the activity <strong>of</strong><br />
CYP2D6 had lower levels <strong>of</strong> endoxifen (52). Subsequently, using tissue sample<br />
from a cohort <strong>of</strong> early stage breast cancer patients treated with tamoxifen, it was<br />
shown that women with a homozygous CYP2D6 phenotype tended to have a<br />
higher risk <strong>of</strong> disease relapse and a lower incidence <strong>of</strong> hot flashes (53).<br />
Polymorphisms associated with the activity <strong>of</strong> the metabolites <strong>of</strong> tamoxifen<br />
teach us two important lessons. First, the interindividual variability in the<br />
response to tamoxifen is explained both by genetic variation in CYP2D6 and<br />
the coadministration <strong>of</strong> drugs that inhibit the activity <strong>of</strong> CYP2D6. Second, the<br />
toxicity associated with drug can help identify responders.<br />
CONCLUSION<br />
At present time, dosages <strong>of</strong> anticancer drugs are administered based on the<br />
assumption <strong>of</strong> homogeneity among individuals and are rarely based on individual<br />
characteristics. As a result we encounter a situation where by a large
<strong>Ph</strong>armacology, <strong><strong>Ph</strong>armacogenetics</strong>, and <strong>Ph</strong>armacoepidemiology 21<br />
number <strong>of</strong> individuals experience unwanted adverse side effects after treatment<br />
without attaining maximum drug efficacy. Such adverse drug reactions impose a<br />
huge burden not only on the patient but also on the health care system. A better<br />
understanding <strong>of</strong> the pharmacoepidemiological, pharmacological, and pharmacogenetic<br />
determinants <strong>of</strong> a chemotherapeutic agent is needed to optimize drug<br />
dosing regimens to obtain maximal efficacy with minimal toxicity. Advancements<br />
in the field <strong>of</strong> pharmacogenetics hold promise in helping to identify which<br />
polymorphisms are likely to be important in clinical oncology thereby enabling<br />
the development <strong>of</strong> effective agents that will make their way to the clinic in a<br />
timely manner. Realizing the importance <strong>of</strong> each <strong>of</strong> the components <strong>of</strong> individualized<br />
therapy and incorporating each one into the infrastructure <strong>of</strong> clinical<br />
trials will allow us to further explore and define the relationships between different<br />
polymorphisms and observed phenotypes. Considering the fact that anticancer<br />
drugs have narrow a therapeutic range oncology is a field that will<br />
certainly benefit from the application <strong>of</strong> pharmacogenetic and pharmacological<br />
principles to dosage individualization. The idea <strong>of</strong> the “one model fits all” should<br />
be accepted as grossly flawed and, as we progress in the field <strong>of</strong> cancer therapeutics,<br />
will be replaced in the future with the application <strong>of</strong> “personalized<br />
cancer chemotherapy” into routine clinical practice.<br />
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Oncol 2005; 23:9312–9318.
3<br />
Role <strong>of</strong> Genetic Variability in <strong>Breast</strong><br />
<strong>Cancer</strong> Treatment Outcomes<br />
Kandace L. Amend<br />
Department <strong>of</strong> Oncological Sciences, Mount Sinai School <strong>of</strong> Medicine,<br />
New York, New York, U.S.A.<br />
Ji-Yeob Choi and Christine B. Ambrosone<br />
Department <strong>of</strong> <strong>Cancer</strong> Prevention and Control, Roswell Park<br />
<strong>Cancer</strong> Institute, Buffalo, New York, U.S.A.<br />
INTRODUCTION<br />
Variability in toxicity and response to the same dose <strong>of</strong> medication may occur<br />
among cancer patients and contribute to the unpredictability <strong>of</strong> clinical outcomes.<br />
In addition to the potential importance <strong>of</strong> clinical factors in determining<br />
drug effects (1), inherited differences in drug metabolism are likely to have a<br />
pr<strong>of</strong>ound effect on treatment outcomes (2). <strong><strong>Ph</strong>armacogenetics</strong>, the study <strong>of</strong> the<br />
contribution <strong>of</strong> genetic variation to these interindividual differences, may allow<br />
for the individualization <strong>of</strong> therapy and improved prediction <strong>of</strong> pharmacodynamic<br />
events, including host toxicity and treatment efficacy. To date, the role <strong>of</strong><br />
pharmacogenetics in breast cancer therapy has not been clearly defined. Herein<br />
we discuss the drugs most commonly used to treat breast cancer and describe<br />
their metabolic pathways, as well as provide epidemiologic support for the role<br />
<strong>of</strong> pharmacogenetics in breast cancer outcomes.<br />
25
26 Amend et al.<br />
BREAST CANCER THERAPEUTICS<br />
The most frequently used agents to treat breast cancer include cyclophosphamide<br />
(CP) in combination with doxorubicin [Adriamycin 1 (A)] or with methotrexate<br />
(MTX) and 5-flourouracil (5FU) (3). The taxanes paclitaxel (Taxol 1 ) and<br />
docetaxel (Taxotere 1 ) have an important role in the treatment <strong>of</strong> early-stage<br />
breast cancer as well as metastatic disease. In patients with estrogen receptor<br />
(ER)-positive tumors, tamoxifen (TAM) reduces the risk <strong>of</strong> recurrence by 31%<br />
and breast cancer morality by one-third (4), and randomized trials have indicated<br />
that treatment with third-generation aromatase inhibitors (AIs) is now an<br />
important option for the adjuvant treatment <strong>of</strong> hormone receptor–positive breast<br />
cancer (5,6).<br />
Multiple enzymatic pathways are involved in the metabolism and detoxification<br />
<strong>of</strong> chemotherapeutic agents, and common genetic polymorphisms in<br />
drug metabolizing enzymes are likely to contribute to interindividual variability<br />
in toxicity and cancer recurrence (7).<br />
Cyclophosphamide<br />
Cyclophosphamide requires multistep activation before it can function as an<br />
antitumor alkylating agent. The drug is first oxidized in the liver to the active<br />
4-hydroxy metabolite mainly by CYP2B6, although other enzymes, including<br />
CYP2A6, CYP3A4, CYP2C8, CYPC29, and CYP2C19, are involved. Drug<br />
intermediates are transported in blood and decompose spontaneously to phosphoramide<br />
mustard and acrolein, both <strong>of</strong> which can react with nucleophilic<br />
groups, forming DNA cross-links and inhibition <strong>of</strong> protein synthesis (8). Inactivating<br />
glutathionyl conjugation reactions <strong>of</strong> C intermediates are catalyzed by<br />
glutathione S-transferases (GSTs) GSTA1 and GSTP1 (9). Variability in the<br />
activity <strong>of</strong> these activating and detoxifying enzymes is likely to affect ultimate<br />
tumor tissue dose and efficacy <strong>of</strong> treatment.<br />
The human cytochrome P450 CYP2B6 gene is involved in the metabolic<br />
activation <strong>of</strong> a number <strong>of</strong> clinically important chemotherapeutic drugs for breast<br />
cancer, including CP (10,11) as well as the antioestrogen TAM (12). Several<br />
single nucleotide polymorphisms (SNPs) in CYP2B6 have been described, some<br />
having functional significance (13) and affecting the pharmacokinetic parameters<br />
<strong>of</strong> CP. In cell culture, enzymatic activities in microsomes from COS-1 cells<br />
expressing a K262R amino acid substitution (CYP2B6*4, CYP2B6*6, and<br />
CYP2B6*7) showed increased values for V max and V max /K m compared with that<br />
<strong>of</strong> the wild type (CYP2B6*1) (14), while a second study reported that the amino<br />
acid substitution Q172H resulting from the CYP2B6*6 allele enhanced<br />
7-ethoxycoumarin O-deethylase activity (15). A study by Lang et al. (2001)<br />
identified a total <strong>of</strong> nine SNPs by sequencing DNA from 35 subjects and<br />
reported extensive variability in the expression and activity <strong>of</strong> CYP2B6, where<br />
CYP2B6 expression was significantly reduced in carriers <strong>of</strong> the R487T, Q172H,
Role <strong>of</strong> Genetic Variability in <strong>Breast</strong> <strong>Cancer</strong> Treatment Outcomes 27<br />
and K262R polymorphisms (16). Ethnic variation in allelic variants and CYP2B6<br />
expression has been also been observed (17,18).<br />
CYP3A4 also plays a large role in the metabolism <strong>of</strong> CP, and the CYP3A4<br />
gene has a number <strong>of</strong> known variant alleles, including some having functional<br />
significance (19). A single base substitution in the 5 0 promoter region <strong>of</strong> the gene<br />
(20), present in 9% <strong>of</strong> European-Americans and 53% <strong>of</strong> African-Americans (21),<br />
with the CYP3A4*1B allele associated with expression levels is 1.4-fold higher<br />
than that in common alleles (22). Using enzyme kinetic analyses to evaluate<br />
intersample variation in activation <strong>of</strong> CP by CYP3A and 2B in human liver, both<br />
Chang et al. (1993) and Roy et al. (1999) found considerable interindividual<br />
differences in CYP3A and CYP2B levels and variation over a wide range in CP<br />
hydroxylation (10,23). Variation in CYP3A enzyme levels and expression may<br />
be related, in part, to genetic differences or to other chemotherapeutic and<br />
natural agents that have been shown to increase CYP3A4 expression (24).<br />
CYP3A5 accounts for at least 50% <strong>of</strong> the total CYP3A content in most<br />
Caucasians and African-Americans and is thus considered an important contributor<br />
to interindividual differences in CYP3A-dependent drug clearance (25).<br />
Although CYP3A5 is polymorphically expressed at high levels in a minority <strong>of</strong><br />
Caucasians, the expression in human liver is more frequent in African-<br />
Americans (60%) than in Caucasians (33%) (25). Reduced expression in some<br />
individuals may be due to a common polymorphism in intron 3 <strong>of</strong> CYP3A5<br />
(A6986G), which generates a cryptic splice site (CYP3A*6) resulting in a<br />
truncated amino acid (25,26).<br />
Genetic polymorphisms have been described in several <strong>of</strong> the CYP2C<br />
family members, including CYP2C8, CYP2C29, CYP2C18, and CYP2C19,<br />
although the catalytic contribution <strong>of</strong> CYP2C8 and CYP2C18 to activation <strong>of</strong> CP<br />
is likely minor compared with the contributions <strong>of</strong> CYP2C9 and CYP2C19. In<br />
vitro, CYP2C19 was found to bioactivate CP in human liver microsomes (27),<br />
and while both CYP2C9 and CYP2C19 were found to possess CP 4-hydroxylase<br />
activity in vitro, CYP2C19 exhibited higher CP 4-hydroxylase activity following<br />
only CYP2B6 (23). Although known variants in CYP2C9 and CYP2C19 exist,<br />
little is known regarding their contribution to pharmacokinetic variability in<br />
relation to breast cancer prognosis. In a study by Xie et al., CYP2C9*2,<br />
CYP2C9*3, CYP2C19*2, and CYP2C19*3 were not significantly correlated with<br />
the metabolism <strong>of</strong> CP in 19 human liver specimens (11).<br />
<strong>Ph</strong>ase II drug metabolism is catalyzed by GSTs, which consist <strong>of</strong> several<br />
isoenzymes, including mu (GSTM1), pi (GSTP1), alpha (GSTA1), theta<br />
(GSTT1), and zeta. The GSTs detoxify many chemotherapeutic drugs, including<br />
CP, are induced by oxidative stress, and detoxify the reactive oxygen species<br />
(ROS) that are produced by chemotherapeutic drugs and radiation therapy.<br />
GSTA1, the enzyme most active in glutathione conjugation reactions with CP<br />
intermediates, has a polymorphism in the 5 0 promoter region <strong>of</strong> the gene (28).<br />
Coles et al. (2001) identified substitutions designated as GSTA1*A ( 567, 69,<br />
and 52) and GSTA1*B ( 631, 567, and 69) (29), with the GSTA1*B variant
28 Amend et al.<br />
showing decreased hepatic expression and increased GSTA2 expression compared<br />
with GSTA1*A homozygotes. Given that GSTA1 increases the conjugation<br />
<strong>of</strong> CP intermediates (9), these differences in hepatic expression may affect the<br />
detoxification <strong>of</strong> CP metabolites and, ultimately, the outcomes <strong>of</strong> treatment and<br />
survival.<br />
For the GSTP1 gene, two variant alleles, GSTP1*B and GSTP1*C, have<br />
been detected (30). A single amino acid change from isoleucine to valine occurs<br />
at codon 105 (Ile105Val) <strong>of</strong> the GSTP1 gene, with genotype frequencies in<br />
Caucasians 51% homozygous for Ile/Ile, 43% heterozygous for Ile/Val, and 6%<br />
homozygous for the variant allele Val/Val (31). The allelic variant 105Val<br />
exhibited lower glutathione conjugation <strong>of</strong> the alkylating agent thiopeta compared<br />
with the isoleucine allele (32). GSTP1 variants may also differ in activity<br />
toward CP, structurally similar to thiotepa, and patients with GSTP1 allelic<br />
variants may have reduced activity in removal <strong>of</strong> active CP.<br />
Anthracyclines<br />
The commonly used anthracyclines include doxorubicin, daunorubicin, and<br />
epirubicin, although doxorubicin and epirubicin are used more frequently for the<br />
treatment <strong>of</strong> breast cancer. Several different mechanisms have been proposed for<br />
anthracycline tumor activity, including free radical generation, DNA binding and<br />
alkylation, DNA cross-linking, interference with DNA unwinding or strand<br />
separation, direct membrane effects, inhibition <strong>of</strong> topoisomerase II, and induction<br />
<strong>of</strong> apoptosis (33). Doxorubicin, like other anthracyclines, results in the<br />
formation <strong>of</strong> quinone-mediated free radicals, which have the capacity to cause<br />
oxidative damage and cytotoxicity, resulting in significant cardiotoxicity as a<br />
result <strong>of</strong> the production <strong>of</strong> ROS (34). Cellular oxidoreductases reduce Adriamycin<br />
to a semiquinone radical that is subsequently reoxidized by oxygen to a<br />
superoxide anion and the parent quinine (35–37). Superoxide anions can dismutate<br />
to form hydrogen peroxide and/or react with nitric oxide to form peroxynitrite.<br />
Lipid peroxides resulting from doxorubicin can further break down to<br />
yield hydroxyalkenals, which are substrates for glutathione-conjugating isozymes<br />
(38), indicating the potential importance <strong>of</strong> GSTs in mediating therapeutic<br />
outcomes.<br />
Carbonyl reductase (CBR) is a monomeric, NADPH-dependent oxidoreductase<br />
that catalyzes the reduction <strong>of</strong> a variety <strong>of</strong> carbonyl compounds,<br />
including doxorubicin and daunorubicin. In humans, two CBRs have been<br />
identified, CBR1 and CBR3. An SNP has been identified in CBR3 (CBR3<br />
V244M), where the M244 is<strong>of</strong>orm results in higher V max and is 100% more<br />
efficient in catalyzing the reduction <strong>of</strong> substrate (39). To our knowledge, CBRs<br />
have not been investigated in relation to breast cancer outcomes following<br />
treatment with anthracyclines.<br />
In addition to its mechanistic role in cytotoxicity <strong>of</strong> anthracyclines, it is well<br />
documented that oxidative stress plays a role in the cell-killing abilities <strong>of</strong> many
Role <strong>of</strong> Genetic Variability in <strong>Breast</strong> <strong>Cancer</strong> Treatment Outcomes 29<br />
other chemotherapy agents, as well as in radiation therapy. Specifically for breast<br />
cancer, CP, doxorubicin (Adriamycin), and paclitaxel and radiation therapy generate<br />
ROS and induce apoptosis, which may contribute to treatment efficacy and<br />
increased survival. ROS can cause mitochondrial permeability, and once the<br />
mitochondrial membrane barrier function is lost, a number <strong>of</strong> other factors contribute<br />
to cell death. While ROS, among other factors, induce or facilitate mitochondrial<br />
permeability, glutathione and antioxidant enzymes such as manganese<br />
superoxide dismutase (MnSOD), catalase (CAT), and glutathione peroxidase<br />
(GPX1) inhibit it (40). In the mitochondrion, MnSOD catalyzes the dismutation <strong>of</strong><br />
two superoxide radicals, producing H 2 O 2 and oxygen. If H 2 O 2 is not neutralized, it<br />
may contribute to further generation <strong>of</strong> ROS by a reaction catalyzed by myeloperoxidase<br />
(MPO). Thus, levels <strong>of</strong> MnSOD, CAT, GPX1, and MPO are important<br />
in determining the amount <strong>of</strong> ROS or detoxification <strong>of</strong> H 2 O 2 . It is plausible that<br />
interindividual variability resulting from polymorphisms in these genes may<br />
influence the efficacy <strong>of</strong> cancer treatment and survival.<br />
Two genetic polymorphisms appear to affect mitochondrial levels <strong>of</strong><br />
MnSOD. A single-base substitution variant in the MnSOD gene results in an<br />
amino acid substitution from alanine to valine at position 9 (ala-9) in the<br />
mitochondrial targeting sequence (41). The substitution alters the secondary<br />
structure <strong>of</strong> the protein, affecting the cellular distribution <strong>of</strong> the enzyme and<br />
transport <strong>of</strong> MnSOD in the mitochondrion, where it would be biologically<br />
available (41). In the mitochondria, the MnSOD Ala variant has been shown to<br />
generate more active MnSOD protein compared with the Val allele (42). A<br />
second polymorphic variant has been identified in MnSOD, and Ile or Thr at<br />
amino acid position 58 (43) and the Thr 58 MnSOD variant was shown to exhibit<br />
only half the enzymatic activity <strong>of</strong> Ile 58 MnSOD (43,44). It can be hypothesized<br />
that the variant (ala-9 and Thr 58 MnSOD) would confer reduced protection <strong>of</strong><br />
breast tumor cells from ROS produced during therapy. Thus, lesser MnSOD<br />
activity due to polymorphisms could increase treatment efficacy but also result<br />
in greater toxicity due to ROS.<br />
CAT is most abundant in the liver, kidneys, and erythrocytes, where it<br />
decomposes H 2 O 2 to H 2 O and O 2 . A common functional polymorphism<br />
(C-262T) alters transcription factor binding and the expression <strong>of</strong> CAT in<br />
erythrocytes. We previously found that individuals with C alleles had significantly<br />
higher CAT levels compared with those with T/T genotypes (45).<br />
H 2 O 2 , if not neutralized, may contribute to further generation <strong>of</strong> ROS by a<br />
reaction catalyzed by MPO. MPO generates ROS endogenously by functioning<br />
as an antimicrobial enzyme, catalyzing a reaction between H 2 O 2 and chloride to<br />
generate hypochlorous acid (HOCl), a potent oxidizing agent. HOCl further<br />
reacts with other biological molecules to generate secondary radicals (46). The<br />
promoter region <strong>of</strong> the MPO gene has a G-to-A base substitution at position 463,<br />
which creates high- (G) and low-expression (A) alleles (47). Variability in genes<br />
in this oxidative stress-related pathway may influence ultimate levels <strong>of</strong> ROS<br />
and, thereby, tumor cell kill.
30 Amend et al.<br />
5-Fluorouracil and Methotrexate<br />
The agents 5FU and MTX are involved in folate metabolism, in the conversion<br />
<strong>of</strong> homocysteine to methionine, and in purine and pyrimidine synthesis. The<br />
activated metabolites <strong>of</strong> 5FU, similar to pyrimidine nucleotides, competitively<br />
and reversibly bind fluoropyrimidine thymidylase synthase (TS) inhibitors,<br />
forming covalently bound complexes with the TS enzyme, thus inhibiting the<br />
enzyme’s activity (48). Folate antagonists, such as MTX, are structurally similar<br />
to folate and inhibit the action <strong>of</strong> various enzymes such as dihydr<strong>of</strong>olate<br />
reductase (DHFR) (49). Their action is accomplished through incorporation as<br />
false precursors in DNA or RNA or through inhibition <strong>of</strong> proteins involved in<br />
nucleotide metabolism.<br />
Common polymorphisms have been reported in many enzymes and transport<br />
proteins involved in intracellular folate metabolism. Using a candidate gene<br />
approach, pharmacogenetic research, to date, has focused on TS because <strong>of</strong> its role<br />
as a drug target, dihydropyrimidine dehydrogenase (DPD) because <strong>of</strong> its<br />
involvement in pyrimidine degradation, and methylenetetrahydr<strong>of</strong>olate reductase<br />
(MTHFR) because <strong>of</strong> its key involvement in the control <strong>of</strong> the flux <strong>of</strong> intracellular<br />
folate metabolites (50). A genetic polymorphism in the 5 0 regulatory region <strong>of</strong> the<br />
TS gene promoter, consisting <strong>of</strong> either double (2R) or triple (3R) repeats <strong>of</strong> a 28-bp<br />
sequence (51), has been shown to influence TS expression, with higher expression<br />
in 3R/3R tumors (52). A G>C polymorphism has been identified within the<br />
second repeat <strong>of</strong> 3R alleles that alters the transcriptional activation <strong>of</strong> the TS gene<br />
constructs bearing this genotype (53). A 6-bp deletion (1494del6) within the<br />
3 0 UTR (54) has been also associated with decreased mRNA stability (55). Several<br />
clinical studies have shown the potential contribution <strong>of</strong> the above TS germinal<br />
polymorphism in the prediction <strong>of</strong> tumor responsiveness and/or toxicity following<br />
treatment by fluoropyrimidines (56). However, the role <strong>of</strong> TS polymorphism as a<br />
predictor <strong>of</strong> treatment outcome is still unclear.<br />
MTHFR catalyzes the conversion <strong>of</strong> 5,10-methyltetrahydr<strong>of</strong>olate to 5-methyltetrahydr<strong>of</strong>olate,<br />
which is an essential c<strong>of</strong>actor in the biosynthesis <strong>of</strong> deoxythymidine<br />
monophosphate. A common C677T transition in exon 4 and an A1298C<br />
transition in exon 7 <strong>of</strong> the MTHFR gene result in lower specific activity (57,58).<br />
Most studies, to date, have examined the association between 5FU and MTHFR<br />
polymorphisms in the context <strong>of</strong> colorectal cancer, but T<strong>of</strong>foli et al. (59) reported<br />
that <strong>of</strong> six patients who developed severe acute toxicity in the first cycle <strong>of</strong> adjuvant<br />
CMF, five had the MTHFR 677 T/T genotype and one had the C/C genotype.<br />
Because 5FU has a relatively narrow therapeutic index, toxicity increases<br />
as the dose is increased, and more than 80% <strong>of</strong> administered 5FU is catabolized<br />
by DPD (1). Patients with complete or partial deficiency <strong>of</strong> DPD <strong>of</strong>ten experience<br />
severe or even life-threatening toxicity after the administration <strong>of</strong> 5FU (60).<br />
At least 11 <strong>of</strong> 33 mutations identified in the dihydropyrimidine dehydrogenase<br />
(DPYD) gene have been detected in patients suffering from severe 5FU-associated<br />
toxicity (61–63).
Role <strong>of</strong> Genetic Variability in <strong>Breast</strong> <strong>Cancer</strong> Treatment Outcomes 31<br />
There have been few studies <strong>of</strong> MTHFR and TS polymorphisms in relation<br />
to breast cancer treatment outcomes. In a study with 19 human cancer cell lines,<br />
including breast cancer, Etienne et al. (64) found that there was a marked trend<br />
for greater 5FU efficacy in mutated MTHFR 1298C cell lines compared with<br />
those with A/A genotypes (P ¼ 0.055), but MTHFR C677T and TS 2R/3R<br />
polymorphisms were not associated with 5FU sensitivity. Largillier et al. (65)<br />
noted that genetic polymorphisms <strong>of</strong> MTHFR (677C>T and 1298A>C), DPD<br />
(IVS14þ1G>A), and TS were associated with efficacy and toxicity associated<br />
with capecitabine, an oral 5FU prodrug, in 105 breast cancer patients receiving<br />
capecitabine as monotherapy.<br />
Although MTX has been widely used to treat many cancers, including<br />
leukemia, lymphomas, and breast cancer, as well as some autoimmune diseases,<br />
there has been limited research to evaluate the effects <strong>of</strong> genetic variability on<br />
treatment outcomes. Associations have been noted for polymorphisms in folaterelated<br />
genes in leukemia (66–69), and Sohn et al. (70) found that in vitro<br />
transfection <strong>of</strong> mutant 677T MTHFR cDNA in breast and colon cancer cells<br />
increased the chemosensitivity <strong>of</strong> these cells to 5FU but decreased the chemosensitivity<br />
<strong>of</strong> breast cancer cells to MTX, suggesting at least a modulating role <strong>of</strong><br />
this genotype on cellular fluouropyrimidine sensitivity.<br />
Taxanes<br />
The taxanes paclitaxel and docetaxel interfere with microtubular disassembly,<br />
ultimately resulting in DNA fragmentation and features <strong>of</strong> apoptosis (71). The<br />
contributions <strong>of</strong> genetic variability to paclitaxel metabolism and breast cancer<br />
outcomes is still unclear. CYP2C8 is thought to be the main CYP-metabolizing<br />
enzyme <strong>of</strong> paclitaxel, while CYP3A4 and CYP3A5 play more minor roles in drug<br />
metabolism (19). At least three known variants in CYP2C8 exist, and the CYP2C8*3<br />
variant allele has been associated with decreased paclitaxel 6-ahydroxylase activity<br />
in human cell lines and human liver microsomes (72). In a recent study <strong>of</strong><br />
paclitaxel metabolism in breast cancer, however, CYP2C8 genotypes (CYP2C8*3<br />
and CYP2C8*4) were not significantly associated with paclitaxel clearance (73).<br />
Ethnic variation exists in the distribution <strong>of</strong> CYC2C8 alleles, with the CYP2C8*2<br />
allele more common in African-Americans than Caucasians (18% vs. 0%,<br />
respectively) and the CYP2C*3 allele more common in whites (13%) than<br />
African-Americans (2%). The CYPC2*5 allele is found in 9% <strong>of</strong> Asians (74).<br />
Antiestrogens<br />
The pharmacology and metabolism <strong>of</strong> TAM is complex, and differences in<br />
patient outcomes could be due to individual variation in the metabolism <strong>of</strong> the<br />
drug. TAM is metabolized primarily by CYP3A4, CYP2D6, CYP2C9,<br />
SULT1A1, and UGT2B15 (75–78). The main metabolites <strong>of</strong> TAM are<br />
N-desmethyltamoxifen (formed by CYP3A4) and endoxifen and 4-OH-TAM
32 Amend et al.<br />
(formed by CYP2D6) (8,76). CYP2D6 is polymorphic with 46 known allelic<br />
variants, which are categorized as resulting in abolished, decreased, normal,<br />
increased, or qualitatively altered catalytic activity (79). The major polymorphic<br />
variant CYP2D6 alleles include CYP2D6*2, CYP2D6*4, CYP2D6*5,<br />
CYP2D6*10, CYP2D6*17, and CYP2D6*41 (79).<br />
Human sulfotransferase 1A1 (SULT1A1) catalyzes the sulfation <strong>of</strong> a<br />
variety <strong>of</strong> phenolic and estrogenic compounds, including endogenous and<br />
environmental estrogens and the 4-hydroxy metabolite <strong>of</strong> TAM, 4-OH TAM<br />
(80). An exon 7 polymorphism in SULT1A1 (SULT1A1*2) results in a translated<br />
protein with approximately tw<strong>of</strong>old lower catalytic activity and decreased<br />
thermostability than the common allele (SULT1A1*1) (81). We found, as<br />
hypothesized, that women treated with TAM who were homozygous for the<br />
variant SULT1A1*2/*2 allele had approximately three times the risk <strong>of</strong> death<br />
compared with those homozygous for the common allele or heterozygotes (82).<br />
A wide range <strong>of</strong> drugs are substrates for glucuronosyltransferases (UGTs)<br />
and this pathway has been estimated to account for approximately one-third <strong>of</strong><br />
all drugs metabolized by phase II drug metabolizing enzymes (2). SULT1A1 and<br />
UGT2B15 have overlapping substrate specificity for 4-OH TAM. A functional<br />
polymorphism in UGT2B15, a G?T substitution at codon 85 within the putative<br />
substrate recognition site <strong>of</strong> the gene, results in higher V max compared with the<br />
common allele (83). In breast cancer patients treated with TAM, the combined<br />
effects <strong>of</strong> SULT1A1 and UGT2B15 were evaluated, and an increasing number <strong>of</strong><br />
variant SULT1A1 and UGT2B15 alleles was associated with poorer survival (84).<br />
Aromatase Inhibitors<br />
AIs, including exemestane, anastrozole, and letrozole, have come into clinical<br />
use more recently, developed primarily for hormone-dependent breast cancer in<br />
postmenopausal women. AIs act by inhibiting the cytochrome P450 enzyme,<br />
aromatase (CYP19), which catalyzes the conversion <strong>of</strong> androgens to estrogens.<br />
AIs block this enzyme, decreasing circulating levels <strong>of</strong> estrogens. It has been<br />
demonstrated that exemestane is metabolized by CYP3A4 but is also metabolized<br />
by aldoketoreductases (85). Letrazole is metabolized by CYP2A6 and<br />
CYP3A4 to a pharmacologically inactive carbinol metabolite (86), and anastrazole<br />
is metabolized by N-deaklylation and hydroxylation reactions followed<br />
by glucuronidation. However, specific is<strong>of</strong>orms involved and the effects on drug<br />
levels have not yet been identified.<br />
MOLECULAR EPIDEMIOLOGIC STUDIES OF PHARMACOGENETICS<br />
AND BREAST CANCER OUTCOMES<br />
To date, most analytical studies <strong>of</strong> pharmacogenetics and breast cancer outcomes<br />
have been conducted among patients receiving heterogeneous chemotherapy<br />
treatments, although most chemotherapy regimens included CP. Several studies
Role <strong>of</strong> Genetic Variability in <strong>Breast</strong> <strong>Cancer</strong> Treatment Outcomes 33<br />
have evaluated polymorphisms in phase II or GST enzymes in relation to<br />
prognosis after treatment for breast cancer. In a study <strong>of</strong> 92 women with<br />
advanced breast cancer, the GSTM1-null genotype was not significantly associated<br />
with tumor characteristics, disease-free survival (DFS), or overall survival<br />
(OS) (87), but in a retrospective study with a larger sample size, we found that<br />
women with low-activity homozygous genotypes for the GSTA1*B allele had<br />
better survival, with the hazard <strong>of</strong> death reduced by 30% (88,89), and women<br />
with low-activity GSTP1 Val/Val genotype had a similar risk reduction (89).<br />
Deletion polymorphisms in GSTM1 and GSTT1, genes responsive to ROS and<br />
resulting from radiation or chemotherapy and subsequent lipid peroxidation,<br />
were associated with a reduced hazard <strong>of</strong> death compared with women with both<br />
alleles present [HR ¼ 0.59; 95% confidence interval (CI), 0.36–0.97; and HR ¼<br />
0.51; 95% confidence interval (CI), 0.29–0.90] (90). In a study in China (91),<br />
women with GSTP1 Val/Val genotypes experienced a 60% reduction in<br />
overallmortality (HR ¼ 0.4; 95% confidence interval (CI), 0.2–0.8), although<br />
no associations were found with GSTM1 or GSTT1 genotypes. A smaller study<br />
(n ¼ 85) evaluated polymorphisms in both phase I and phase II metabolism <strong>of</strong><br />
CP among women with metastatic or inflammatory breast cancer treated with<br />
high-dose CP, cisplatin, and carmustine. Patients with the GSTM1-null genotype<br />
had prolonged OS, while those with variants in CYP3A4*1B and CYP3A5*1 had<br />
shortened survival. To our knowledge, only one genotype association study has<br />
focused on paclitaxel metabolism. In that study <strong>of</strong> 93 patients (73), there were no<br />
significant associations between ABCB1, ABCG2, CYP1B1, CYP3A4, CYP3A5,<br />
and CYP2C8 and paclitaxel clearance. However, patients homozygous for<br />
CYP1B1*3 Leu/Leu genotype had significantly longer progression-free survival<br />
(PFS) compared with patients with at least one Val allele (Table 1).<br />
Few studies have examined the effects <strong>of</strong> MTHFR genotypes on breast<br />
cancer survival. In the Shanghai <strong>Breast</strong> <strong>Cancer</strong> Study (92), no significant<br />
associations were found between MTHFR genotypes and the risk <strong>of</strong> death. A<br />
second study evaluated these same genotypes in African-American and Caucasian<br />
women from the Baltimore area (93) and found that carriers <strong>of</strong> the lowactivity<br />
allele at codon 1298 (A/C or C/C) had significantly poorer survival,<br />
while those with the variant allele at codon 677 (C/T or T/T) had improved<br />
survival. Associations were stronger in patients with ER-negative tumors, and<br />
African-American women had improved survival if they were carriers <strong>of</strong> either<br />
variant allele (A1298C, P interaction ¼ 0.088, C677T, P interaction ¼ 0.026).<br />
Molecular epidemiologic studies have indicated that genetic variation in<br />
phases I and II conjugating enzymes can influence the efficacy <strong>of</strong> TAM therapy<br />
for breast cancer. In a recent study in Sweden (94), patients homozygous for<br />
CYP2D6*4 alleles experienced significantly better DFS compared with patients<br />
with at least *1 allele (P ¼ 0.05). In a group <strong>of</strong> women randomized to 5, rather<br />
than two years <strong>of</strong> TAM, patients homozygous for CYP3A5*3 experienced<br />
improved relapse-free survival (RFS) (HR ¼ 0.02; CI, 0.07–0.55). However, this<br />
finding should be interpreted with caution since the number <strong>of</strong> patients within
34 Amend et al.<br />
Table 1 Epidemiologic Studies <strong>of</strong> Genetic Variation in Chemotherapeutic Metabolizing Genes and <strong>Breast</strong> <strong>Cancer</strong> Outcomes<br />
Genes SNP Participants Treatment Outcome Risk (95% CI) Reference<br />
Cyclophosphamide<br />
GSTM1 GSTM1-null<br />
genotype<br />
92 patients CAF OS or DFS GSTM1 not significant 87<br />
GSTA1 GSTA1*B/*B 245 patients CP based 5-yr survival GSTA1*B/*B HR ¼ 0.3 (0.1–0.8) 88<br />
GSTP1 GSTP Val/Val 240 patients CP based OS GSTP Val/Val HR ¼ 0.3 (0.1–1.0) 89<br />
GSTP1 GSTP Val/Val 1034 patients CP based OS GSTP Val/Val HR ¼ 0.4 (0.2–0.8) 91<br />
6 genes, CYPs,<br />
ABCB1,<br />
ABCG2<br />
17 genes, CYPs,<br />
METs, GSTs<br />
8 SNPs 93 patients High-dose<br />
paclitaxel and<br />
CP and A<br />
29 SNPs 85 patients CP and cisplatin<br />
and carmustine<br />
PFS CYP1B1*3 Leu/Leu Longer PFS (P ¼ 0.037) 73<br />
PK <strong>of</strong> CP<br />
and OS<br />
GSTM1-null genotype; better OS; at least one<br />
variant allele in CYP3A4*1B or CYP3A5*1<br />
(98)<br />
Methotrexate and 5-fluorouracil<br />
MTHFR MTHFR C677T;<br />
MTHFR<br />
A1298C<br />
MTHFR MTHFR C677T;<br />
Tamoxifen<br />
CYP3A5;<br />
CYP2D6;<br />
SULT1A1;<br />
UGT2B15<br />
CYP3A5;<br />
CYP2D6<br />
MTHFR<br />
A1298C<br />
CYP3A5*3;<br />
CYP2D6*4;<br />
SULT1A1*2;<br />
UGT2B15*2<br />
CYP3A5*3;<br />
CYP2D6*4;<br />
1067 patients Combination<br />
(most received<br />
5FU)<br />
OS MTHFR genotypes not significant with OS 92<br />
248 patients Combination OS MTHFR 1298 A/C or C/C HR ¼ 2.05<br />
(1.05–4.0);<br />
MTHFR 677 C/T or T/T HR ¼ 0.65 (0.31–1.35)<br />
677 patients<br />
(238<br />
randomized<br />
to TAM)<br />
TAM RFS CYP3A5 *3/*3 in 5-year<br />
TAM group HR ¼ 0.20 (0.07–0.55);<br />
CYP2D6 *4/*4 better<br />
RFS (n ¼ 677) (P ¼ 0.05)<br />
223 patients TAM RFS or DFS<br />
or OS<br />
CYP2D6 *4/*4<br />
HR ¼ 2.71 (1.15–6.41) for RFS;<br />
HR ¼ 2.44 (1.22–4.90) for DFS (both ratios<br />
unadjusted)<br />
(93)<br />
94<br />
(95)
Role <strong>of</strong> Genetic Variability in <strong>Breast</strong> <strong>Cancer</strong> Treatment Outcomes 35<br />
Table 1 Epidemiologic Studies <strong>of</strong> Genetic Variation in Chemotherapeutic Metabolizing Genes and <strong>Breast</strong> <strong>Cancer</strong> Outcomes (Continued )<br />
Genes SNP Participants Treatment Outcome Risk (95% CI) Reference<br />
CYP2D6;<br />
UGT2B15;<br />
SUL1A1<br />
CYP2D6*4;<br />
UGT2B15*2;<br />
SULT1A1*2<br />
337 patients<br />
(162 patients<br />
treated with<br />
TAM)<br />
SULT1A1 SULT1A1*2 377 patients<br />
(160 treated<br />
with TAM)<br />
TAM OS or PFS CYP2D6 NS;<br />
UGT2B15*2 and SULT1A1*2<br />
HR ¼ 4.4 (1.17–16.55) in TAM group<br />
TAM OS SULT1A1 *2/*2<br />
HR ¼ 2.9 (1.1–7.6) in TAM group<br />
84<br />
(82)<br />
Oxidative stress<br />
GSTM1/GSTT1 GSTM1 and<br />
GSTT1-null<br />
genotypes<br />
MPO, MnSOD,<br />
CAT<br />
MnSOD Ala-9Val,<br />
CAT-262C?T,<br />
MPO-463G?A<br />
MnSOD MnSOD-102 C?T 291 patients<br />
(230 treated<br />
with chemotherapy)<br />
251 patients Combination OS and DFS GSTM1-null genotype<br />
HR ¼ 0.59 (0.36–0.97;<br />
GSTT1-null genotype<br />
HR ¼ 0.51 (0.29–0.90)<br />
279 patients Combination OS MPO GG genotype<br />
HR ¼ 0.60 (0.38–0.95),<br />
MnSOD CC genotype HR ¼ 0.70 (0.40–1.24),<br />
genotypes combined HR ¼ 0.33 (0.13–0.80)<br />
Combination RFS MnSOD CT þ TT HR ¼ 0.65 (0.40–1.05)<br />
for chemotherapy group<br />
90<br />
96<br />
97<br />
Abbreviations: CAF, Cyclophosphamide/doxorubicin/fluorouracil; A, doxorubicin; CP, cyclophosphamide; T, DFS, disease-free survival; NS, not significant; OS,<br />
overall survival; PFS, progression-free survival; RFS, relapse-free survival; TAM, tamoxifen; 5FU, 5-fluorouracil; GST, glutathione S-transferase; SNP, single<br />
nucleotide polymorphism; METs, Metallothioneins; MTHFR, methylenetetrahydr<strong>of</strong>olate reductase; MPO, myeloperoxidase; MnSOD, manganese superoxide dismutase;<br />
CAT, catalase; PT, <strong>Ph</strong>armacokinetics.
36 Amend et al.<br />
this group was small. No statistically significant differences were found for<br />
CYP2D6, SULT1A1, or UGT2B15 and the length <strong>of</strong> TAM treatment. In contrast,<br />
Goetz et al. (95) found no significant associations between CYP3A5*3 and breast<br />
cancer outcomes; however, women with the CYP2D6 *4/*4 genotype (n ¼ 13)<br />
had statistically significant worse RFS (P ¼ 0.023) and DFS (P ¼ 0.12) but not<br />
OS (P ¼ 0.169). However, after adjustment for nodal status and tumor size,<br />
associations were no longer statistically significant. These results are in agreement<br />
with our previous report, wherein we noted no significant associations<br />
between the CYP2D6*4 genotype and OS or PFS among breast cancer patients<br />
treated with TAM (84).<br />
In addition to CYP is<strong>of</strong>orms, phase II enzymes, including sulfotransferase<br />
1A1 (SULT1A1) and UDP-glucuronosyltransferase is<strong>of</strong>orm 2B15 (UGT2B15),<br />
have been implicated in the metabolism <strong>of</strong> TAM, and genetic variability in these<br />
genes together may influence breast cancer survival. In a cohort <strong>of</strong> women with<br />
breast cancer who were treated with TAM, those who were homozygous for the<br />
low-activity SULT1A1*2 allele had a threefold higher risk <strong>of</strong> death compared<br />
with those with at least *1 common allele (HR ¼ 2.9; 1.1–7.6), while no significant<br />
associations were found among women who did not receive TAM (82).<br />
When considering the independent and combined effects <strong>of</strong> SULT1A1 and<br />
UGT2B15 genotypes on OS and recurrence among women treated with TAM,<br />
there was a nonsignificant trend toward increased risk <strong>of</strong> disease recurrence and<br />
poorer survival with increasing number <strong>of</strong> UGT2B15*2 alleles (P ¼ 0.11) (84).<br />
However, there was a significant trend toward decreased OS among women<br />
treated with TAM, with increasing number <strong>of</strong> variant alleles when UGT2B15 and<br />
SULT1A1 genotypes were combined (P ¼ 0.03), although these risk estimates<br />
may be unstable because only 16 cases had two variant alleles.<br />
We previously found that genotypes for MnSOD and MPO resulting in<br />
higher levels <strong>of</strong> ROS were associated with better survival in a study <strong>of</strong> women<br />
from Arkansas, and the combined effects <strong>of</strong> MnSOD and MPO were greater than<br />
those for either polymorphism alone (HR ¼ 0.33; CI, 0.13–0.80) (96). An<br />
additional MnSOD variant, located in the promoter region <strong>of</strong> the gene, was also<br />
associated with a significant decrease in RFS (CTþTT genotype HR ¼ 0.65, CI,<br />
0.40–1.05), although no significant associations were found with OS (97).<br />
In the context <strong>of</strong> a clinical trial for breast cancer with CAF versus CMF<br />
(SWOG 8897) (unpublished data, presented at the San Antonio <strong>Breast</strong> <strong>Cancer</strong><br />
Symposium, 2006), as in the Arkansas study, MPO genotypes encoding for<br />
higher activity were associated with better survival.<br />
FUTURE DIRECTIONS<br />
Studies, to date, indicate that interindividual variability in drug metabolism may<br />
affect treatment-related toxicities and DFS. However, much <strong>of</strong> the data are<br />
inconsistent, and it is likely that the evaluation <strong>of</strong> single genes in complex<br />
pathways presents only part <strong>of</strong> a complicated picture. Thus, future studies <strong>of</strong>
Role <strong>of</strong> Genetic Variability in <strong>Breast</strong> <strong>Cancer</strong> Treatment Outcomes 37<br />
pharmacogenetics and treatment outcomes should focus on entire drug metabolism<br />
pathways in large sample sets receiving homogeneous treatment regimens.<br />
Furthermore, assessment <strong>of</strong> variability across an entire gene, through construction<br />
<strong>of</strong> haplotype blocks and tag SNPs, may give more complete information<br />
than a study <strong>of</strong> single SNPs. It is likely that, in the future, other, more systemic<br />
markers <strong>of</strong> inherited drug metabolism capabilities may better predict treatment<br />
outcomes, and scans <strong>of</strong> variability across the entire genome may reveal factors<br />
that are likely to determine treatment outcomes. The consideration <strong>of</strong> individual<br />
genotypes, as well as tumor characteristics, in therapeutic decision making holds<br />
promise for individualized treatment for breast cancer in the future.<br />
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4<br />
The Role <strong>of</strong> Epigenetics in <strong>Breast</strong> <strong>Cancer</strong>:<br />
Implications for Diagnosis, Prognosis,<br />
and Treatment<br />
Amy M. Dworkin, Tim H.-M. Huang, and Amanda E. Toland<br />
Human <strong>Cancer</strong> Genetics Program, Department <strong>of</strong> Molecular Virology,<br />
Immunology, and Medical Genetics, The Ohio State University,<br />
Columbus, Ohio, U.S.A.<br />
INTRODUCTION<br />
In the past 10 years, the field <strong>of</strong> epigenetics has expanded into an essential<br />
component <strong>of</strong> clinical cancer research and therapeutics. Epigenetic modifications<br />
in DNA or chromatin are stably transmitted over rounds <strong>of</strong> cell division but do<br />
not result in any permanent alterations to the underlying DNA sequence. These<br />
changes, such as histone modifications and DNA methylation <strong>of</strong> tumor suppressor<br />
genes, are considered a hallmark <strong>of</strong> certain cancers, including breast<br />
cancer. Unlike germline mutations, epigenetic modifications can potentially be<br />
reversed, making them very appealing for preventative care and therapeutics<br />
against cancers (1). Because <strong>of</strong> this, demethylating agents are currently being<br />
evaluated for efficacy in the treatment <strong>of</strong> breast cancer.<br />
There are distinct mechanisms that initiate and sustain epigenetic modifications<br />
(2–8). Of these, DNA methylation and posttranslational modifications<br />
<strong>of</strong> histone proteins are the best understood. DNA methylation is a covalent<br />
addition <strong>of</strong> a methyl group to DNA, usually to a cytosine located 5 0 to guanasine<br />
(CpG dinucleotide). CpG dinucleotides are underrepresented in the genome,<br />
45
46 Dworkin et al.<br />
Figure 1 Methylation <strong>of</strong> CpG islands. Unmethylated CpG islands (in light gray) allow<br />
for transcription, while methylated CpG islands (in dark gray) repress transcription.<br />
except for small clusters, referred to as CpG islands, located in or near the<br />
promoter <strong>of</strong> approximately half <strong>of</strong> all genes (1,9–11). In addition, other epigenetic<br />
modifications have recently been explored, including the Polycomb<br />
group (PcG) proteins, which repress gene function and can only be overcome by<br />
germline differentiation processes, and small noncoding RNA molecules, which<br />
regulate gene expression by targeting RNA degradation (3). Small noncoding<br />
RNA molecules have recently been found to also target gene promoters and<br />
result in transcriptional gene silencing (11,12).<br />
DNA methylation plays a variety <strong>of</strong> roles in normal cells: silencing <strong>of</strong><br />
transposable elements and viral sequences, maintenance <strong>of</strong> chromosomal<br />
integrity, X chromosome inactivation, and transcriptional regulation <strong>of</strong> genes<br />
(1). Methylation is established and maintained by at least four DNA methyltransferases<br />
(DNMTs), DNMT1, DNMT3A, DNMT3B, and DNMT3L, which<br />
catalyze the covalent addition <strong>of</strong> methyl groups to cytosine in the CpG dinucleotide.<br />
Methylation <strong>of</strong> CpG islands causes transcriptional inactivation (Fig. 1).<br />
These methyltransferases and their is<strong>of</strong>orms have been demonstrated to be<br />
essential for development; a knockout <strong>of</strong> any <strong>of</strong> these enzymes in mouse<br />
embryos results in embryonic or perinatal lethality (1). Functionally, there are<br />
two types <strong>of</strong> methyltransferases, maintenance and de novo. Maintenance methyltransferases<br />
are essential for copying DNA methylation patterns during cell<br />
division, and de novo methyltransferases introduce new patterns <strong>of</strong> methylation<br />
during early development. There are duplications <strong>of</strong> functions between these<br />
methylases so that perturbation <strong>of</strong> both types may be necessary to change overall<br />
methylation patterns (6). DNMT1 functions as a maintenance methyltransferase<br />
by copying DNA methylation patterns to daughter strands, following replication.<br />
It is the most abundant and catalytically active form. DNMT3A and DNMT3B<br />
act on both unmethylated and hemimethylated DNA. However, they are thought<br />
to have different methylation targets depending on the cell type and stage <strong>of</strong><br />
development (13). In vitro knockouts <strong>of</strong> DNMT3a or DNMT3b each exhibit<br />
distinct phenotypes. DNMT3a-null cells lack methylation at imprinting loci,<br />
while DNMT3b-null cells result in loss <strong>of</strong> DNA methylation on endogenous
The Role <strong>of</strong> Epigenetics in <strong>Breast</strong> <strong>Cancer</strong> 47<br />
Figure 2 Histone modification and chromatin remodeling. Histone modifications, like<br />
acetylation, change the affinity <strong>of</strong> the histones to DNA. The acetylated histones on the left<br />
are in an open euchromatin structure and can be transcribed. The unacetylated histones on<br />
the right are in a heterochromatin structure and repress transcription. Source: Cartoon<br />
compliments <strong>of</strong> Kristi Bennett.<br />
C-type retroviral DNA. Deletion <strong>of</strong> both DNMT3a and DNMT3b abolishes de<br />
novo methylation, while DNMT1 deletion produces bulk DNA demethylation<br />
(7). Of the methyltransferases, DNMT3L is unique. It stimulates de novo<br />
methylation by DNMT3a, does not enhance its binding to DNA, and alone does<br />
not bind to DNA (14).<br />
Along with methylation, posttranslational modification <strong>of</strong> histones plays<br />
an important role in epigenetics. Histone proteins associate with DNA to form<br />
nucleosomes, permitting large amounts <strong>of</strong> DNA to be neatly packaged into the<br />
nucleus. There are two configurations <strong>of</strong> chromatin, heterochromatin and<br />
euchromatin. Heterochromatin is condensed and transcriptionally inactive, while<br />
euchromatin has an “open” configuration and is favorable for gene transcription<br />
(Fig. 2). The N-terminal tails <strong>of</strong> histones “stick out” <strong>of</strong> the nucleosome and are<br />
subject to posttranslational modification such as acetylation, phosphorylation,<br />
methylation, ubiquitination, and sumoylation (2,4,7,15). The pattern <strong>of</strong> histone<br />
modification creates a “code,” which is read by proteins involved in chromatin<br />
remodeling and the dynamics <strong>of</strong> gene transcription (6,11). Acetylation and<br />
deacteylation are the most common types <strong>of</strong> histone modification. Histone<br />
deacetylases (HDACs) are a class <strong>of</strong> enzymes that remove acetyl groups from a<br />
e-N-acetyl lysine amino acid on a histone. Its action is opposite to that <strong>of</strong> histone<br />
acetyltransferases (HATs). Deacetylation removes acetyl groups from histone<br />
tails, causing the histones to wrap more tightly around the DNA and interfering<br />
with transcription by blocking access to transcription factors. The overall result<br />
<strong>of</strong> histone deacetylation is a global (nonspecific) reduction in gene expression<br />
(Table 1).<br />
METHYLATION IN CANCER<br />
<strong>Cancer</strong> development is complex due to progressive changes <strong>of</strong> the genome that<br />
result in altering normal cellular processes such as cell growth, apoptosis, and<br />
angiogenesis (12). Both genetic and epigenetic modifications play a strong role<br />
in tumorigenesis (7). In classic tumor biology, cells lose both copies <strong>of</strong> tumor
48 Dworkin et al.<br />
Table 1<br />
Histone Modifiers<br />
Histone modifier Mechanism Catalysis Role in transcription<br />
Acetylation<br />
Deacetylation<br />
<strong>Ph</strong>osphorylation<br />
Dephosphorylation<br />
Methylation<br />
Demethylation<br />
Ubiquitination<br />
Deubiquitination<br />
Sumoylation<br />
Introduction <strong>of</strong> the<br />
acetyl group to<br />
the lysine residue<br />
at the N terminus<br />
<strong>of</strong> the histone<br />
protein<br />
Removal <strong>of</strong> the<br />
acetyl group<br />
from the lysine<br />
residue at the N<br />
terminus <strong>of</strong> the<br />
histone protein<br />
Addition <strong>of</strong><br />
phosphate group<br />
Removal <strong>of</strong><br />
phosphate group<br />
Addition <strong>of</strong> 1, 2, or<br />
3 methyl groups<br />
on either lysine<br />
or arginine<br />
residues<br />
Removal <strong>of</strong> methyl<br />
groups<br />
Covalent<br />
attachment<br />
<strong>of</strong> ubiquitin<br />
Removal <strong>of</strong><br />
ubiquitin<br />
Covalent<br />
attachment<br />
<strong>of</strong> SUMO to<br />
lysine residues<br />
HATs<br />
HDACs<br />
Histone kinases<br />
Histone<br />
phosphatases<br />
HMTs<br />
Cytochrome P450<br />
family <strong>of</strong> liver<br />
enzymes<br />
Enzymatic cascade,<br />
including<br />
ubiquitin ligase,<br />
E1 ubiquitin<br />
activating<br />
enzyme, and<br />
ATP<br />
Isopeptidases<br />
Enzymatic cascade,<br />
including SUMOactivating<br />
enzyme<br />
and ATP<br />
Enhances transcription<br />
(removes positive<br />
charge and reduces<br />
affinity between<br />
histones and DNA)<br />
Represses transcription<br />
Enhances transcription<br />
(neutralizes histones’<br />
basic charge and<br />
reduces affinity to<br />
DNA)<br />
Represses transcription<br />
Silences genes<br />
Activates genes<br />
Labels protein for<br />
degradation<br />
Reverses<br />
ubiquitination<br />
Can mediate gene<br />
silencing through<br />
recruitment <strong>of</strong> HDAC<br />
and heterochromatin<br />
protein 1<br />
Abbreviations: HATs, histone acetyltransferases; HDACs, histone deacetylases; HMT, histone<br />
methyltranferases; SUMO, small ubiquitin-like modifier; ATP, adenosine triphosphate.
The Role <strong>of</strong> Epigenetics in <strong>Breast</strong> <strong>Cancer</strong> 49<br />
suppressor genes through mutation or deletion. Epigenetic modifications, such<br />
as DNA methylation, serve a similar role to somatic mutations, leading to<br />
gene silencing. Overall, tumors tend to exhibit hypomethylation <strong>of</strong> repeat<br />
elements and pericentromeric regions and hypermethylation <strong>of</strong> normally<br />
unmethylated CpG islands. Gene-specific hypermethylation <strong>of</strong> CpG islands is<br />
associated with chromosomal instability, resulting in increased mutation rates<br />
and abnormal gene expression. For example, hypermethylation <strong>of</strong> critical<br />
genes, such as tumor suppressors, adhesion molecules, DNA repair genes,<br />
inhibitors <strong>of</strong> angiogenesis, and inhibitors <strong>of</strong> metastasis, is observed in<br />
tumorigenesis (7). Hypermethylation <strong>of</strong> DNA repair genes (e.g., hMLH1) is<br />
associated with genomic instability through disruption <strong>of</strong> chromosome replication<br />
control (7). Unlike gene mutations, DNA methylation can be reversed<br />
by DNMT inhibitors.<br />
An important component <strong>of</strong> the cancer epigenome is an altered DNA<br />
methylation pattern <strong>of</strong> global demethylation and localized promoter hypermethylation.<br />
Aberrant methylation patterns are seen in almost every cancer type<br />
(7,16). In addition to aberrant methylation, tumors also show altered expression<br />
<strong>of</strong> HATs, making them another potential target for therapy (11). It is believed<br />
that histone modifications precede promoter hypermethylation to initiate transcriptional<br />
silencing <strong>of</strong> tumor suppressor genes. Repressive histone modifications<br />
are first established. Over time the density <strong>of</strong> DNA methylation<br />
increases in the gene promoter regions, which creates a “permanent” mode <strong>of</strong><br />
silencing (17).<br />
THE ROLE OF EPIGENETICS IN BREAST CANCER<br />
In the United States, breast cancer is one <strong>of</strong> the most common cancers, with<br />
approximately 200,000 women diagnosed with breast cancer each year (http://<br />
www.cancer.org). In addition to well-understood DNA mutations in breast<br />
tumors, epigenetic alterations <strong>of</strong> gene expression are key contributors (16,18–24).<br />
A large number <strong>of</strong> genes are found to be hypermethylated in breast tumors but<br />
not in normal breast tissue. Many <strong>of</strong> these genes have well-characterized functions<br />
in tumor development. Genes commonly methylated in breast cancer and<br />
their potential roles in tumor development are listed in Table 2. Four key genes,<br />
RASSF1A, BRCA1, ESR1, and ESR2, are further discussed in detail.<br />
RASSF1A<br />
The CpG island <strong>of</strong> the Ras-associated domain family 1A gene (RASSF1) is<br />
hypermethylated in 60% to 77% <strong>of</strong> breast cancers, and its transcripts are frequently<br />
inactivated in cancer cell lines and primary tissues (13,22). When cells<br />
lacking RASSF1A expression are treated with a DNMT inhibitor, such as 5-aza-<br />
2 0 -deoxycytidine, expression can be reactivated (25). RASSF1A is involved in
50 Dworkin et al.<br />
Table 2<br />
Genes Methylated in <strong>Breast</strong> <strong>Cancer</strong><br />
Gene <strong>Cancer</strong> hallmark contribution Reference<br />
RASSF1A Apoptosis 15<br />
CYP26A1 Microenvironment, encodes for cytochrome p450 enzymes 17<br />
KCNAB1 Microenvironment 17<br />
SNCA Microenvironment 17<br />
RARb Growth regulation 15<br />
TWIST Cell lineage and differentiation 15<br />
Cyclin D2 Cell cycle regulation 15<br />
APC Cell cycle regulation and apoptosis 8<br />
BRCA1 Cell cycle regulation 8,15<br />
BRCA2 Tumor suppressor gene 15<br />
p16 Cell cycle regulation 8,15<br />
DAPK Apoptosis 8<br />
GSTP1 Cellular metabolism and carcinogen detoxification 8<br />
TMS1 Apoptosis 8<br />
PR Growth regulation 8<br />
CDH1 Angiogenesis 8<br />
WWOX Apoptosis 38<br />
growth regulation and apoptotic pathways. Specifically, RASSF1A is a Ras<br />
effector and induces apoptosis through its interactions with pro-apoptotic kinase<br />
MST1. Mouse knockout studies show that RASSF1a –/– mice are prone to<br />
spontaneous development <strong>of</strong> lung adenomas, lymphomas, and breast adenocarcinomas.<br />
Knockout RASSF1A mice are prone to early spontaneous tumorigenesis<br />
and show a severe tumor susceptibility phenotype compared to wild-type<br />
mice (25). In addition to breast tumors, hypermethylation <strong>of</strong> RASSF1A can be<br />
detected in nonmalignant breast cells and patient sera. In one study, RASSF1A<br />
hypermethylation in sera <strong>of</strong> breast cancer patients was detected in six out <strong>of</strong> 26<br />
cases. These data suggest that RASSF1A hypermethylation may be a promising<br />
biomarker to screen putative cancer patients (25).<br />
BRCA1<br />
Germline mutations <strong>of</strong> BRCA1 and BRCA2 are responsible for familial breast<br />
cancers. Somatic mutations in sporadic cases are rare, but chromosomal losses<br />
occur in 30% to 50% <strong>of</strong> sporadic tumors, respectively (26). BRCA1 acts as a<br />
tumor suppressor gene for both breast and ovarian cancer (16). It encodes a<br />
multifunctional protein involved in DNA repair, cell cycle checkpoint control,<br />
protein ubiquitinylation, and chromatin remodeling (19). In vitro studies showed<br />
that decreased BRCA1 expression in cells led to increased levels <strong>of</strong> tumor<br />
growth, while increased expression <strong>of</strong> BRCA1 led to growth arrest and apoptosis.<br />
Inactivation <strong>of</strong> BRCA1 by promoter methylation is seen in 7% to 31% <strong>of</strong>
The Role <strong>of</strong> Epigenetics in <strong>Breast</strong> <strong>Cancer</strong> 51<br />
sporadic breast cancer cases (27). It is believed that aberrant methylation pairs<br />
with loss <strong>of</strong> heterozygosity to reduce BRCA1 expression in invasive sporadic<br />
breast tumors (16,19,22,26,28). The magnitude <strong>of</strong> the decrease <strong>of</strong> functional<br />
BRCA1 protein correlates with disease prognosis (19,22). <strong>Ph</strong>enotypically,<br />
BRCA1-methylated tumors are similar to tumors with germline mutations.<br />
Higher-grade tumors tend to show complete loss <strong>of</strong> BRCA1 protein. In contrast to<br />
BRCA1, BRCA2 does not show as high a degree <strong>of</strong> promoter methylation (16).<br />
ESR1 and ESR2<br />
Along with other genes, the estrogen receptors (ERs) ERa and ERb have been<br />
implicated in breast cancer development. ERa is encoded by ESR1, and when<br />
estrogen is activated, it stimulates cell proliferation. ERb is encoded by ESR2<br />
and is known to inhibit the proliferation and invasion <strong>of</strong> breast cancer cells. ERa<br />
and ERb both have promoter-associated CpG islands that can be abnormally<br />
methylated in breast cancer, but ESR2 methylation has been less well studied<br />
(16,18). Almost all breast cancers show some degree <strong>of</strong> DNA methylation <strong>of</strong> the<br />
ESR1 gene promoter, but this methylation is only associated with gene repression<br />
in ~30% <strong>of</strong> tumors (16,18). Approximately 66% <strong>of</strong> breast cancers express<br />
ERa. A fraction <strong>of</strong> breast cancers that are initially ERa positive lose ER<br />
expression during tumor progression, but it is unclear if this is due to methylation<br />
or other causes (18).<br />
The presence or absence <strong>of</strong> ERs plays an important role in both therapy<br />
and survival (16,18). ER-positive breast cancer can be treated with antiestrogenic<br />
drugs, such as tamoxifen, which binds to the ER, preventing estrogen from<br />
binding. This results in a decrease in growth <strong>of</strong> ER-positive breast cancer cells.<br />
ER-negative cells are no longer responsive to estrogen; therefore, antiestrogenic<br />
drugs have no effect (18). Reversal <strong>of</strong> ER-negative status is an attractive target<br />
for breast cancer therapy.<br />
BIOMARKERS AND DETECTION<br />
Gene-specific epigenetic changes for breast cancer are likely to occur early in<br />
tumorigenesis and have potential to be used as early detection and prevention<br />
methods (2). As epigenetic modifications become clear biomarkers for breast<br />
cancer treatment and therapeutic intervention, it is important to understand the<br />
many different techniques available for studying and detecting the presence <strong>of</strong><br />
methylation, histone modifications, and microRNAs (11).<br />
New, promising high-throughput methylation detection methods are<br />
available, which enable researchers and clinicians to identify an “epigenetic<br />
signature” specific to breast cancer. Biomarkers based on unique DNA methylation<br />
and histone modification patterns <strong>of</strong> breast tumors may be used in the<br />
future for diagnosis and early detection. Currently, breast cancer detection relies<br />
on various screening methods. Cytology is the main standard for identification
52 Dworkin et al.<br />
<strong>of</strong> abnormalities typical <strong>of</strong> cellular transformation. Quantitative multiplex<br />
methylation-specific polymerase chain reaction (PCR) (QM-MSP) is a highly<br />
sensitive method to quantitate cumulative gene promoter hypermethylation in<br />
samples where DNA is limiting. First, gene-specific primer pairs are used to<br />
coamplify genes, independent <strong>of</strong> their methylation status. Next, gene-specific<br />
primers (methylated and unmethylated) conjugated with fluorescent labels are<br />
combined with the first PCR product. The signal generated is proportional to the<br />
extent <strong>of</strong> DNA amplification (15). Since it is known that multigene methylation<br />
<strong>of</strong> CpG islands is common in early breast cancer, a panel <strong>of</strong> nine genes were<br />
tested for their sensitivity and specificity <strong>of</strong> detecting premalignant changes<br />
using QM-MSP. These genes included RASSF1A, RARb, TWIST, HIN1, Cyclin<br />
D2, APC, BRCA1, BRCA2, and p16. Tests were done using ductal lavage, nipple<br />
aspiration fluids, and fine needle aspirates.<br />
Using this detection panel, the rate <strong>of</strong> detection <strong>of</strong> breast cancer cells rises<br />
from 43% sensitivity with cytologic examination alone to 71.4% sensitivity (15).<br />
Hypermethylation <strong>of</strong> genes that are commonly methylated in breast cancer in<br />
sera in breast cancer patients has also been used to detect early malignant<br />
changes. Sera methylation can be detected using methylation-specific PCR (MS-<br />
PCR), a noninvasive PCR-based technique. Early studies indicate that MS-PCR<br />
is a promising approach to screen putative cancer patients (25). This technique<br />
utilizes primers designed for methylated or unmethylated bisulfite-modified<br />
DNA (29). RASSF1A gene hypermethylation has been detected using MS-PCR<br />
in sera <strong>of</strong> ovarian cancer patients, with 100% specificity (25).<br />
Recurrence rates for breast cancer in the same breast have been recorded as<br />
high as 40% despite negative pathological margins. Researchers believe that a<br />
primary tumor may serve as a locus from which methylation density progressively<br />
diffuses outward to surrounding tissues. CYP26A1, KCNAB1, RASSF1A,<br />
and SNCA have found to be frequently methylated in adjacent normal tissue. A<br />
methylation pr<strong>of</strong>ile containing genes such as these, or those identified in other<br />
studies (Table 2), could be used by clinicians to determine whether patients<br />
should undergo radiological therapy or other treatments to prevent future<br />
recurrence (17).<br />
MassARRAY and pyrosequencing, two additional methods <strong>of</strong> detecting<br />
methylation, are currently being developed for use in clinical settings.<br />
MassARRAY is a highly sensitive technique that uses base-specific cleavage and<br />
matrix-assisted laser desorption/ionization time-to-flight mass spectrometry<br />
(MALDI-TOF MS). It is capable <strong>of</strong> detecting DNA methylation levels as low as<br />
5%. It is suitable for testing methylation patterns on various sources, including<br />
archival tissues and laser capture microdissected specimens (1). Pyrosequencing<br />
is a locus-specific quantitative method that utilizes the detection <strong>of</strong> pyrophosphate,<br />
which is liberated from incorporated nucleotides by DNA polymerase<br />
during strand elongation. Free pyrophosphates are converted to adenosine triphosphate<br />
(ATP), which provides energy for the oxidation <strong>of</strong> luciferin to then<br />
generate light. Nucleotides are added sequentially to enable base calling.
The Role <strong>of</strong> Epigenetics in <strong>Breast</strong> <strong>Cancer</strong> 53<br />
Pyrosequencing has two major advantages over MS-PCR. First, the data are<br />
actual nucleotide sequences rather than fluorescence data. Second, pyrosequencing<br />
can detect partially methylated sequences that are outside <strong>of</strong> the priming<br />
sites (30).<br />
Commonly used methods for detecting histone modifications usually rely<br />
on chromatin immunoprecipitation (ChIP). In ChiP, DNA is cross-linked to<br />
chromatin, followed by co-immunoprecipitation using antibodies against specific<br />
histone residues or chromatin proteins. “ChIP” DNA can be used in microarrays,<br />
cloning, and sequencing (11). Techniques for detecting histone modifications<br />
have not yet been adapted for clinical application.<br />
CLINICAL APPLICATION<br />
<strong>Breast</strong> cancer prevention, treatments, and diagnostics are being developed to<br />
target epigenetic changes leading to breast cancer. Treatments for breast cancer<br />
currently being studied extensively focus on reversing aberrant DNA methylation<br />
and histone acetylation <strong>of</strong> tumor suppressor genes and genes involved in<br />
therapeutic response. Combinations <strong>of</strong> epigenetic-targeted therapies with conventional<br />
chemotherapeutic agents may provide a way to resensitize drugresistant<br />
tumors (Table 3).<br />
Methylation Therapy<br />
DNA methylation in proliferating cells is dependent on continued expression <strong>of</strong><br />
DNMTs. Treatments that focus on inhibiting the expression <strong>of</strong> these proteins<br />
would therefore result in progressive reduction in DNA methylation in newly<br />
divided cells. There are three main classes <strong>of</strong> DNA methylation inhibitors:<br />
nucleoside inhibitors, nonnucleoside weak inhibitors, and rationally designed<br />
inhibitors. Nucleoside inhibitors trap DNMTs for degradation. However, they<br />
require DNA incorporation and active DNA synthesis, which limits the drug in<br />
hypoproliferating cells. 5-Azacytidine (5-aza-dC) and its deoxy analog are two<br />
<strong>of</strong> the first hypomethylating agents approved by the U.S. Food and Drug<br />
Administration for cancer treatment (31). They work by two mechanisms: (i) as<br />
demethylators by incorporating into CpG sites opposite a methylated CpG site on<br />
the template strand and (ii) by binding to the cytosine in the catalytic domain <strong>of</strong><br />
the DNMT1 enzyme. Within a few hours <strong>of</strong> treatment with 5-aza-dC, there is<br />
rapid passive loss <strong>of</strong> methylation (32). Nonnucleoside inhibitors are not as well<br />
understood and are limited by their low level <strong>of</strong> hypomethylation induction.<br />
Nonnucleoside inhibitors were first discovered for their use in reducing autoreactivity<br />
for autoimmune diseases. During this study researchers discovered their<br />
potential as demethylating agents. Researchers are currently using clinically<br />
approved nonnucleoside inhibitors as a starting point to study their use as<br />
demethylating agents. Rationally designed inhibitors are also in development.<br />
These drugs are designed on the basis <strong>of</strong> knowledge <strong>of</strong> specific chemical
54 Dworkin et al.<br />
Table 3<br />
Drugs in Use for <strong>Breast</strong> <strong>Cancer</strong> Treatment<br />
Nucleoside inhibitors<br />
Drug Use Research level Reference<br />
5-Azacytidine Trap DNMTs for degradation Clinical use 31,40<br />
5-aza-<br />
Trap DNMTs for degradation Clinical use 31<br />
2 0 deoxycytidine<br />
Zebularine Unknown mechanism Shown promise in vitro 31<br />
5 0 -fluoro-<br />
2 0 deoxycytidine<br />
Unknown mechanism In clinical trials 31<br />
Nonnucleoside inhibitors<br />
Drug Use Research level Reference<br />
Procainamide<br />
Hydralazine<br />
Mechanism not well<br />
understood but proven to<br />
decrease expression <strong>of</strong><br />
DNMT1 and DNMT3a<br />
Mechanism not well<br />
understood but proven to<br />
decrease expression <strong>of</strong><br />
DNMT1 and DNMT3a<br />
Rationally designed inhibitors<br />
Clinically used as<br />
an antihypertensive<br />
drug<br />
Clinically used as a<br />
vasodilator<br />
31,41,42<br />
31,41,42<br />
Drug Use Research level Reference<br />
NSC303530 Blocks the active site <strong>of</strong> DNMTs Basic research 31,43<br />
NSC401077 Blocks the active site <strong>of</strong> DNMTs Basic research 31,43<br />
HDAC inhibitors<br />
Drug Use Advantages Disadvantages<br />
Research<br />
level<br />
Reference<br />
LAQ824<br />
Magnesium<br />
Valporate<br />
and<br />
Hydralazine<br />
HDAC<br />
inhibitor<br />
N-hydroxy-2- HDAC<br />
propenamide inhibitor<br />
Unknown<br />
HDAC and Increases<br />
methyltransferase<br />
conventional<br />
efficacy <strong>of</strong><br />
inhibitor cytotoxic<br />
agents<br />
Suppresses<br />
mammary<br />
tumor growth<br />
in cells<br />
Important to<br />
pay attention<br />
to schedule <strong>of</strong><br />
administration<br />
Unknown<br />
Unknown<br />
Preclinical<br />
level; studies<br />
on hematological<br />
malignancies<br />
<strong>Ph</strong>ase III<br />
clinical<br />
trials<br />
Research<br />
level<br />
40<br />
44<br />
l45<br />
Abbreviations: DNMTs, DNA methyltransferases; HDAC, histone deacetylase.
The Role <strong>of</strong> Epigenetics in <strong>Breast</strong> <strong>Cancer</strong> 55<br />
Figure 3 Drug impact on histone modifications. HDAC modifies histones and leads to<br />
chromatin remodeling so that genes are transcriptionally inactive. HAT can reverse the<br />
effects <strong>of</strong> HDAC to acetylate histones and open the confirmation into an active state.<br />
Drugs such as Scriptaid, TSA, LAQ824, and N-hydroxy-2-propenamide can act on this<br />
pathway to block HDACs and keep DNA transcriptionally active. Abbreviations: HDAC,<br />
histone deactylase; HAT, histone acetyltransferase; TSA, trichostatin A. Source: Cartoon<br />
compliments <strong>of</strong> Kristi Bennett.<br />
responses in the body, tailoring combinations <strong>of</strong> these to fit a treatment pr<strong>of</strong>ile.<br />
An example <strong>of</strong> a rational design involves the use <strong>of</strong> three-dimensional information<br />
to target the drug. Limitations <strong>of</strong> DNA methylation inhibitors include the<br />
cooperation between different DNMTs such that the drugs must inhibit several <strong>of</strong><br />
them simultaneously. All demethylating agents suffer from nonspecificity and<br />
toxicity. The next phase <strong>of</strong> drug development is how to target gene-specific<br />
hypomethylation (e.g., unmethylated oligonucleotides) (31).<br />
Histone Modification Therapy<br />
Histone deacetylases (HDACs) are also targets for therapy (Fig. 3). HDACs are<br />
important because <strong>of</strong> their role in cell cycle arrest and inhibition <strong>of</strong> apoptosis.<br />
These drugs have been studied in isolation and synergistically with DNMTs.<br />
Therapies that target both DNMTs and HDACs act globally, which can cause<br />
reactivation <strong>of</strong> other genes that are typically silenced, such as oncogenes (18,33–37).<br />
ER Therapy<br />
Drugs are in development for different types <strong>of</strong> breast cancer. A therapy that could<br />
restore gene expression <strong>of</strong> ERS1 (for ER-negative cancer patients) could reestablish<br />
cancer cell growth regulation through estrogen. After reexpression <strong>of</strong><br />
ERS1, antiestrogenic drugs could subsequently be used. A number <strong>of</strong> drugs,<br />
including DNMT inhibitors and HDAC inhibitors, are being used to reactivate ER<br />
expression (Table 4). DNMTs and HDACs work synergistically to silence gene<br />
expression <strong>of</strong> ER. One problem found in treatments that target DNMTs and HDACs<br />
is that alone they may not be enough to reverse ER-promoter hypermethylation.
56 Dworkin et al.<br />
Table 4<br />
Drugs Involved in Reactivation <strong>of</strong> ER Expression<br />
Treatment Result Reference<br />
5-aza-Dc<br />
(methyltransferase<br />
inhibitor)<br />
Antisense oligonucleotides<br />
(for DNMT1)<br />
Partial demethylation <strong>of</strong> ER CpG islands,<br />
reexpression <strong>of</strong> ER mRNA, and synthesis<br />
<strong>of</strong> functional ER protein<br />
ER gene reexpression and estrogen<br />
responsiveness reestablished<br />
18,36,37,46<br />
18,35<br />
TSA (HDAC inhibitor) Reexpression <strong>of</strong> ERa protein 18,33<br />
DNMT and HDAC Reactivation ERa protein and expression 18,37<br />
inhibitor<br />
Scriptaid (HDAC inhibitor) Reexpression <strong>of</strong> functional ER in vivo 18,34<br />
Scriptaid and 5-aza-Dc More effective in inducing ER expression<br />
than just Scriptaid or 5-aza-Dc alone<br />
18,34<br />
Abbreviations: ER, estrogen receptor; DNMT, DNA methyltransferase; HDAC, histone deacetylase;<br />
TSA, trichostatin A.<br />
Tamoxifen is a commonly used drug for ERa-positive breast cancers.<br />
Unfortunately, acquired resistance <strong>of</strong> tamoxifen occurs in nearly 40% <strong>of</strong> patients.<br />
Therefore, it is important to identify patients most likely to respond to<br />
Tamoxifen and to identify individuals likely to acquire resistance. One promising<br />
biomarker for Tamoxifen response is WWOX. Preliminary studies show<br />
that WWOX expression levels predict tamoxifen resistance better than the two<br />
previously known biomarkers, PR and HER2 (38). WWOX appears to mediate<br />
tamoxifen sensitivity, and its expression is reduced in a large fraction (63.2%) <strong>of</strong><br />
breast cancers (39). The primary mechanism <strong>of</strong> downregulation <strong>of</strong> WWOX is<br />
through DNA hypermethylation <strong>of</strong> its regulatory region.<br />
FUTURE DIRECTIONS<br />
The next 10 years are likely to bring significant advances for breast cancer<br />
treatment. Therapeutics that target methylation and histone modifications may<br />
play a leading role in treating breast cancer. Since epigenetic modifications can<br />
also be used as biomarkers, targeted therapies may some day be used as preventative<br />
measures.<br />
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with epigenetic biomarkers in normal breast tissue. Clin <strong>Cancer</strong> Res 2006; 12(22):<br />
6626–6636.<br />
18. Giacinti L, Claudio PP, Lopez M, et al. Epigenetic information and estrogen receptor<br />
alpha expression in breast cancer. Oncologist 2006; 11(1):1–8.<br />
19. Mirza S, Sharma G, Prasad CP, et al. Promoter hypermethylation <strong>of</strong> TMS1, BRCA1,<br />
ERalpha and PRB in serum and tumor DNA <strong>of</strong> invasive ductal breast carcinoma<br />
patients. Life Sci 2007; 81(4):280–287.<br />
20. Sharma D, Blum J, Yang X, et al. Release <strong>of</strong> methyl CpG binding proteins and histone<br />
deacetylase 1 from the Estrogen receptor alpha (ER) promoter upon reactivation in<br />
ER-negative human breast cancer cells. Mol Endocrinol 2005; 19(7):1740–1751.<br />
21. Sui M, Huang Y, Park BH, et al. Estrogen receptor alpha mediates breast cancer cell<br />
resistance to paclitaxel through inhibition <strong>of</strong> apoptotic cell death. <strong>Cancer</strong> Res 2007;<br />
67(11):5337–5344.<br />
22. Vincent-Salomon A, Ganem-Elbaz C, Manié E, et al. X inactive-specific transcript<br />
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23. Visvanathan K, Sukumar S, Davidson NE. Epigenetic biomarkers and breast cancer:<br />
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24. Zhou Q, Davidson NE. Silencing estrogen receptor alpha in breast cancer cells.<br />
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25. Pfeifer GP, Dammann R. Methylation <strong>of</strong> the tumor suppressor gene RASSF1A in<br />
human tumors. Biochemistry (Mosc) 2005; 70(5):576–583.<br />
26. Esteller M, Silva JM, Dominguez G, et al. Promoter hypermethylation and BRCA1<br />
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564–569.<br />
27. Wei M, Grushko TA, Dignam J, et al. BRCA1 promoter methylation in sporadic<br />
breast cancer is associated with reduced BRCA1 copy number and chromosome 17<br />
aneusomy. <strong>Cancer</strong> Res 2005; 65(23):10692–10699.<br />
28. Catteau A, Morris JR. BRCA1 methylation: a significant role in tumour development?<br />
Semin <strong>Cancer</strong> Biol 2002; 12(5):359–371.<br />
29. Herman JG, Graff JR, Myöhänen S, et al. Methylation-specific PCR: a novel PCR<br />
assay for methylation status <strong>of</strong> CpG islands. Proc Natl Acad Sci U.S.A. 1996; 93(18):<br />
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30. Shames DS, Minna JD, Gazdar AF. Methods for detecting DNA methylation in<br />
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31. Issa JP. DNA methylation as a therapeutic target in cancer. Clin <strong>Cancer</strong> Res 2007; 13(6):<br />
1634–1637.<br />
32. Christman JK. 5-Azacytidine and 5-aza-2 0 -deoxycytidine as inhibitors <strong>of</strong> DNA<br />
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34. Keen JC, Yan L, Mack KM, et al. A novel histone deacetylase inhibitor, scriptaid,<br />
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human breast cancer cells in combination with 5-aza 2 0 -deoxycytidine. <strong>Breast</strong> <strong>Cancer</strong><br />
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35. Yan L, Nass SJ, Smith D, et al. Specific inhibition <strong>of</strong> DNMT1 by antisense oligonucleotides<br />
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5<br />
Comparative Genomic Hybridization<br />
and Copy Number Abnormalities in<br />
<strong>Breast</strong> <strong>Cancer</strong><br />
Nicholas Wang and Joe Gray<br />
Life Sciences Division, Lawrence Berkeley National Laboratory,<br />
Berkeley, California, U.S.A.<br />
INTRODUCTION<br />
The progression <strong>of</strong> normal breast epithelial cells from a normal state toward one<br />
characterized by uncontrolled growth and metastatic behavior is caused by the<br />
deregulation <strong>of</strong> key cellular processes and signaling pathways. These alterations<br />
in normal cellular behavior are rooted in the accumulation <strong>of</strong> genomic and<br />
epigenomic lesions that impact hallmarks <strong>of</strong> cancer, such as the ability <strong>of</strong> the cell<br />
to control proliferation, undergo apoptosis, increase motility leading to invasion,<br />
and alter angiogenesis. A suite <strong>of</strong> technologies has now been developed to assess<br />
genomic and epigenomic aberrations that contribute to cancer progression. The<br />
application <strong>of</strong> these has shown that genome copy number abnormalities (CNAs)<br />
are among the most frequent genomic aberrations. Remarkably, these studies<br />
have revealed that 10% to 15% <strong>of</strong> the genes in a typical carcinoma tumor may be<br />
deregulated by recurrent genome CNAs and regions. Some <strong>of</strong> these genes<br />
influence disease progress and so may be assessed to facilitate prognosis. Others<br />
influence response to therapy and so may be assessed as predictive markers.<br />
Some <strong>of</strong> these enable oncogenic processes, on which tumors depend for survival,<br />
and so are candidate therapeutic targets. In most cases, array comparative<br />
61
62 Wang and Gray<br />
genomic hybridization (CGH) is the method <strong>of</strong> choice for their discovery and<br />
may be used in some setting for clinical assessments <strong>of</strong> these abnormalities.<br />
Accordingly, we review here several <strong>of</strong> the current array CGH technologies<br />
available and the considerations needed when determining the technology most<br />
applicable to a given study.<br />
CGH analyses <strong>of</strong> thousands <strong>of</strong> primary breast tumors and cell lines have<br />
now defined recurrent CNAs throughout the genome and identified genes<br />
encoded in these aberrant regions that are deregulated by the abnormalities (1,2–4).<br />
In breast cancer, for example, 10% to 15% <strong>of</strong> known genes are deregulated by<br />
these CNAs (2–4). The aberrations tend to fall into two general classes that may<br />
contribute in different ways to cancer pathophysiology: low-level CNAs that<br />
occur through the gain or loss <strong>of</strong> extended regions <strong>of</strong> single chromosomes or<br />
extended regions there<strong>of</strong> and high-level amplification or homozygous deletions<br />
that tend to be narrower in genomic extent.<br />
Most genes are deregulated by low-level aberrations. The contributions <strong>of</strong><br />
these genes to cancer pathophysiology are not well understood; however, evidence<br />
is now emerging that these may provide, en ensemble, a general metabolic<br />
advantage (4–6). These aberrations typically occur relatively early in the carcinogenesis<br />
process. It has been proposed that these aberrations are selected during<br />
successful transition through a telomere crisis (7).<br />
High-level amplification and narrow homozygous deletions provide strong<br />
evidence <strong>of</strong> active selection during cancer progression and seem to occur later<br />
during cancer progression. In breast cancer, regions <strong>of</strong> high-level amplification<br />
encode well-studied oncogenes, such as ERBB2 (17q12) (8), FGFR1 (8p11) (9),<br />
PVT1 and MYC (8q24) (10,11), CCND1 and EMS1 (11q13) (12), and ABI1 and<br />
ZNF217 (20q13) (13,14), while deletions contribute to the inactivation <strong>of</strong> tumor<br />
suppressor genes such as TP53 (17p13) (15), CDKN2A (9p21) (16), and BRCA1<br />
(13q14) (17). While it is <strong>of</strong>ten assumed that these oncogenes and tumor suppressor<br />
genes are the sole targets for these aberrations, a growing body <strong>of</strong><br />
functional data suggests that several genes in each region <strong>of</strong> recurrent abnormality<br />
may be coselected by the event. In breast cancer, for example, regions <strong>of</strong><br />
amplification in which more than one gene appears to be functionally important<br />
include 8p11, 8q24, 11q13, 17q21, and 20q13 (4). Table 1 summarizes published<br />
studies for genes in these regions <strong>of</strong> amplification. The concept <strong>of</strong> multigene<br />
selection is also supported by studies in mouse models, where the extents <strong>of</strong><br />
amplicons in mice and humans are similar. Similarities in the extent <strong>of</strong> amplification<br />
around ERBB2 in humans and in polyomavirus middle T-induced<br />
mammary tumors in mice are particularly striking (18). Similar studies are<br />
emerging at other organ sites (19). As a result, it is critically important that the<br />
extent <strong>of</strong> each region be defined as precisely as possible. High-resolution CGH<br />
analyses are now contributing to this process.<br />
Several recurrent high-level CNAs are associated with clinical endpoints,<br />
including reduced survival duration and response to aberration-targeted therapies.<br />
Assays for recurrent aberrations or genes deregulated by CNA abnormalities have
Comparative Genomic Hybridization in <strong>Breast</strong> <strong>Cancer</strong> 63<br />
Table 1<br />
Array CGH Analysis Platform Summary<br />
Platform<br />
composition Reference Density<br />
Allele<br />
specific<br />
Source<br />
cDNA 5 40K No Academic<br />
BAC 68 25K No Academic/Spectral<br />
Genomics<br />
Oligonucleotide http://affymetrix.com 950K Yes Affymetrix<br />
Oligonucleotide http://www.<br />
400K No Nimblegen<br />
nimblegen.com<br />
Oligonucleotide http://www.chem. 240K No Agilent<br />
agilent.com<br />
Oligonucleotide http://www.illumina. 1M Yes Illumina<br />
com<br />
MIP 41 20K Yes Academic<br />
Abbreviations: CGH, comparative genomic hybridization; BAC, bacterial artificial chromosome;<br />
MIP, molecular inversion probe.<br />
already been incorporated into molecular indicators <strong>of</strong> outcome. In addition,<br />
several therapeutic agents are now being developed to attack genes deregulated<br />
fully or in part by recurrent genome abnormalities so that assays for these targets<br />
may be useful as predictors <strong>of</strong> response. In breast cancer, the most prominent<br />
examples are trastuzumab (20,21) and lapatinib (22) (NEJM PMID 17192538),<br />
developed to attack tumors driven by amplification <strong>of</strong> ERBB2. In addition,<br />
therapies have been developed to treat tumors arising from deletion or inactivation<br />
<strong>of</strong> TP53 (23) and activation <strong>of</strong> elements <strong>of</strong> the PI3-kinase pathway, including<br />
PIK3CA, PTEN, AKT, and ZNF217 (24–27).<br />
The ability to detect and define the extents <strong>of</strong> CNAs has steadily improved<br />
with the evolution <strong>of</strong> CGH analysis technologies (Table 2). The first CGH<br />
studies mapped CNAs onto metaphase chromosomes, but the resolution <strong>of</strong> such<br />
analyses was limited to about 10 Mbp by the complex packaging <strong>of</strong> the DNA<br />
into chromosomes (28). The introduction <strong>of</strong> array CGH in which CNAs were<br />
mapped onto arrays <strong>of</strong> cloned probes substantially increased resolution. Early<br />
arrays comprised hundreds to thousands <strong>of</strong> probes and improved resolution to<br />
about 1 Mbp (29,30). Current cloned probe arrays interrogate the genome at<br />
approximately single-gene resolution (5). Today, several commercial platforms<br />
map abnormalities onto arrays <strong>of</strong> oligonucleotide probes with subgene resolution<br />
(either on flat substrates or attached to microbeads) (31), and some allow<br />
analyses in an allele-specific manner (32). Applications <strong>of</strong> these platforms are<br />
further refining the extents <strong>of</strong> recurrent genomic aberrations but also reveal the<br />
somewhat surprising existence <strong>of</strong> germline copy number polymorphisms that<br />
must be taken into account when interpreting CGH measurements. Several<br />
aspects <strong>of</strong> these important array CGH analysis technologies are reviewed in the<br />
following sections.
64 Wang and Gray<br />
Table 2<br />
Data Analysis Algorithms<br />
Platform Program Reference Type<br />
Required<br />
s<strong>of</strong>tware<br />
Two-color arrays aCGH 54 HMM R<br />
GLAD 56 Adaptive weight R<br />
smoothing<br />
CLAC 57 Clustering along R<br />
chromosomes<br />
DNAcopy 55 Segmentation R<br />
Single-color arrays dChipSNP 37 HMM none<br />
(Affymetrix)<br />
CARAT 69 none<br />
CNAG 70 Regression none<br />
GEMCA 71 Regression none<br />
Abbreviations: aCGH, array comparative genomic hybridization; HMM, hidden Markov model;<br />
GLAD, Gaussian model–based approach; CART, copy number analysis with regression and tree; CLAC,<br />
clustering along each chromosome; SNP, single nucleotide polymorphism; CNAG, Copy Number<br />
Analyzer for GeneChip; GEMCA, Genotyping microarray based copy number variation analysis.<br />
COMPARATIVE GENOMIC HYBRIDIZATION<br />
CGH is the primary technology used for assessment <strong>of</strong> genome copy number.<br />
Typically, analysis proceeds by hybridizing differentially labeled tumor and normal<br />
DNA to a representation <strong>of</strong> the normal genome, although some platforms<br />
perform tumor and normal reference hybridization in separate representations. The<br />
ratio <strong>of</strong> tumor label to normal label, hybridized to each locus on the representation<br />
is then measured as an estimate <strong>of</strong> relative copy number at that locus.<br />
The first CGH experiments employed metaphase chromosomes as the<br />
representation onto which information was mapped (28,33). While useful, the<br />
genomic resolution was limited by the nonlinear organization <strong>of</strong> DNA along<br />
metaphase chromosomes. Thus, chromosome CGH has now been largely supplanted<br />
by array CGH, in which information is mapped onto arrays <strong>of</strong> DNA<br />
probes (Fig. 1). The initial array CGH platforms comprised large genomic cloned<br />
probes, such as yeast artificial chromosomes (YACs) (34), bacterial artificial<br />
chromosomes (BACs), and P1-derived artificial chromosomes (PACs) (29), or<br />
shorter cDNA sequences spotted on glass slides (35). More recently, synthetic<br />
oligonucleotide probes have been used successfully (36–39). Several platforms<br />
are summarized in Table 1 and in the following sections.<br />
Cloned probe arrays are typically available from large academic cores with<br />
the experience and resources to maintain and print the arrays. These arrays are<br />
developed using the large collections <strong>of</strong> cDNAs (35) or BACs (29,30,40)<br />
developed during the course <strong>of</strong> the human genome program. The advantages <strong>of</strong>
Comparative Genomic Hybridization in <strong>Breast</strong> <strong>Cancer</strong> 65<br />
Figure 1 The use <strong>of</strong> a dual-color hybridization protocol for the detection <strong>of</strong> genome copy<br />
number aberrations. Both a normal sample (representing two copies <strong>of</strong> the genome) and a<br />
tumor sample are randomly labeled with differential fluorescent dyes. They are then<br />
cohybridized onto an array platform (BACs or oligonucleotides) with the subsequent log 2<br />
dye ratios <strong>of</strong> the tumor sample compared with the normal sample used to detect copy<br />
number changes. Abbreviation: BACs, bacterial artificial chromosomes.<br />
this approach are that the clones are readily available in the public domain and<br />
the signals produced during hybridization are large so that measurement precision<br />
is relatively high. The disadvantages are that the clones are not perfectly<br />
curated and mapped, so some may be mismapped. In addition, managing the<br />
large number is daunting, and the ultimate resolution <strong>of</strong> the analysis platforms is<br />
limited by the relatively genomic extents <strong>of</strong> the cloned probes.<br />
Commercial vendors, such as Affymetrix, Agilent, Illumina, and NimbleGen,<br />
have begun <strong>of</strong>fering oligonucleotide-based arrays with which to study<br />
genomewide CNAs (41–43). The Illumina arrays are unique in that the oligonucleotide<br />
probes are attached to microbeads (39), while the NimbleGen arrays<br />
are synthesized on demand and so can be easily customized (44). The smaller<br />
target sequences, 25 to 85 bp, also <strong>of</strong>fer the possibility <strong>of</strong> higher resolution than<br />
is possible using cloned probe arrays. Some protocols generate relative copy<br />
number information by comparing tumor and normal hybridization signals using<br />
two-color cohybridization approaches, while others rely on hybridizations <strong>of</strong><br />
tumor and normal reference DNA to separate arrays. The latter approach is<br />
technically simpler but requires additional normalization steps to account for<br />
array-to-array differences, introducing additional experimental variables when<br />
analyzing data sets.
66 Wang and Gray<br />
Since each oligonucleotide probe is synthesized to be complementary to a<br />
known sequence, oligonucleotide array platforms do not suffer as much from<br />
incorrect mapping, and probes can be selected to be uniquely represented in the<br />
genome, thereby minimizing cross-hybridization artifacts. The smaller sequences<br />
also are useful in detecting single nucleotide polymorphisms (SNPs), since<br />
hybridization rates to 25 mer are significantly reduced by a single nucleotide<br />
mismatch (37,45). The additional information provides researchers the ability to<br />
detect loss <strong>of</strong> heterozygosity (LOH) in regions <strong>of</strong> neutral copy change and also<br />
provide data for use in association studies (42). Regions <strong>of</strong> LOH that do not carry<br />
imbalances in copy number may provide evidence <strong>of</strong> selective pressures duplicating<br />
one allele coupled with the loss <strong>of</strong> the other. This is an important<br />
mechanism enabling removal <strong>of</strong> wild-type alleles while selecting mutations.<br />
Allele-specific selection can be either positive or negative. For example, loss <strong>of</strong><br />
the wild-type allele and duplication <strong>of</strong> an inactivating mutation might lead to<br />
inactivation <strong>of</strong> a tumor suppressor gene. The high frequency <strong>of</strong> LOH on 17p<br />
where p53 is located is an example <strong>of</strong> this type <strong>of</strong> selection (Fig. 2). On the other<br />
hand, amplification <strong>of</strong> a polymorphism that increases gene activity may contribute<br />
an advantage to the tumor. Amplification <strong>of</strong> a specific polymorphism in<br />
the aurora kinase STK15 is one example (46). The utility <strong>of</strong> allele-specific copy<br />
number analysis may also contribute to the identification <strong>of</strong> polymorphisms that<br />
confer increased susceptibility to cancer, since evidence is now emerging that<br />
tumors may increase the number <strong>of</strong> copies <strong>of</strong> polymorphisms that confer<br />
increased risk and may decrease the number <strong>of</strong> copies <strong>of</strong> polymorphisms that<br />
confer decreased risk <strong>of</strong> developing cancer (47). The frequency <strong>of</strong> allele-specific<br />
copy increases and decreases in individual tumors is substantial, as illustrated in<br />
Figure 3.<br />
While commercial oligonucleotide arrays <strong>of</strong>fer advantages, the smaller<br />
target sequences also present some disadvantages. Foremost among these is the<br />
relatively low signal-to-noise ratio generated during hybridization. Because the<br />
hybridization sequences are smaller, there is less nucleic acid sequence available<br />
to label, leading to less intense labeling. This typically leads to higher variability<br />
between probe sets in oligonucleotide arrays, and the need to combine multiple<br />
data points to estimate copy number, which in turn reduces the actual resolution<br />
<strong>of</strong> these arrays (38,48). The increased variability compared to BAC arrays also<br />
makes the detection <strong>of</strong> low-level changes, such as single-copy gains and losses,<br />
more difficult to detect, although performance with oligonucleotide arrays is<br />
improving steadily. Typically, microgram quantities <strong>of</strong> starting DNA are needed<br />
for an analysis.<br />
The most recent array CGH analysis platform does not operate through<br />
hybridization <strong>of</strong> tumor and normal DNA to a normal representation. Instead, it<br />
utilizes a molecular inversion probe (MIP) interrogation strategy to perform<br />
allele-specific copy number analysis (45). In this approach, each locus is interrogated<br />
by a linear MIP probe that has sequence homology to approximately<br />
20-bp regions flanking a single base to be tested. Each MIP also contains a unique
Comparative Genomic Hybridization in <strong>Breast</strong> <strong>Cancer</strong> 67<br />
Figure 2 LOH detection using Affymetrix GeneChip 1 Human Mapping 50K Array Xba<br />
240. A panel <strong>of</strong> breast cancer cell lines is individually hybridized to oligonucleotide<br />
arrays, and copy number changes and LOH are determined using dChipSNP. (A) The<br />
expected result <strong>of</strong> a genomic deletion is the presence <strong>of</strong> LOH, as seen on chromosome 8.<br />
(B) However, chromosome 17 shows much higher levels <strong>of</strong> LOH compared with deletions,<br />
suggesting that these regions have undergone genomic changes such as a deletion or<br />
duplication event or genomic conversion <strong>of</strong> one homolog so that you maintain normal<br />
copy number but not allelic information. This may result in the loss <strong>of</strong> a functional allele<br />
<strong>of</strong> a tumor suppressor gene or an active gain-<strong>of</strong>-function allele in the case <strong>of</strong> an oncogene.<br />
These genomic changes are only evident using a platform capable <strong>of</strong> detecting allelic<br />
changes. Abbreviation: LOH, loss <strong>of</strong> heterozygositiy.<br />
tag sequence and universal polymerase chain reaction (PCR) primer-binding<br />
sites facing away from each other so that PCR with primers to the universal<br />
sequences will not amplify. The first step in MIP CGH is to hybridize the linear<br />
MIPs to the DNA sample to be interrogated. After hybridization, the reaction<br />
mix is separated into multiple tubes, each containing polymerase, ligase, and one<br />
<strong>of</strong> the four nucleotides. The polymerase and ligase then fill the gaps in each<br />
reaction mix that contains a nucleotide complementary to the base at the test<br />
locus. The linear DNA and probes are then eliminated, reactions are mixed, and<br />
the ligated probes are amplified and labeled using PCR with the universal primers.<br />
The resulting PCR products are hybridized to an array comprising oligonucleotides<br />
homologous to the tag sequences in the MIPs. To date, MIP analyses<br />
interrogating over 20,000 loci have been reported (45). This approach reports
68 Wang and Gray<br />
Figure 3 Allelic copy number changes in breast cancer cell lines from a 50K MIP<br />
array platform. Allele copy numbers are represented as the higher copy allele and<br />
lower copy allele. (A) Allele copy number data from HCC38 from chromosome 5.<br />
The lack <strong>of</strong> copies <strong>of</strong> the lower allele is indicative <strong>of</strong> LOH. The array detects a region<br />
<strong>of</strong> LOH without copy number change. (B) Allele copy number data from ZR75.30<br />
from chromosome 8. 8q displays a region <strong>of</strong> monoallelic amplification in the region<br />
that contains MYC. Abbreviations: MIP, molecular inversion probe; LOH, loss <strong>of</strong><br />
heterozygositiy.
Comparative Genomic Hybridization in <strong>Breast</strong> <strong>Cancer</strong> 69<br />
copy number in an allele-specific manner and works well with FFPE material<br />
since only ~40 bp <strong>of</strong> intact DNA is needed at each locus for proper analysis.<br />
Typically, only about 75 ng <strong>of</strong> DNA is needed for an analysis since the reaction<br />
itself contains a PCR amplification step. The approach has the advantage that the<br />
tag sequences and arrays can be designed to have uniform hybridization conditions<br />
to copy number and the hybridization does not depend on the quality <strong>of</strong><br />
the hybridized DNA.<br />
DNA REQUIREMENTS<br />
One <strong>of</strong> the most important factors when considering a clinical CGH tool is the<br />
quality and amount <strong>of</strong> DNA available for analysis. Depending on which array<br />
platform is chosen, the amounts needed for successful hybridization can range<br />
from 50 ng to 3 mg. In addition, some protocols have been developed that allow<br />
analysis <strong>of</strong> material from formalin-fixed, paraffin-embedded (FFPE) samples<br />
(39,49). The largest amounts <strong>of</strong> starting material generally are used in protocols<br />
in which entire genomes are labeled and hybridized to the array, as is usual for<br />
array CGH using BAC or Agilent oligonucleotide arrays. In these experiments,<br />
the starting DNA is typically labeled with fluorescent nucleotides using random<br />
priming. For BAC arrays, unlabeled repeat-rich DNA is included to suppress<br />
hybridization <strong>of</strong> repeated sequences to repetitive sequences that are present in<br />
the BAC probes. Larger amounts <strong>of</strong> DNA are needed to “drive” these wholegenome<br />
hybridization reactions. The amount <strong>of</strong> starting material needed for<br />
analysis is reduced if the DNA is amplified or reduced in complexity—for<br />
example, through a PCR amplification step preceded by restriction digestion that<br />
reduces the complexity <strong>of</strong> the hybridized DNA (50). This complexity reduction<br />
results in an increase <strong>of</strong> template sequences, which can utilized in downstream<br />
hybridization steps. Both academically and commercially developed platforms<br />
result in data sets that are able to detect gains and losses with the caveat that<br />
variations and noise may differ between platforms (Fig. 4).<br />
While there is a range <strong>of</strong> starting DNA amounts between the various<br />
platforms, recent protocols have been developed that can amplify a large<br />
quantity <strong>of</strong> DNA from small starting amounts. These applications can be critical<br />
when dealing with samples that are limiting. These whole-genome amplifications<br />
are useful for both intact, high-quality DNA and degraded samples and<br />
involve the use <strong>of</strong> j29 enzyme or PCR (51,52). Alternately, the MIP platform has<br />
a targeted PCR amplification inherent to the protocol, so it is particularly tolerant<br />
<strong>of</strong> small amounts <strong>of</strong> starting DNA (45). To date, successful data sets have been<br />
generated using as little material as 1 ng or a single cell (53). However, as with<br />
any random amplification procedure, the protocols may artificially create misrepresentations<br />
<strong>of</strong> the genome, resulting in false positives for amplifications and<br />
deletions. Determining the amount and quality <strong>of</strong> starting material along with the
70 Wang and Gray<br />
Figure 4 Chromosomal copy number changes in the breast cancer cell line MDAMB453. DNA from MDAMB453 is applied to three separate<br />
platforms: (A) a 3K BAC array, (B) a 20K MIP array, and (C) a 50K XbaI Affymetrix SNP array. Data from each platform are scaled using adjacent data<br />
points so that the resulting resolutions are comparable. The data are then smoothed using the GLAD s<strong>of</strong>tware package. Abbreviations: BAC, bacterial<br />
artificial chromosome; MIP, molecular inversion probe; SNP, single nucleotide polymorphism; GLAD, Gaussian model–based approach.
Comparative Genomic Hybridization in <strong>Breast</strong> <strong>Cancer</strong> 71<br />
type <strong>of</strong> information required will help determine the appropriate array CGH<br />
platform and DNA preparation technique to utilize.<br />
ABNORMALITY DETECTION AND INTERPRETATION<br />
Computational strategies to identify copy number gains and losses are a critical<br />
part <strong>of</strong> array CGH. The first step in the analysis <strong>of</strong> data is the identification <strong>of</strong><br />
any features with anomalous intensity readings. These could result from a high<br />
background, errors in printing, contamination, or air bubbles prohibiting<br />
hybridization <strong>of</strong> the sample to the array. After removal <strong>of</strong> these spurious data<br />
points, a normalization step is applied to calculate the tumor hybridization<br />
intensity along the genome, relative to the normal hybridization intensity. A<br />
statistical analysis is then applied to identify genome segments with different<br />
copy number levels. Several strategies have been developed for this purpose and<br />
are summarized in Table 2. These include the hidden Markov model (HMM)<br />
(54), circular binary segmentation (55), a Gaussian model–based approach<br />
(GLAD) (56), hierarchical clustering along each chromosome (CLAC) (57), an<br />
expectation maximization–based method (58), a Bayesian model (59), and a<br />
wavelet approach (60). Several <strong>of</strong> these algorithms have been made available as<br />
R packages through Bioconductor (www.bioconductor.org). Comparsions <strong>of</strong><br />
several <strong>of</strong> these methods have now been published (61,62).<br />
Detection <strong>of</strong> the differences in copy number between tumor and normal<br />
samples is increasingly straightforward and precise as CGH procedures improve.<br />
However, the interpretation <strong>of</strong> CGH analyses requires additional interpretation.<br />
First, high-resolution array CGH analyses are important now that focal, germline<br />
genome copy number polymorphisms are relatively common (36). These may be<br />
scored as CNAs if the samples for tumor versus normal CGH analyses are not<br />
from the same individual. In addition, it is tempting to think that the regions <strong>of</strong><br />
the genome not detected as aberrant during CGH analyses are present in cancer<br />
genomes in two copies. This is <strong>of</strong>ten not the case since tumor genomes may be<br />
highly aneuploid. Thus, a CGH comparison <strong>of</strong> a perfectly tetraploid tumor with a<br />
diploid normal sample will not show any evidence <strong>of</strong> abnormality. Thus, if<br />
absolute copy number assessments are important, it will be necessary to normalize<br />
the CGH analyses using information about absolute copy number at one or a few<br />
loci measured using fluorescent in situ hybridization (FISH) or quantitative PCR.<br />
CONCLUSIONS AND FUTURE DIRECTIONS<br />
Array CGH technologies are becoming increasingly powerful tools for the<br />
identification <strong>of</strong> recurrent genome CNAs that are associated with outcome or<br />
response to therapy in breast and other cancers (63). The ability to detect genetic<br />
copy number changes <strong>of</strong> both large and small scales will be critical in evaluating<br />
the most effective therapies on an individual patient basis. In the short term,<br />
array CGH will likely be accomplished using commercial oligonucleotide<br />
analysis platforms that allow assessment <strong>of</strong> allele-specific copy number using
72 Wang and Gray<br />
material from FFPE samples. In the long term, however, array CGH may be<br />
supplanted by high-throughput sequencing technologies that allow assessment <strong>of</strong><br />
both copy number and DNA sequence through deep sequencing. It is already clear<br />
that shotgun sequencing, if done at sufficient redundancy, <strong>of</strong>fers the same or better<br />
resolution than CGH platforms (64,65). Next-generation sequencing platforms<br />
based on massively parallel single-molecule sequencing promise sufficiently lowcost<br />
sequencing, so that these platforms may become a viable alternative to array<br />
CGH (66,67). Today, this is not cost effective, but costs <strong>of</strong> high-throughput<br />
sequencing are falling rapidly, so these approaches should be followed closely.<br />
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6<br />
Gene Expression Pr<strong>of</strong>iling as an Emerging<br />
Diagnostic Tool to Personalize<br />
Chemotherapy Selection for<br />
Early Stage <strong>Breast</strong> <strong>Cancer</strong><br />
Cornelia Liedtke and Lajos Pusztai<br />
Department <strong>of</strong> <strong>Breast</strong> Medical Oncology, The University <strong>of</strong> Texas,<br />
Anderson <strong>Cancer</strong> Center, Houston, Texas, U.S.A.<br />
INTRODUCTION<br />
The selection <strong>of</strong> adjuvant chemotherapy for breast cancer is currently more <strong>of</strong> an<br />
art than a science. Several combination chemotherapy regimens can improve<br />
disease-free and overall survival <strong>of</strong> stage I to stage III breast cancer when used as<br />
adjuvant (postoperative) or neoadjuvant (preoperative) therapy (1,2). However,<br />
no chemotherapy regimen is universally effective for all patients, and it is currently<br />
impossible to predict which <strong>of</strong> these regimens will actually work for a<br />
particular individual. Choosing the best possible adjuvant or neoadjuvant chemotherapy<br />
regimen for an individual is important because the right choice may<br />
make the difference between cure and relapse. Results from traditionally<br />
designed randomized clinical trials <strong>of</strong>fer little guidance for individualized<br />
treatment selection. These studies compare the activity <strong>of</strong> different treatments to<br />
establish which is the most effective for the entire study population. This<br />
approach ignores the fact that many patients are cured with earlier generation<br />
adjuvant regimens and therefore are not well served by the administration <strong>of</strong><br />
77
78 Liedtke and Pusztai<br />
longer, potentially more toxic, and more expensive second- or third-generation<br />
combination therapies. These studies also cannot reveal whether some cancers<br />
are particularly sensitive to a specific drug (or a combination <strong>of</strong> drugs) and<br />
therefore can only be cured by that treatment. The most effective treatment for<br />
an individual may or may not be the same regimen that is the most effective for<br />
the whole population. These theoretical concerns and the increasing number <strong>of</strong><br />
adjuvant chemotherapy options, as well as the advent <strong>of</strong> new molecular analytical<br />
techniques, fuel a renewed interest in developing chemotherapy response<br />
predictors that may lead to more personalized treatment recommendations.<br />
THE ROLE OF NEOADJUVANT CHEMOTHERAPY<br />
IN THE TREATMENT OF BREAST CANCER<br />
Administration <strong>of</strong> chemotherapy before surgical resection <strong>of</strong> the cancer (preoperative<br />
or neoadjuvant chemotherapy) provides a unique opportunity to<br />
identify molecular markers <strong>of</strong> response. Preoperative chemotherapy is the current<br />
standard <strong>of</strong> care for locally advanced breast cancer because this treatment<br />
<strong>of</strong>ten renders previously inoperable cancers amenable to surgery due to frequent<br />
and substantial tumor responses (3). Preoperative chemotherapy is also<br />
increasingly used in the management <strong>of</strong> operable breast cancer because it can<br />
reduce tumor size and allow for more limited surgery and possibly improved<br />
cosmetic outcome (4). Importantly, there is no statistically significant difference<br />
in survival between those who receive chemotherapy before surgery (i.e., neoadjuvant)<br />
and those who are treated with the same regimen postoperatively (i.e.,<br />
adjuvant) (5–7). Therefore, preoperative chemotherapy is an appropriate option<br />
for most patients who present with a breast cancer that requires systemic therapy.<br />
Furthermore, eradication <strong>of</strong> all invasive cancer from the breast and regional<br />
lymph nodes by the preoperative therapy [i.e., pathologic complete response,<br />
(pCR)], is associated with excellent long-term, disease-free, and overall survival<br />
(7,8). Therefore, one could propose that the regimen most likely to induce pCR is<br />
the most effective treatment for a particular individual, and that such treatment<br />
could be ideally suited to that patient either as adjuvant or neoadjuvant therapy.<br />
This is the rationale why pCR is a commonly used clinical endpoint for breast<br />
cancer response predictors. However, it is important to realize that most patients<br />
with less than pCR had at least some clinical response to chemotherapy, and<br />
therefore cannot be considered truly resistant to treatment, but rather, cases with<br />
pCR represent extreme chemotherapy sensitivity.<br />
CURRENT CHEMOTHERAPY RESPONSE PREDICTORS<br />
There are several clinical and pathologic variables <strong>of</strong> breast cancer that are consistently<br />
associated with a greater likelihood <strong>of</strong> response to neoadjuvant chemotherapy.<br />
These include estrogen receptor (ER)-negative status, smaller tumor<br />
size, increased proliferative activity, and high nuclear or histological grade (9–12).
Gene Expression Pr<strong>of</strong>iling to Personalize Chemotherapy Selection 79<br />
Tumor size, grade, and ER-status are routinely available parameters that can be<br />
combined into a multivariate response prediction model (13). One such model<br />
is freely available at our institutional Web site, http://www.mdanderson.org/<br />
Care_Centers/<strong>Breast</strong>Center/dIndex.cfm?pn¼448442B2-3EA5-4BAC-<br />
98310076A9553E63. However, clinical variable–based predictions are limited to<br />
predict response to chemotherapy in general and lack regimen specificity.<br />
A large number <strong>of</strong> molecular markers were proposed as potential predictors<br />
<strong>of</strong> response to various chemotherapy agents (11,12,14–16). However, currently<br />
there are no molecular markers <strong>of</strong> response that could be used in the clinic to select<br />
patients for a particular therapy. Biomarker studies were traditionally conducted as<br />
correlative science projects attached to therapeutic trials as optional components.<br />
This has imposed serious limitations on these studies. Sample sizes for marker<br />
discovery were limited by the availability <strong>of</strong> specimens that has lead to underpowered<br />
observations and introduced potential bias due to subset analysis. Most<br />
commonly, only associations between marker and response were reported, but no<br />
formal prediction models were developed. The methodologies to detect a marker<br />
were rarely standardized, and definitions for test-positive and test-negative cases<br />
<strong>of</strong>ten differed from study to study, even for the same marker.<br />
In addition to these technical limitations, there are important theoretical<br />
considerations, which also suggest that single gene molecular markers expressed<br />
by the cancer may have limited predictive value. Factors that reside outside the<br />
neoplastic cells could influence drug response. The importance <strong>of</strong> interactions<br />
between stroma and neoplastic cells, including neovascularization, changes in<br />
interstitial fluid pressure, and immune/inflammatory response are increasingly<br />
recognized as potential determinants <strong>of</strong> prognosis and possibly response to<br />
therapy (17,18). Rates <strong>of</strong> drug metabolism are also variable and can have an<br />
effect on drug activity. Cellular response to toxic insults is a complex molecular<br />
process that involves simultaneous induction <strong>of</strong> several proapoptotic and survival<br />
pathways, including activation <strong>of</strong> repair and drug elimination mechanisms<br />
(19,20). Tumor response may depend on the balance <strong>of</strong> these pathways, and<br />
therefore any single gene may contain only limited information about the<br />
complexity <strong>of</strong> the mechanisms that determine response to chemotherapy.<br />
GENE EXPRESSION PROFILING AS A NOVEL TOOL<br />
FOR RESPONSE PREDICTION<br />
Gene expression pr<strong>of</strong>iling with DNA microarrays represents a relatively recent<br />
tissue analysis tool that was developed in the early 1990s (21). This technology<br />
enables investigators to measure, in a semiquantitative manner in a single<br />
experiment, the expression <strong>of</strong> almost all mRNAs expressed in a small piece <strong>of</strong><br />
cancer tissue. The rationale behind pharmacogenomic a research is that by using<br />
a <strong>Ph</strong>armacogenomics is defined in this review as the use <strong>of</strong> mRNA expression pr<strong>of</strong>iles <strong>of</strong> the tumor to<br />
predict drug efficacy. <strong><strong>Ph</strong>armacogenetics</strong> is defined as the study <strong>of</strong> DNA variations in the germline to<br />
predict toxicity or treatment response.
80 Liedtke and Pusztai<br />
this technology, one can identify individually predictive genes and combine<br />
these into a multivariate prediction model that will be more accurate than any<br />
single gene alone. Multivariate outcome prediction models are not new to<br />
medicine. Independent and individual weakly predictive clinical variables have<br />
been combined into useful and validated prognostic or predictive models. There<br />
are several established statistical methods for combining variables into prediction<br />
models, including the commonly used logistic regression analysis and<br />
the Cox proportional hazards regression model (22). Predictors could be<br />
binary—that is, predicting response versus no response—or could predict the<br />
probability <strong>of</strong> response on a continuous scale. The Adjuvant Online (http://www<br />
.adjuvantonline.com) s<strong>of</strong>tware represents a freely available validated multivariate<br />
prognostic prediction tool for breast cancer.<br />
The general strategy for multigene marker development in neoadjuvant<br />
studies is to compare the transcriptional pr<strong>of</strong>iles <strong>of</strong> cancers that responded to<br />
therapy with those that did not and to identify differentially expressed genes that<br />
can be combined into a prediction model. There are several caveats that make<br />
pharmacogenomic predictor discovery challenging.<br />
1. The multiple comparison problem inherent to microarray analysis is well<br />
known. It refers to the large number <strong>of</strong> variables (i.e., genes) that are<br />
compared between two small data sets. As a consequence, observation <strong>of</strong><br />
many small p values may be due to chance. This could result in spuriously<br />
identifying genes as associated with response when in fact they are not.<br />
Several methods used to adjust for this phenomenon, at least to some extent,<br />
by calculating false discovery rates for particular p values were described (23).<br />
2. Another confounder relates to the coordinated expression <strong>of</strong> thousands <strong>of</strong> genes.<br />
Individual transcripts do not represent independent variables, but rather their<br />
expression is highly correlated with one another. Many phenotypic characteristics<br />
<strong>of</strong> breast cancer are also associated with, and likely caused by, the coordinated<br />
expression <strong>of</strong> thousands <strong>of</strong> genes. For example, ER-positive cancers<br />
differ from ER-negative tumors in the expression <strong>of</strong> thousands <strong>of</strong> genes (24). The<br />
gene expression pattern <strong>of</strong> high-grade tumors is also different from low-grade<br />
tumors (25). These large-scale gene expression patterns associated with clinical<br />
phenotypes have a pr<strong>of</strong>ound influence on the pharmacogenomic discovery<br />
process, because ER-negative, high-grade breast cancers are more sensitive<br />
to many different types <strong>of</strong> chemotherapies compared with ER-positive and<br />
low-grade tumors. Unadjusted pharmacogenomic comparison <strong>of</strong> responders<br />
with nonresponders will yield gene lists that are dominated by ER- and gradeassociated<br />
genes, and the resulting pharmacogenomic predictor may represent<br />
a predictor <strong>of</strong> clinical phenotype. When the discovery sample size is large, it<br />
is possible to adjust for these phenotypic confounders; however, in small<br />
studies, these adjustments may be difficult to do.<br />
3. The probability that a supervised pharmacogenomic discovery approach, when<br />
responders are compared with nonresponders, will lead to regimen-specific
Gene Expression Pr<strong>of</strong>iling to Personalize Chemotherapy Selection 81<br />
predictors depends on to what extent the response groups are balanced for<br />
strong phenotypic markers and on the extent <strong>of</strong> molecular differences that<br />
determine drug-specific response. If drug sensitivity is influenced by the<br />
two- to threefold higher or lower expression <strong>of</strong> a few dozen genes, these<br />
differences may not be readily discovered through supervised pharmacogenomic<br />
analysis <strong>of</strong> small data sets. The modest gene expression differences<br />
between responders and nonresponders can be masked by the larger scale<br />
molecular differences due to any phenotypic imbalance between the<br />
response groups and the technical noise <strong>of</strong> microarray experiments. For<br />
example, when 24,000 measurements are performed (e.g., Affymetrix<br />
U133A gene chip) and the overall concordance is 97.98% in a technical<br />
replicate (i.e., same RNA pr<strong>of</strong>iled twice), 1.31% <strong>of</strong> all measurements can<br />
have tw<strong>of</strong>old or greater variation. This means that 314 genes decreased or<br />
increased tw<strong>of</strong>old from one experiment to another due to technical noise<br />
alone (26). If the sample size is large, this technical noise can be separated<br />
from the true signal, but when sample sizes are small it is more difficult to<br />
separate noise from the true signal.<br />
PHARMACOGENOMIC MARKER DEVELOPMENT PROCESS<br />
Ideally, the development process <strong>of</strong> molecular markers should resemble the<br />
development stages <strong>of</strong> therapeutic agents (Fig. 1) (27). In this context, a phase I<br />
marker discovery study would demonstrate the technical feasibility <strong>of</strong> developing a<br />
predictor and assess the reproducibility <strong>of</strong> marker results. The second phase<br />
Figure 1<br />
Schema <strong>of</strong> the developmental stages <strong>of</strong> molecular markers.
82 Liedtke and Pusztai<br />
includes phase II validation studies <strong>of</strong> statistically adequate power to estimate<br />
more precisely the predictive performance <strong>of</strong> the test in independent validation<br />
cases. The next level <strong>of</strong> marker evaluation would include the demonstration <strong>of</strong><br />
clinical value by showing that a particular clinical endpoint improves when the<br />
new test is used compared with the current standard, which could be no testing<br />
or another type <strong>of</strong> testing. This may require a prospective phase III marker<br />
validation study. Currently, no pharmacogenomic response predictors have<br />
completed all three levels <strong>of</strong> clinical evaluation. Most published studies represent<br />
phase I marker discovery and phase II marker validation studies.<br />
FEASIBILITY OF MICROARRAY-BASED DIAGNOSTIC TESTS<br />
Technical Reproducibility<br />
The reproducibility <strong>of</strong> microarray measurements has been examined systematically<br />
by the Microarray Quality Control Project (MAQC), which included the US<br />
Food and Drug Administration and 51 academic and industry collaborators. The<br />
goal <strong>of</strong> this project was to assess the within- and between-laboratory technical<br />
reproducibility <strong>of</strong> microarray measurements and compare the results across<br />
different microarray platforms. Four different RNA samples were pr<strong>of</strong>iled in five<br />
replicates on seven different array platforms, three different laboratories representing<br />
each platform. The same RNA samples were also pr<strong>of</strong>iled by three<br />
different reverse-transcription polymerase chain reaction (RT-PCR) methods.<br />
The most important finding <strong>of</strong> this collaborative effort was that the microarray<br />
measurements were highly reproducible within and across the different commercially<br />
available microarray platforms (28,29). The median coefficient <strong>of</strong><br />
variation (CV) for within-laboratory replicates ranged from 5% to 15% for the<br />
various platforms and it was 10% to 20% for between-laboratory replicates. Most<br />
importantly, the concordance between the microarray based mRNA measurements<br />
and RT-PCR results were also high. The correlation coefficients for gene<br />
expression ratios measured by arrays and by RT-PCR in two different samples<br />
ranged from 0.79 to 0.92 for several hundreds <strong>of</strong> genes.<br />
Between-platform variation in gene expression measurements is due to<br />
sequence variations in the probe sets that target the same gene at different<br />
locations. Factors that determine signal intensities generated by distinct probes<br />
for the same gene include GC content <strong>of</strong> the sequence, sequence length, crossmatch<br />
opportunities, and the location <strong>of</strong> the probe sequence in relation to the<br />
3 0 end <strong>of</strong> the target gene. The concordance between signal intensity for probes<br />
that target the same gene on different platforms is directly related to sequence<br />
homology (30,31). Probes with complete sequence match yield highly concordant<br />
results across platforms.<br />
Few studies examined the within- and cross-platform reproducibility <strong>of</strong><br />
multigene prediction scores, as opposed to the reproducibility <strong>of</strong> single gene<br />
measurements. Two reports indicated high reproducibility <strong>of</strong> pharmacogenomic
Gene Expression Pr<strong>of</strong>iling to Personalize Chemotherapy Selection 83<br />
prediction results in replicate experiments from the same RNA, when the same<br />
experimental procedures were used (26,32). Furthermore, when random noise<br />
was added to corrupt the gene expression data, a remarkable degree <strong>of</strong> robustness<br />
was observed in the face <strong>of</strong> noise.<br />
However, caution must be exercised when trying to reproduce prediction<br />
results across different platforms. There are multiple sources <strong>of</strong> variation that<br />
will decrease reproducibility including differences in probe set and different<br />
normalization methods. Essentially, all published results that attempted crossplatform<br />
testing <strong>of</strong> gene signatures reported diminished, but not completely<br />
lost, classification accuracy on data generated by other than the original<br />
platform (33).<br />
Preanalytical Sources <strong>of</strong> Variation<br />
Another potentially important source <strong>of</strong> variation in microarray data is preanalytical<br />
variations, including differences in tissue sampling and RNA degradation.<br />
There are several different techniques to obtain tissue from a patient for<br />
pharmacogenomic studies, including excisional biopsy (with or without microdissection),<br />
fine needle aspiration (FNA), and core needle biopsy (CNB). Both<br />
needle biopsy techniques yield sufficient RNA for gene expression pr<strong>of</strong>iling with<br />
DNA microarrays (1–3 mg) (34–36). However, different sampling methods may<br />
result in different cellular composition and distinct gene expression pr<strong>of</strong>iles (37).<br />
FNA specimens contain mostly neoplastic cells with few infiltrating leukocytes,<br />
whereas the tumor cell content <strong>of</strong> CNB can vary from 10% to 90%. It is<br />
important to consider the different amount <strong>of</strong> stromal cell that might be present<br />
in a specimen, because the presence or absence <strong>of</strong> stromal genes might influence<br />
prognostic or predictive gene signatures. Also, in studies comparing pre- and<br />
postchemotherapy samples, an important source <strong>of</strong> variation may be the different<br />
cellular composition <strong>of</strong> the specimens.<br />
RNA is an easily degradable molecule and therefore was considered<br />
suboptimal target for marker measurements compared with DNA or proteins.<br />
The development <strong>of</strong> single step universal RNA stabilizing and preserving<br />
reagents (RNA later, Ambion) has completely changed the field <strong>of</strong> RNA-based<br />
diagnostics (38). Today, RNA can be stabilized within seconds after a fresh<br />
tissue biopsy has been obtained and stored at room temperature for several days<br />
(if needed) due to readily available commercial RNA stabilizing solutions.<br />
Under these circumstances, RNA degradation from properly collected fresh<br />
tissues is now rare. Interestingly, it is now more cumbersome to preserve proteins<br />
for future analysis than it is to preserve RNA. A single step, universal<br />
protein preserving solution is yet to be discovered. However, it is important to<br />
realize that surgical resection methods (i.e., length <strong>of</strong> warm ischemia) and the<br />
length <strong>of</strong> time between removing a piece <strong>of</strong> tissue from the body and placing it<br />
into RNA preservative have a major effect on RNA quality (39).
84 Liedtke and Pusztai<br />
STATISTICAL DESIGN OF PHARMACOGENOMIC<br />
DISCOVERY AND VALIDATION STUDIES<br />
Clinical biomarker discovery and validation is governed by similar statistical<br />
rules as therapeutic clinical trials (40). Estimation <strong>of</strong> discovery sample size is<br />
essential to develop a reliable pharmacogenomic predictor, and appropriately<br />
powered independent validation studies are needed to convincingly demonstrate<br />
its predictive values. There are several statistical methods that could be used to<br />
estimate the number <strong>of</strong> patients needed to develop a clinical outcome predictor<br />
(41). One simple approach is to power the study to discover individual genes that<br />
show a predefined level <strong>of</strong> differential expression (<strong>of</strong>ten expressed in statistical<br />
terms as standard effect size) between outcome groups. However, the number<br />
and extent <strong>of</strong> differentially expressed genes are <strong>of</strong>ten unknown in advance, and<br />
the multiple comparisons (i.e., thousands <strong>of</strong> different measurements on each<br />
case) represent a major bias in this approach. Another approach takes into<br />
account accumulating preliminary results to estimate the sample size needed for<br />
discovery (42). With multivariate prediction algorithms, it is expected that the<br />
predictive accuracy increases as the predictor is developed from and trained on<br />
larger and larger data sets until it reaches a plateau (i.e., best possible predictor<br />
within the constrains <strong>of</strong> the technology). The general principle is to fit a learning<br />
curve into existing data by incrementally increasing the training set size and<br />
extrapolate results to larger sample sizes. This is an intuitively appealing<br />
approach, but it requires a substantial amount <strong>of</strong> preliminary results from many<br />
dozens <strong>of</strong> cases. It also implies that discovery sample size will vary depending<br />
on the difficulty <strong>of</strong> separating the two response groups on the basis <strong>of</strong> gene<br />
expression results. When we applied this method to human breast cancer gene<br />
expression data to estimate the number <strong>of</strong> cases needed for discovery <strong>of</strong> a<br />
chemotherapy response predictor, the results indicated that 80 to 100 cases<br />
would yield a predictor as good as extending the sample size to 200 (32).<br />
To assess the accuracy <strong>of</strong> the predictor, it is necessary to test it on independent<br />
patient cohorts. Often, there are not enough cases available for validation,<br />
and investigators report cross-validation results. In cross-validation, some<br />
samples are left out from the marker development process and later on used to<br />
test the accuracy <strong>of</strong> the predictor, and this process is repeated many times. This<br />
leave-one-out cross-validation method gives a reliable estimate <strong>of</strong> the overall<br />
accuracy <strong>of</strong> the marker development process, but it is not the validation <strong>of</strong> a<br />
particular predictor. The genes that contribute to the predictors differ from<br />
iteration to iteration. The final single best predictor that is developed from the<br />
entire training data must be tested on completely separate cases to assess its<br />
predictive value. The goal <strong>of</strong> an independent validation study is to (i) define the<br />
true sensitivity, specificity, and the positive and negative predictive values (PPV<br />
and NPV) with narrow confidence intervals (CI) and (ii) to prove clinical utility<br />
<strong>of</strong> the test. Since the estimated performance characteristics <strong>of</strong> the test are known<br />
from the cross-validation results, the design <strong>of</strong> validation studies is more
Gene Expression Pr<strong>of</strong>iling to Personalize Chemotherapy Selection 85<br />
straightforward. Sample size for a single arm validation studies can be calculated<br />
on the basis <strong>of</strong> the known historical response rate in unselected patients and the<br />
estimated PPV <strong>of</strong> the genomic predictor. For example, if we assume that the<br />
response rate in unselected patients is 30% and the true PPV <strong>of</strong> the test is 60%<br />
(two-fold greater chance <strong>of</strong> response in “test-positive cases”), the sample<br />
size could be calculated as follows: The lower bound <strong>of</strong> a two-sided 95% CI<br />
should not be less than 0.3 (since the expected maximum pCR rate is 30% in<br />
unselected cases). This implies that the standard error for the PPV is less than 0.1<br />
and therefore the study must include at least 24 “test-positive” patients. The<br />
proportion <strong>of</strong> patients with response is expected to be around 30%, and an<br />
accurate prediction algorithm should generate a proportion that test positive also<br />
about 0.20 to 0.40, this equates to a total sample size <strong>of</strong> 80 to 120 patients for<br />
validation. If the predictor performs better and the true PPV is higher than 60%,<br />
fewer patients may be sufficient. A randomized trial design could simultaneously<br />
address response rates in selected and unselected patients and could also assess<br />
treatment specificity <strong>of</strong> the predictor.<br />
BREAST CANCER PHARMACOGENOMIC RESPONSE PREDICTORS<br />
Supervised <strong>Ph</strong>armacogenomic Predictors Developed from Human Data<br />
Results from several phase I genomic response marker discovery studies were<br />
reported that compared gene expression pr<strong>of</strong>iles <strong>of</strong> cases with response and noresponse,<br />
and used the genes that were differentially expressed to construct<br />
multigene predictors. Some <strong>of</strong> these markers were also assessed in small independent<br />
validation cohorts. Table 1 summarizes the currently available literature<br />
in this field. Chang et al. presented the first study that suggested the feasibility <strong>of</strong><br />
developing multigene predictors <strong>of</strong> response to neoadjuvant chemotherapy. They<br />
developed a 92-gene predictor for clinical response to docetaxel using gene<br />
expression data from 20 patients with locally advanced breast cancer (36).<br />
Leave-one-out partial cross-validation indicated a predictive accuracy <strong>of</strong> 88%<br />
(95% CI, 68–97%), sensitivity <strong>of</strong> 85% (95% CI, 55–98%), and specificity <strong>of</strong> 91%<br />
(95% CI, 59–100%). Larger scale independent validation <strong>of</strong> this marker set has<br />
not yet been reported. Another small study examined 24 breast cancer patients<br />
treated with neoadjuvant paclitaxel, 5-fluoruracil, doxorubicin, and cyclophosphamide<br />
(T/FAC) chemotherapy and developed a 74-gene predictor for<br />
pCR (43). This multigene predictor model, tested on an independent cohort <strong>of</strong><br />
12 patients, showed a predictive accuracy <strong>of</strong> 78% (95% CI, 52–94%), PPV <strong>of</strong> 100%<br />
(95% CI, 29–100%), NPV <strong>of</strong> 73% (95% CI, 45–92%), sensitivity <strong>of</strong> 43% (95%<br />
CI, 10–82%), and specificity <strong>of</strong> 100% (95% CI, 72–100%). A larger follow-up<br />
study included 82 cases for marker discovery and examined 780 distinct pharmacogenomic<br />
predictors in cross-validation (32). Many <strong>of</strong> the distinct predictors<br />
demonstrated statistically equal performance in complete cross-validation. This<br />
(text continues on page 91)
86 Liedtke and Pusztai<br />
Table 1 Overview <strong>of</strong> Published Studies on <strong>Ph</strong>armacogenomic Predictors <strong>of</strong> Response to Neoadjuvant Chemotherapy in <strong>Breast</strong> <strong>Cancer</strong><br />
Patient characteristics and<br />
treatment Method/hypothesis<br />
Gene expression<br />
platform Results Comment<br />
Chang et al.,<br />
Lancet 2003<br />
(36)<br />
24 (¼dev.) and 6 (¼val.) pts.<br />
with locally advanced breast<br />
cancer<br />
? 4 cycles 3-weekly<br />
docetaxel<br />
Development,<br />
LOOCV and<br />
independent<br />
validation <strong>of</strong> a<br />
gene expression<br />
pr<strong>of</strong>ile<br />
predictive for<br />
clinical response<br />
(25% RD)<br />
HgU95-Av2<br />
(12,625 genes)<br />
92 differentially<br />
expressed genes (p <<br />
0.001) from different<br />
functional classes;<br />
Combination into<br />
prediction model<br />
LOOCV: 88% accuracy,<br />
92% PPV, 83%<br />
NPV, 85% sensitivity,<br />
91% specificity<br />
Independent validation:<br />
correct classification<br />
<strong>of</strong> six pts. with sensitive<br />
tumors<br />
First study developing<br />
a multigene<br />
predictor for<br />
response to<br />
chemotherapy in<br />
breast cancer<br />
patients<br />
Ayers et al., JCO<br />
2004 (43)<br />
24 (¼dev.) and 12 (¼val.) pts.<br />
with primary breast cancer<br />
? 3 cycles 3-weekly or 12<br />
cycles weekly paclitaxel<br />
followed by 4 cycles <strong>of</strong> FAC<br />
(T/FAC)<br />
Development and<br />
independent<br />
validation <strong>of</strong><br />
gene expression<br />
predictor for<br />
pCR<br />
cDNA arrays<br />
(30,721 genes)<br />
Development <strong>of</strong><br />
74-gene-multigene<br />
predictor<br />
Independent validation:<br />
78% accuracy, 100%<br />
PPV, 73% NPV,<br />
43% sensitivity,<br />
100% specificity<br />
No single gene sufficiently<br />
associated<br />
with pCR to serve as<br />
single valid marker<br />
Pro<strong>of</strong><strong>of</strong>principle<br />
for building multigene<br />
predictors for<br />
chemotherapy<br />
response
Gene Expression Pr<strong>of</strong>iling to Personalize Chemotherapy Selection 87<br />
Table 1 Overview <strong>of</strong> Published Studies on <strong>Ph</strong>armacogenomic Predictors <strong>of</strong> Response to Neoadjuvant Chemotherapy in <strong>Breast</strong> <strong>Cancer</strong> (Continued )<br />
Patient characteristics and<br />
treatment Method/hypothesis<br />
Gene expression<br />
platform Results Comment<br />
Iwao-Koizumi,<br />
JCO 2005 (44)<br />
44 (¼dev.) and 26 (¼val.) pts.<br />
with stage II/IV breast cancer<br />
? 4 cycles <strong>of</strong> docetaxel<br />
Development and<br />
independent<br />
validation <strong>of</strong><br />
gene expression<br />
predictor for<br />
clinical response<br />
(WHO criteria)<br />
ATAC-PCR (2,453<br />
selected genes)<br />
Development <strong>of</strong><br />
85-gene signature<br />
Independent validation:<br />
81% accuracy, 73%<br />
PPV, 91% NPV,<br />
92% sensitivity, 71%<br />
specificity<br />
Genes controlling the<br />
cellular redox<br />
environment associated<br />
with docetaxel<br />
resistance<br />
? supported by<br />
in vitro results<br />
Hannemann et al.,<br />
JCO 2005 (45)<br />
48 pts. with LABC<br />
? cycles <strong>of</strong> doxorubicin/<br />
docetaxel (AD, n ¼ 24)<br />
or doxorubicin/cyclophosphamide<br />
(AC, n ¼ 24)<br />
Identification <strong>of</strong><br />
gene expression<br />
predictor for<br />
pCR or nPCR<br />
for response to<br />
AD and/or AC<br />
Proprietary array<br />
(18,432 genes)<br />
Failure to identify differentially<br />
expressed<br />
genes or define multigene<br />
predictor<br />
Hierarchical clustering:<br />
pCR/npCR samples<br />
did not cluster distinctly<br />
compared<br />
to RD<br />
Comparison <strong>of</strong> gene<br />
expression between<br />
pre- and<br />
postchemotherapy<br />
samples: more<br />
transcriptional<br />
changes among<br />
sensitive compared<br />
to resistant tumors<br />
Gianni et al., JCO<br />
2005 (46)<br />
89 (¼dev.) and 82 (¼val.) pts.<br />
with LABC<br />
? 3 cycles <strong>of</strong> 3-weekly<br />
doxorubicin/paclitaxel<br />
followed by 12 cycles<br />
weekly paclitaxel<br />
? 3 cycles 3-weekly or<br />
12 cycles weekly paclitaxel<br />
followed by 4 cycles <strong>of</strong><br />
FAC (T/FAC)<br />
Identification <strong>of</strong><br />
gene expression<br />
markers that<br />
predict response<br />
Correlation<br />
between RS and<br />
response<br />
RT-PCR (384<br />
genes)<br />
Affymetrix HG-<br />
U133 Plus 2.0<br />
86 genes correlating<br />
with pCR (p < 0.05)<br />
forming three clusters<br />
(ER, proliferation<br />
and immunerelated<br />
gene cluster)<br />
RS correlated with pCR<br />
86-gene model developed<br />
and successfully<br />
validated<br />
(Continued)
88 Liedtke and Pusztai<br />
Table 1 Overview <strong>of</strong> Published Studies on <strong>Ph</strong>armacogenomic Predictors <strong>of</strong> Response to Neoadjuvant Chemotherapy in <strong>Breast</strong> <strong>Cancer</strong> (Continued )<br />
Patient characteristics and<br />
treatment Method/hypothesis<br />
Gene expression<br />
platform Results Comment<br />
Folgueira et al.,<br />
Clin <strong>Cancer</strong> Res<br />
2005 (47)<br />
38 (¼dev.) and 13 (¼val.)<br />
31 (¼dev.) and 13 (¼val.)<br />
? 4 cycles 3-weekly<br />
doxorubicin/cyclophosphamide<br />
(AC)<br />
Development and<br />
independent<br />
validation <strong>of</strong><br />
gene expression<br />
predictor for<br />
pCR<br />
692 cDNA<br />
microarray<br />
4,608 gene<br />
platform<br />
25 differentially<br />
expressed transcripts<br />
between responders<br />
and nonresponders,<br />
3 gene classifier could<br />
not be validated;<br />
successful development<br />
and validation<br />
(LOOCV and independent<br />
dataset) <strong>of</strong><br />
distinct three-gene<br />
classifier<br />
Dressman et al.,<br />
Clin <strong>Cancer</strong> Res<br />
2006 (48)<br />
37 pts. with stage IIB/III<br />
breast cancer<br />
? 4 cycles <strong>of</strong> liposomal<br />
doxorubicin/paclitaxel þ<br />
local whole breast<br />
hyperthermia<br />
Definition <strong>of</strong> gene<br />
signature for<br />
(a) inflammatory<br />
breast cancer<br />
(IBC), (b) tumor<br />
hypoxia, and (c)<br />
clinical response<br />
and validation in<br />
two independent<br />
datasets<br />
Affymetrix<br />
HG-U133<br />
Plus 2.0<br />
Identification and validation<br />
(LOOCV) <strong>of</strong><br />
(a) 22-gene signature<br />
for IBC<br />
(b) 18-gene signature<br />
for tumor hypoxi<br />
Failure to identify gene<br />
signature predicting<br />
clinical response<br />
Development and<br />
successful validation<br />
(2 independent<br />
datasets)<strong>of</strong>a<br />
signature predictive<br />
for lymph node<br />
persistence
Gene Expression Pr<strong>of</strong>iling to Personalize Chemotherapy Selection 89<br />
Table 1 Overview <strong>of</strong> Published Studies on <strong>Ph</strong>armacogenomic Predictors <strong>of</strong> Response to Neoadjuvant Chemotherapy in <strong>Breast</strong> <strong>Cancer</strong> (Continued )<br />
Patient characteristics and<br />
treatment Method/hypothesis<br />
Gene expression<br />
platform Results Comment<br />
Thuerigen et al.,<br />
JCO 2006 (49)<br />
52 (¼dev/GEsDoc) and 48<br />
(¼val./GEDoc) pts. with<br />
breast cancer<br />
? 5 cycles biweekly gemcitabine/epirubicin<br />
followed by<br />
4 cycles biweekly docetaxel<br />
(GEsDoc)<br />
? 6 cycles <strong>of</strong> 3-weekly<br />
gemcitabine/epirubicin/<br />
docetaxel (GEDoc)<br />
Development and<br />
independent<br />
validation <strong>of</strong><br />
gene expression<br />
predictor for<br />
pCR<br />
Oligonucleotide<br />
array (21329<br />
genes)<br />
512-gene-signature<br />
predictive <strong>of</strong> pCR<br />
with: independent<br />
validation: 88%<br />
accuracy, 64% PPV,<br />
95% NPV, 78%<br />
sensitivity, 90%<br />
specificity<br />
HER2/neu expression<br />
independently<br />
predictive <strong>of</strong><br />
response<br />
Park et al., <strong>Breast</strong><br />
<strong>Cancer</strong> Res<br />
Treat 2006 (50)<br />
21 pts. with stage II/III breast<br />
cancer<br />
? 4 cycles <strong>of</strong> 3-weekly<br />
FEC followed by 12 cycles<br />
<strong>of</strong> weekly paclitaxel<br />
Development and<br />
LOOCV <strong>of</strong><br />
ABC transporter<br />
gene expression<br />
pr<strong>of</strong>iles<br />
predictive for<br />
pCR<br />
Affymetrix HG-<br />
U133 Plus 2.0<br />
(49 ABC transporters)<br />
11 differentially expressed<br />
ABC transporters multigene<br />
predictor model <strong>of</strong><br />
differentially expressed<br />
ABC transporters predictive<br />
<strong>of</strong> pCR<br />
LOOCV: 93% accuracy,<br />
93% PPV, 94%<br />
NPV, 88% sensitivity,<br />
96% specificity<br />
Cleator et al.,<br />
<strong>Breast</strong> <strong>Cancer</strong><br />
Res Treat 2006<br />
(51)<br />
40 pts. with primary breast<br />
cancer<br />
? 6 cycles doxorubicin/<br />
cyclophosphamide (AC)<br />
Development and<br />
LOOCV <strong>of</strong><br />
predictor for<br />
clinical response<br />
Affymetrix<br />
HG-U133A<br />
253 differentially<br />
expressed genes;<br />
75 and 178 genes overexpressed<br />
in resistant<br />
and sensitive tumors,<br />
respectively<br />
Development and<br />
LOOCV <strong>of</strong> gene predictor<br />
for response<br />
Previously identified<br />
92-gene docetaxel<br />
classifier (36) predicted<br />
only 39–61%<br />
tumors correctly<br />
? gene expression<br />
pattern for docetaxel<br />
is not predictive for<br />
AC response<br />
(Continued)
90 Liedtke and Pusztai<br />
Table 1 Overview <strong>of</strong> Published Studies on <strong>Ph</strong>armacogenomic Predictors <strong>of</strong> Response to Neoadjuvant Chemotherapy in <strong>Breast</strong> <strong>Cancer</strong> (Continued )<br />
Patient characteristics and<br />
treatment Method/hypothesis<br />
Gene expression<br />
platform Results Comment<br />
Hess et al., JCO<br />
2006 (32)<br />
82 (¼dev.) and 51 (¼val.) pts.<br />
with primary breast cancer<br />
? 3 cycles 3-weekly or<br />
12 cycles weekly paclitaxel<br />
followed by 4 cycles <strong>of</strong> FAC<br />
(T/FAC)<br />
Development and<br />
independent<br />
validation <strong>of</strong><br />
gene expression<br />
predictor for<br />
pCR<br />
Affymetrix<br />
HG-U133A<br />
Identification <strong>of</strong> 30-<br />
gene predictor<br />
Validation fivefold<br />
cross-validation and<br />
permutation testing<br />
Independent validation:<br />
76% accuracy, 52%<br />
PPV, 96% NPV,<br />
92% sensitivity, 71%<br />
specificity<br />
Follow-up study<br />
<strong>of</strong> (43)<br />
Predictor preserved its<br />
predictive accuracy<br />
when applied to<br />
replicate samples<br />
Largest study to date<br />
Mina et al., <strong>Breast</strong><br />
<strong>Cancer</strong> Res<br />
Treat 2006 (52)<br />
45 pts. with stage II/III breast<br />
cancer (including 6 pts. with<br />
inflammatory BC)<br />
? 3 cycles biweekly doxorubicin<br />
and 6 cycles weekly<br />
docetaxel<br />
Identification <strong>of</strong><br />
genes correlating<br />
with pCR<br />
Correlation <strong>of</strong> pCR<br />
with RS or ERrelated<br />
genes<br />
Identification <strong>of</strong><br />
genes correlating<br />
with inflammatory<br />
phenotype<br />
RT-PCR assay<br />
(274 genes)<br />
22 <strong>of</strong> 274 candidate genes<br />
correlated with pCR (p <<br />
0.05) forming three large<br />
clusters (angiogenesis,<br />
proliferation, and invasion-related<br />
genes)<br />
Lack <strong>of</strong> correlation<br />
between pCR and<br />
RS/ER-related genes<br />
24/274 genes correlated<br />
with inflammatory BC<br />
Abbreviations: ABC transporter, ATP-binding cassette transporter; dev., development/training set; IBC, inflammatory breast cancer; LABC, locally advanced breast<br />
cancer; LOOCV, leave-one-out cross-validation; npCR, near pCR; NPV, negative predictive value; pCR, pathological complete response; PPV, positive predictive<br />
value; pts., patients; RD, residual disease; RS, recurrence score (Oncotype DX # ) (53); RT-PCR, reverse transcription polymerase chain reaction; val., validation set.
Gene Expression Pr<strong>of</strong>iling to Personalize Chemotherapy Selection 91<br />
observation is confirmed by similar reports in the genomic literature, which<br />
indicates that several different but equally good predictors can be developed<br />
from the same data. This is due to the coordinated expression <strong>of</strong> many thousands<br />
<strong>of</strong> genes. Any combination from a particular group <strong>of</strong> coexpressed genes can<br />
carry similar information. However, a nominally best 30-probe set genomic<br />
predictor <strong>of</strong> pCR (DLDA-30) was selected for independent validation on 51<br />
cases, and its performance was compared with a multivariate clinical variable–<br />
based predictor including age, estrogen receptor status, and grade. The genomic<br />
predictor showed a sensitivity <strong>of</strong> 92% that was significantly higher than that <strong>of</strong><br />
the clinical predictor (61%). The NPV (96% vs. 86%) and area under the<br />
receiving operator curve (0.877 vs. 0.811) were also slightly better. Several other<br />
smaller studies demonstrated similar pro<strong>of</strong> <strong>of</strong> concept that this supervised<br />
approach to marker development is feasible (Table 1).<br />
Genomic Predictors from Cell Line Models<br />
An interesting alternative approach is to use cancer models to develop chemotherapy<br />
response predictors and test the performance <strong>of</strong> these in human data. The<br />
gene expression pr<strong>of</strong>iles <strong>of</strong> breast cancer cell lines are different from those <strong>of</strong><br />
human breast cancers, but nevertheless, they recapitulate many biological and<br />
genomic properties <strong>of</strong> primary breast cancers (54). Several groups reported drugspecific<br />
transcriptional changes in response to chemotherapy exposure in vitro<br />
that might be exploited as predictors <strong>of</strong> therapy response in patients (55).<br />
However, the predictive value <strong>of</strong> these in vitro generated signatures in patients<br />
has not yet been studied extensively. Nevertheless, this is an important research<br />
direction that if working can have important implications for individualizing<br />
treatment regimens for patients. In fact one could envisage custom-assembled<br />
chemotherapy regimens based on sensitivity pr<strong>of</strong>iles for individual drugs. It is<br />
rapidly becoming feasible to test such concepts in silico. Several groups have<br />
made their complete gene expression data from neoadjuvant biopsies publicly<br />
available. Cell line-based predictive signatures can be tested on these already<br />
existing and clinically annotated human data sets (56).<br />
Gene Expression Changes in Serial Biopsies<br />
Similar to cell lines, transcriptional response to chemotherapy was also observed<br />
in serial biopsies before and during chemotherapy. The rationale is that these<br />
changes may be more predictive <strong>of</strong> response than baseline gene expression<br />
pr<strong>of</strong>iles. However, the few small studies that examined this approach reported<br />
very heterogeneous transcriptional response to chemotherapy (57). In essence,<br />
different genes change in different individuals. Sotiriou et al. examined gene<br />
expression changes after one complete cycle <strong>of</strong> neoadjuvant chemotherapy and<br />
demonstrated that tissue samples, obtained from patients with good response,<br />
exhibited much greater changes in gene expression than those from poor
92 Liedtke and Pusztai<br />
responders (34). These observations were corroborated by results obtained by<br />
Hannemann et al., who compared gene expression pr<strong>of</strong>iling on pretreatment<br />
biopsies (n ¼ 46) and posttreatment tumors (n ¼ 17) obtained at time <strong>of</strong> surgery.<br />
They also demonstrated that neoadjuvant chemotherapy causes major changes in<br />
gene expression in those cases who responded to treatment compared with those<br />
who did not (58,59). Several technical and theoretical concerns limit our<br />
enthusiasm for this approach to predictor development. It is difficult to obtain<br />
serial biopsies at regular intervals from patients who undergo chemotherapy.<br />
There is substantial variation in the actual timing <strong>of</strong> the biopsy compared with<br />
the planned time <strong>of</strong> obtaining the follow-up biopsy. For example, biopsies<br />
planned at 24 hours after chemotherapy are in fact usually collected during an<br />
18- to 36-hour window. Since the kinetics <strong>of</strong> important gene expression changes<br />
are unknown, this introduces unpredictable variation into already highly variable<br />
data. Also, there are altogether missing time points, which further reduce the<br />
already small sample size for discovery. These factors increase the risk for false<br />
discovery from serially collected small pharmacogenomic data sets. More<br />
importantly, the practical value <strong>of</strong> a response predictor that is applied to a biopsy<br />
after chemotherapy was given is not particularly appealing.<br />
FUTURE POTENTIAL OF PHARMACOGENOMICS<br />
<strong>Ph</strong>armacogenomic predictive marker discovery is a new science. It is relatively<br />
less mature than the field <strong>of</strong> prognostic gene signatures because <strong>of</strong> the lack <strong>of</strong><br />
preexisting tissue banks from patients who received preoperative chemotherapy.<br />
All human biopsies had to be collected in prospective clinical trials in the past<br />
few years, unlike the readily available frozen and archived tumor banks that were<br />
suitable for prognostic signature discovery and validation. However, several<br />
such data sets now exist and the field has likely entered an accelerated phase <strong>of</strong><br />
development.<br />
We have learned several important lessons. (i) It is clear that the gene<br />
expression pr<strong>of</strong>iles <strong>of</strong> breast cancers with extreme chemotherapy sensitivity are<br />
different from those with lesser sensitivity. Unfortunately, these gene expression<br />
differences correspond to major clinical pathological phenotypes such as ERnegative<br />
status and high grade that somewhat limits the clinical value <strong>of</strong> the<br />
current predictors. However, as larger data sets become available, it will be<br />
possible to attempt to develop predictors after stratification by known clinical<br />
pathological variables. (ii) It is also clear from many different reports in the<br />
genomic literature that there is no unique, single best signature for any particular<br />
classification problem. There are several distinct predictors that can perform<br />
equally well (60). (iii) The availability <strong>of</strong> large, public, clinically annotated,<br />
comprehensive gene expression databases started to revolutionize the biomarker<br />
discovery process. It is now possible to test in silico any mRNA-based marker<br />
for prognostic or chemotherapy or endocrine therapy predictive values, respectively,<br />
on data sets that were prospectively assembled for this purpose (61). As
Gene Expression Pr<strong>of</strong>iling to Personalize Chemotherapy Selection 93<br />
more neoadjuvant data sets become available from patient cohorts treated with<br />
different treatment regimens, it will be possible to examine the regimen specificity<br />
<strong>of</strong> the various predictors. (iv) The most important long-term benefit from<br />
pharmacogenomic research may not be the current predictive signatures or the<br />
ones that will be developed in the near future, which still represent relatively<br />
crude attempts to match patients with existing drugs, but rather the large<br />
molecular data sets that this research generates. These databases could allow the<br />
identification <strong>of</strong> novel drug targets in human data and the development <strong>of</strong> drugs<br />
that target subsets <strong>of</strong> patients with particular genomic abnormalities.<br />
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(Adriamycin) and cyclophosphamide (cytoxan) (AC) response and resistance. <strong>Breast</strong><br />
<strong>Cancer</strong> Res Treat 2006; 95(3):229–233.<br />
52. Mina L, Soule SE, Badve S, et al. Predicting response to primary chemotherapy:<br />
gene expression pr<strong>of</strong>iling <strong>of</strong> paraffin-embedded core biopsy tissue. <strong>Breast</strong> <strong>Cancer</strong><br />
Res Treat 2007; 103(2):197–208.<br />
53. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence <strong>of</strong> tamoxifentreated,<br />
node-negative breast cancer. N Engl J Med 2004; 351(27):2817–2826.<br />
54. Neve RM, Chin K, Fridlyand J, et al. A collection <strong>of</strong> breast cancer cell lines for the<br />
study <strong>of</strong> functionally distinct cancer subtypes. <strong>Cancer</strong> Cell 2006; 10(6):515–527.<br />
55. Troester MA, Hoadley KA, Sorlie T, et al. Cell-type-specific responses to chemotherapeutics<br />
in breast cancer. <strong>Cancer</strong> Res 2004; 64(12):4218–4226.<br />
56. Potti A, Dressman HK, Bild A, et al. Genomic signatures to guide the use <strong>of</strong> chemotherapeutics.<br />
Nat Med 2006; 12(11):1294–1300.<br />
57. Buchholz TA, Stivers DN, Stec J, et al. Global gene expression changes during<br />
neoadjuvant chemotherapy for human breast cancer. <strong>Cancer</strong> J 2002; 8(6):461–468.<br />
58. Hannemann J, Oosterkamp HM, Bosch CAJ, et al. Changes in gene expression<br />
pr<strong>of</strong>iling due to primary chemotherapy in patients with locally advanced breast<br />
cancer. Proc Am Soc Clin Oncol 2004; 22(14S):502 (abstract).<br />
59. Modlich O, Prisack HB, Munnes M, et al. Immediate gene expression changes after<br />
the first course <strong>of</strong> neoadjuvant chemotherapy in patients with primary breast cancer<br />
disease. Clin <strong>Cancer</strong> Res 2004; 10(19):6418–6431.<br />
60. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors<br />
for breast cancer. N Engl J Med 2006; 355(6):560–569.<br />
61. Andre F, Hatzis C, Anderson K, et al. Microtubule-associated protein-tau is a<br />
bifunctional predictor <strong>of</strong> endocrine sensitivity and chemotherapy resistance in<br />
estrogen receptor-positive breast cancer. Clin <strong>Cancer</strong> Res 2007; 13(7):2061–2067.
7<br />
Gene Expression–Based Predictors<br />
<strong>of</strong> Prognosis and Response to<br />
Chemotherapy in <strong>Breast</strong> <strong>Cancer</strong><br />
Soonmyung Paik<br />
Division <strong>of</strong> Pathology, NSABP Foundation, Pittsburgh, Pennsylvania, U.S.A.<br />
INTRODUCTION<br />
With the explosion <strong>of</strong> genomic technology, numerous studies have demonstrated<br />
the potential clinical utility <strong>of</strong> gene expression pr<strong>of</strong>iles as a predictor <strong>of</strong> prognosis<br />
and response to therapy. Of particular interest is the demonstration that<br />
there is a general link between poor prognostic features and response to chemotherapy.<br />
In addition, linearity <strong>of</strong> RNA-based assays shows a strong promise<br />
that they can be very useful as predictive assays for targeted therapies.<br />
Since many excellent reviews have been written on this subject, this<br />
chapter will concentrate mainly on conceptual background to aid the clinical<br />
readers understand what is going on in the field rather than going into details.<br />
GENE EXPRESSION PROFILING PROVIDES<br />
PROGNOSTIC INFORMATION<br />
Gene expression pr<strong>of</strong>iling using microarray platforms allows interrogation <strong>of</strong><br />
expression levels <strong>of</strong> essentially all known genes in the tumor cells (1). In most<br />
studies, gross tumor rich area is homogenized to extract RNA. Therefore, the<br />
pr<strong>of</strong>ile that is achieved is mean expression levels <strong>of</strong> each gene among different<br />
97
98 Paik<br />
cell population in the tumor, including host stromal cells or inflammatory infiltrates.<br />
It is also important to realize that current methods do not have sensitivity to<br />
allow absolute measurement <strong>of</strong> gene expression levels <strong>of</strong> all genes printed on the<br />
microarray. Instead only about 50% <strong>of</strong> genes will have enough signal above<br />
background level (so-called present call). Different microarray platforms use DNA<br />
probes <strong>of</strong> various lengths (ranging from 22-mer oligonucleotide in Affymetrix<br />
GeneChip, 60-mer oligonucleotides for Agilent arrays, and average 1 kilobase for<br />
cDNA arrrays) representing different positions in each gene between the arrays.<br />
Sensitivity and dynamic range for measuring a specific gene could be widely<br />
different among different array platforms. Thus different studies using different<br />
platforms may result in identifying different set <strong>of</strong> genes representing same clinical<br />
phenotype (2). Obviously most studies also differ in the study population.<br />
Until recently microarray gene expression pr<strong>of</strong>iling required fresh or snap frozen<br />
tissue, seriously limiting the scale <strong>of</strong> studies conducted so far.<br />
It is not entirely surprising to find gene expression pr<strong>of</strong>iling to provide<br />
prognostic information, since after all morphology-based prognostic factors such<br />
as tumor histological grade is essentially end result <strong>of</strong> gene expression changes in<br />
the tumor cells. Two seminal papers using microarray gene expression pr<strong>of</strong>iling,<br />
one by Sorlie et al. (3) and the other by van ’t Veer et al. (4), clearly demonstrated<br />
the association <strong>of</strong> gene expression pr<strong>of</strong>iles and prognosis <strong>of</strong> patients diagnosed<br />
with early breast cancer. However, these two studies used very different<br />
approaches. In the seminal paper published in 2000, Perou et al. used unsupervised<br />
analysis <strong>of</strong> gene expression data and identified distinct biological subtypes<br />
(normal-like, luminal A and B, basal-like, and HER2) (5). Subsequently Sorlie<br />
et al. demonstrated that these subtypes confer differing prognosis with basal-like<br />
and HER2 associated with poor clinical outcome (3). On the other hand, van ’t<br />
Veer et al. conducted a supervised analysis to develop prognostic index based on<br />
expression levels <strong>of</strong> 70 genes identified from microarray analysis (4). This was<br />
then validated in a larger cohort (6). Recently Dr. Perou’s group has demonstrated<br />
that there is a significant overlap <strong>of</strong> information between the two approaches<br />
using same dataset—meaning those classified as good prognosis using 70-gene<br />
van ’t Veer set was found to be largely populated by luminal A subtype (2).<br />
While studies described above had enormous impact on how we view<br />
breast cancer, despite recent clearance <strong>of</strong> 70-gene Mammaprint 1 assay by the<br />
U.S. Food and Drug Administration (FDA), actual clinical implementation has<br />
been impeded by the need for fresh or snap frozen tumor tissue because <strong>of</strong> the<br />
requirement for highest quality RNA as a starting material for the microarray<br />
gene expression pr<strong>of</strong>iling.<br />
DEVELOPMENT AND VALIDATION OF OncotypeDx TM<br />
RECURRENCE SCORE ASSAY<br />
RNA isolated from formalin-fixed, paraffin-embedded tissue (FFPET) samples<br />
is a poor material for gene expression pr<strong>of</strong>iling. Masuda et al. analyzed the RNA<br />
extracted from FFPET for chemical modifications (2,7). In freshly made FFPET
Predictors <strong>of</strong> Prognosis and Response to Chemotherapy 99<br />
that were fixed and processed in ideal conditions (fixed in 10% buffered formalin<br />
at 48C), RNAs are fairly well preserved. Although the extracted RNAs did not<br />
show any sign <strong>of</strong> degradation compared with fresh samples, they were poor<br />
substrates for cDNA synthesis and subsequent polymerase chain reaction (PCR)<br />
amplification, so that only PCR amplification <strong>of</strong> short targets was possible. The<br />
investigators found addition <strong>of</strong> monomethylol (CH 2 OH) groups to all four bases<br />
to varying degrees and some adenines dimerized through metheylene bridging.<br />
In addition to the chemical modification, however, RNAs in FFPET continue to<br />
be degraded or fragmented over time during storage for reasons unclear. Cronin<br />
et al. systematically examined the quality <strong>of</strong> RNA extracted from breast cancer<br />
FFPET specimens taken at different times. RNA from FFPET archived for<br />
approximately 1 year were less fragmented than RNA archived for nearly 6 or<br />
17 years (8).<br />
There have been reports on the use <strong>of</strong> reverse transcription polymerase<br />
chain reaction (RT-PCR) to measure expression levels <strong>of</strong> few genes using RNA<br />
isolated from FFPET, but large-scale RT-PCR was not reported until recently.<br />
Cronin et al. from Genomic Health Inc. has invested on developing high quality<br />
controlled robotic assays for RT-PCR and published pro<strong>of</strong> <strong>of</strong> concept study<br />
(8). In the latter study, while RNAs isolated from old formalin fixed tissue were<br />
heavily fragmented with average size under 200 base pairs and absolute signal<br />
was much less for older samples, normalization <strong>of</strong> signal using a set <strong>of</strong> reference<br />
genes could largely correct these problems.<br />
Encouraged by these results, the National Surgical Adjuvant <strong>Breast</strong> and<br />
Bowel Project (NSABP) has engaged in a collaborative study with Genomic<br />
Health Inc. to develop prognostic gene expression assay for node negative (N )<br />
estrogen receptor–positive (ERþ) breast cancer treated with tamoxifen (9).<br />
While there are many other outstanding clinical questions that need to be<br />
addressed, we were compelled to first address the question <strong>of</strong> who among N<br />
ERþ tamoxifen treated patients need chemotherapy. In addition, there were two<br />
independent tamoxifen treated cohorts (tamoxifen arms <strong>of</strong> B-14 and B-20 trials)<br />
in the NSABP tissue bank so that discovery and validation could be performed<br />
without difficulty. We wanted to make sure that by the time <strong>of</strong> reporting, the<br />
assay was validated in a completely independent cohort. At that time, technology<br />
and cost limited Genomic Health investigators to examine only 250 genes<br />
maximum in RNA extracted from 60 mm thick section from a paraffin block.<br />
Therefore, existing cancer literature and microarray data were surveyed and<br />
250 candidate genes were selected. Before tumor blocks from B-20 tamoxifen<br />
treated cohort were examined, two other cohorts, one from mixed patient<br />
population and the other from those with more than 10 positive nodes were<br />
examined with the same assay (10). We did not believe that there will be different<br />
prognostic genes based on nodal status and therefore selected the prognostic<br />
genes that were equally prognostic across all three cohorts as a basis for<br />
prognostic model building. From the beginning, because <strong>of</strong> the continuous nature<br />
<strong>of</strong> RNA expression data, we believed strongly that a linear prediction model<br />
needed to be developed.
100 Paik<br />
Correlating clinical outcome with expression levels yielded many genes<br />
from these three studies, and 16 top-performing genes were identified for final<br />
model building and validation. Relative expression levels <strong>of</strong> the 16 genes are<br />
measured in relationship to average expression levels <strong>of</strong> 5 reference genes.<br />
While a majority <strong>of</strong> genes comprising 16 genes are ER (ER, PGR, BCL2,<br />
SCUBE2) and proliferation (Ki67, STK15, Survivin, CCNB1, MYBL2) related,<br />
there are other genes (HER2, GRB7, MMP11, CTSL2, GSTM1, CD68, BACG1)<br />
(9). The unscaled recurrence score (RSu) was calculated with the use <strong>of</strong> coefficient<br />
that are defined on the basis <strong>of</strong> regression analysis <strong>of</strong> gene expression.<br />
Recurrence score (RS) was rescaled from the unscaled recurrence score (Rsu) as<br />
follows: RS ¼ 0 if Rsu < 0; RS ¼ 20 (Rsu–6.7) if 0 Rsu 100; and RS ¼<br />
100 if Rsu > 100 (9). This resulted in RS ranging from 0 to 100.<br />
Final validation <strong>of</strong> the RS was achieved by examination <strong>of</strong> its performance<br />
in a completely independent cohort from NSABP trial B-14, which was not used<br />
in the model building process (9). The validation study was conducted with<br />
rigorous predefined statistical analysis plan with prespecified outcome end<br />
points and cut<strong>of</strong>fs for RS. The predefined low-risk group (RS below 18) had<br />
significant better prognosis compared with higher risk groups. More importantly,<br />
as shown in the published report, there is a linear relationship between<br />
RS and risk <strong>of</strong> distant failure at 10 years among N ERþ tamoxifen-treated<br />
patients (9).<br />
To test the applicability <strong>of</strong> RS in community oncology setting, Habel et al.<br />
conducted a nested case-control study in breast cancer patients diagnosed from<br />
1985 to 1994 at 14 Kaiser hospitals (11). Eligibility included negative nodes, age<br />
less than 75 years, and no chemotherapy. Cases died <strong>of</strong> breast cancer prior to<br />
2002. Up to three controls were matched to each case on age, race, tamoxifen<br />
treatment, facility, diagnosis year, and follow-up time. Among 4964 potentially<br />
eligible patients, 220 cases and 570 matched controls were identified. Median<br />
age was 59 years (range, 28–74 years); 30.9% were tamoxifen treated (mostly<br />
after 1988). Median follow-up time was 4.9 years for cases (time to death) and<br />
12.9 years for controls. RS was significantly associated with breast death in ERþ<br />
patients treated with or without tamoxifen (p ¼ 0.002). This study is important<br />
since it provides validation <strong>of</strong> RS assay used in a community setting.<br />
PREDICTION OF RESPONSE TO CHEMOTHERAPY<br />
Among gene expression–based markers, only RS has been tested whether it<br />
predicts response to chemotherapy in adjuvant setting (12). Since many <strong>of</strong> the<br />
genes comprising 21 genes in the RS assay are ER or proliferation related, we<br />
hypothesized early on that RS would be predictive <strong>of</strong> response to chemotherapy.<br />
Existence <strong>of</strong> chemotherapy arm in trial B-20 allowed us to ask this<br />
question (in fact this exactly was the reason why we first decided to work with<br />
N ERþ tamoxifen-treated cohort since we knew that we could not only discover<br />
and validate the prognostic index, but also test whether it predicts response
Predictors <strong>of</strong> Prognosis and Response to Chemotherapy 101<br />
to chemotherapy using banked materials readily available to us) (12). There were<br />
651 evaluable patients (227 randomized to tamoxifen and 434 randomized to<br />
tamoxifen plus chemotherapy). Patients with tumors who had high RS greater<br />
than 30 had a large absolute benefit <strong>of</strong> chemotherapy [with an absolute increase<br />
in distant recurrence free survival (DRFS) at 10 years <strong>of</strong> 27.6 8.0%, mean <br />
SE]. Patients with tumors who had low RS less than 18 derived minimal, if any,<br />
benefit from chemotherapy (with an absolute increase in DRFS at 10 years <strong>of</strong><br />
–1.1 2.2%, mean SE). The test for interaction between chemotherapy<br />
treatment and RS was statistically significant (p ¼ 0.038). Of particular<br />
importance is the finding that there is a linear relationship between RS and<br />
degree <strong>of</strong> benefit from chemotherapy when RS was examined as a continuous<br />
variable in Cox Model.<br />
While the relationship between RS and chemotherapy response has not<br />
been validated in an independent cohort as was done for prognostic aspect <strong>of</strong> the<br />
assay, supportive evidences exist to allow its use in clinical decision making.<br />
The most important data are from a cohort <strong>of</strong> patients treated with neoadjuvant<br />
chemotherapy at Milan reported by Gianni et al. (13). In this cohort, higher RS<br />
correlated with higher incidence <strong>of</strong> complete pathological response (pCR). In<br />
addition, due to the fact that many genes in RS are proliferation and ER related, we<br />
can make reasonable assumption that B-20 data are true. Since the decision to get<br />
chemotherapy has to be based on evaluation <strong>of</strong> baseline risk and assessment <strong>of</strong><br />
the degree <strong>of</strong> potential benefit from adding chemotherapy, it is reasonable to<br />
conclude that those with lower RS may not need chemotherapy since their<br />
baseline risk is low on the basis <strong>of</strong> B-14 data, and expected benefit is low on the<br />
basis <strong>of</strong> B-20 data. One headache is the fact that because <strong>of</strong> the continuous nature<br />
<strong>of</strong> the RS, it is difficult to pinpoint from which RS score patients start to gain<br />
benefit from chemotherapy. Clinical oncologists are not used to this kind <strong>of</strong> data.<br />
Uncertainties regarding the degree <strong>of</strong> benefit from chemotherapy in patients with<br />
intermediate range <strong>of</strong> RS resulted in launching a large randomized clinical trial<br />
named Trial Assigning IndividuaLized Options for Treatment (TAILORx) by<br />
North American <strong>Breast</strong> <strong>Cancer</strong> Intergroup (14). Many questioned about the<br />
reasons for the definition <strong>of</strong> the intermediate risk group in TAILORx trial being<br />
different from the definition used in the original publication in B-14 cohort. This<br />
was due to the fact that primary clinical end point in TAILORx is disease-free<br />
survival instead <strong>of</strong> distant disease-free survival and consideration <strong>of</strong> 95% confidence<br />
intervals in the baseline risk assessment. Again one has to realize that<br />
these cut<strong>of</strong>fs are totally arbitrary.<br />
PREDICTION OF RESPONSE TO NEOADJUVANT CHEMOTHERAPY<br />
Preoperative systemic therapy trials provide unique opportunities to assess<br />
sensitivity <strong>of</strong> tumor cells to systemic therapy in vivo while treatment is ongoing.<br />
Numerous studies have demonstrated the correlation <strong>of</strong> pCR and long-term<br />
outcome in breast cancer patients, potentially eliminating the need for long-term
102 Paik<br />
followup. Together with ease <strong>of</strong> obtaining fresh pretreatment core biopsies<br />
allowing high throughput assessment <strong>of</strong> molecular markers, preoperative trials<br />
may allow speedy discovery and validation <strong>of</strong> predictive markers for response to<br />
systemic therapy. So it is not surprising that many investigators attempted to<br />
utilize neoadjuvant setting to develop predictive markers <strong>of</strong> response to chemotherapy<br />
using microarray-based gene expression pr<strong>of</strong>iling. Regrettably the<br />
cumulative experience has so far been somewhat disappointing. No pr<strong>of</strong>ile has<br />
been found to have enough accuracy to be directly clinically applicable, although<br />
Hess et al. demonstrated that gene expression–based predictor still provided<br />
better prediction than clinical predictors (15). But more importantly, there has<br />
been no study with large enough sample size to provide conclusive answers as to<br />
whether one could reliably predict pCR using gene expression levels.<br />
It probably is somewhat naïve to expect gene expression pr<strong>of</strong>ile alone to<br />
predict pCR. For example, one would expect a smaller tumor would have higher<br />
chance <strong>of</strong> getting into pCR than a bigger tumor with same gene expression<br />
phenotype. Host factors such as drug metabolism and distribution may influence<br />
the chance for pCR. Prediction <strong>of</strong> combination regimens also may be more<br />
difficult than predicting response to single regimen. However, in the latter situation,<br />
because <strong>of</strong> low pCR rate, it is very difficult to develop predictor unless<br />
sample size is very high. In this regard, perhaps what investigators have achieved<br />
so far is as far as they can get, unless these other factors are integrated into<br />
algorithm development. In addition, pCR itself is not a perfect measure <strong>of</strong><br />
response either. As many as 80% <strong>of</strong> tumors show clinical response on taxanebased<br />
neoadjuvant chemotherapy, and therefore among those not achieving pCR,<br />
there is a continuous spectrum <strong>of</strong> residual burden in the breast. So it may make<br />
more sense to correlate gene expression with residual tumor burden than pCR,<br />
which has a somewhat arbitrary cut<strong>of</strong>f.<br />
Despite these limitations, certain consensus has emerged that are in<br />
agreement with what we found in B-20 study—that the tumors with poor<br />
prognostic signatures have higher chance <strong>of</strong> responding to chemotherapy. In<br />
addition to above described study by Gianni et al. (13), Rouzier et al. (using gene<br />
expression array) (16) and Carey et al. (using IHC surrogates <strong>of</strong> intrinsic subtypes)<br />
(17) has examined whether Perou’s intrinsic subtypes correlate with<br />
chance <strong>of</strong> pCR. As expected from B-20 data, basal and HER2 subtypes have<br />
higher chance <strong>of</strong> pCR compared to luminal subtypes. So there is a general<br />
agreement in the field that genetic program that determines the prognosis is<br />
tightly linked with general sensitivity to chemotherapy.<br />
ASSESSMENT OF RESIDUAL RISK AFTER CHEMOTHERAPY<br />
Despite the fact that gene expression data alone have not been shown to be able<br />
to predict pCR with high accuracy, gene expression pr<strong>of</strong>iling can be still<br />
important in patients treated with neoadjuvant chemotherapy. In an unpublished<br />
study, using pretreatment core biopsy samples from NSABP B-27, we found that
Predictors <strong>of</strong> Prognosis and Response to Chemotherapy 103<br />
the combination <strong>of</strong> prognostic pr<strong>of</strong>ile with pCR may be used to define residual<br />
clinical risk after chemotherapy, since those with pCR had a very good prognosis<br />
despite having a poor prognostic gene signature. This means that patients with<br />
poor prognostic gene signature who did not achieve pCR are at high risk <strong>of</strong><br />
recurrence after chemotherapy. Such data will allow identification <strong>of</strong> patients<br />
who will require more than chemotherapy without the need for waiting until they<br />
develop recurrences and opens door for a concept for post-neoadjuvant targeted<br />
therapy trials.<br />
CLINICAL UTILITY OF RS ASSAY<br />
How Is RS Compared to NCCN or St. Gallen Clinical Practice Guidelines?<br />
In B-14 study, less than 10% <strong>of</strong> the tamoxifen treated patients are classified as<br />
low risk by either NCCN or St. Gallen clincial guidelines. These patients did<br />
have excellent prognosis with less than 10% distant failure within 10 years. In<br />
contrast, RS classified 51% <strong>of</strong> the same cohort into low-risk category with very<br />
similar 10-year failure rate. Therefore 40% more ERþ tamoxifen-treated<br />
patients would be spared from chemotherapy if RS is used in comparison to<br />
NCCN or St. Gallen clinical guidelines. In addition, unlike NCCN and St. Gallen<br />
guidelines, which assumed that treatment benefit is equal in all subgroups,<br />
NSABP B-20 data showed that the benefit from chemotherapy is nearly zero in<br />
low-risk group defined by RS.<br />
How Is RS Compared to Adjuvant on Line Algorithm?<br />
The high cost <strong>of</strong> the RS assay (estimated at $3400) can only be justified by the<br />
additional information that it provides compared with other assays. Bryant and<br />
colleagues, in collaboration with Peter Ravdin, compared RS with Adjuvant on<br />
Line (AOL) in the NSABP B-14 cohort in a presentation made at the 2005<br />
St. Gallen <strong>Breast</strong> <strong>Cancer</strong> Symposium (personal communications with the<br />
late Dr. John Bryant). The AOL program provides individualized estimates <strong>of</strong><br />
recurrence risk on the basis <strong>of</strong> information such as patient age, tumor size, nodal<br />
involvement, and histological grade. It is actually not that straightforward to<br />
compare RS and AOL, since AOL does not provide risk scores. Therefore, it is<br />
difficult to utilize the numerical output from AOL when comparisons are made<br />
to RS. To compare the two, Dr. Bryant rank ordered output from AOL so that<br />
similar proportion <strong>of</strong> patients belong to low- (50%), intermediate- (25%), and<br />
high-risk (25%) categories as defined by RS. Rank-ordered AOL (R-AOL)<br />
performed remarkably well as a prognostic factor (in B-14) as well as predictive<br />
factor for benefit from chemotherapy (in B-20). RS and R-AOL were both<br />
independently prognostic in B-14 in multivariate analysis, suggesting that both<br />
gene expression signature and clinical features influence patient prognosis.<br />
However, agreement between the two assays was only 48%. RS was able to tease
104 Paik<br />
out patients with poor prognosis from the R-AOL low-risk category, so that the<br />
10-year distant relapse rate was 5.6% for patients categorized as low risk by both<br />
R-AOL and RS. R-AOL low-risk but RS intermediate- and high-risk patients had<br />
a 12.9% distant relapse rate at 10 years in the tamoxifen-treated cohort (p ¼<br />
0.04). For R-AOL intermediate- or high-risk patients, the distant relapse rate was<br />
8.9% when the RS risk was low, whereas the distant relapse rate was 30.7%<br />
when the RS risk was intermediate or high (p < 0.0001). These data suggest that<br />
the RS adds to existing prognostic markers and it would be ideal to incorporate<br />
RS and AOL into a single algorithm. However, at this point we don’t have a<br />
clear way to actually implement this finding into AOL in clinical use.<br />
Applicability in Node-Positive Patients<br />
One question about RS is whether it will work for Nþ ERþ patients. There is no<br />
reason why it should not work when it actually works even in 10þ node patients.<br />
However, risk estimates for RS will be different between N and Nþ patients<br />
although overall direction <strong>of</strong> the data would be the same, so one cannot simply<br />
apply the current RS algorithm to Nþ patients without a study.<br />
Categorical Vs. Continuous Variable<br />
One <strong>of</strong> the biggest problems in the field currently in my view is the use <strong>of</strong> RS as<br />
a categorical variable (low-, intermediate-, and high-risk categories), which was<br />
arbitrarily defined for validation study to ease the understanding <strong>of</strong> the data,<br />
because clinicians are not used to seeing a continuous data (9). It is very<br />
important to recognize the continuous nature <strong>of</strong> the RS. For example, a patient<br />
with score 17 (categorized as low risk) has nearly identical risk as a patient with<br />
score 19 (categorized as intermediate risk). On the other hand, a patient with RS<br />
<strong>of</strong> 3 would have much different prognosis than the one with RS <strong>of</strong> 17, although<br />
they are both categorized as low risk in the published report.<br />
FUTURE PERSPECTIVE<br />
The problem <strong>of</strong> current gene expression pr<strong>of</strong>iling methods is the need for fresh or<br />
snap frozen tissue and/or high cost. There are alternative methods that may be<br />
able to overcome these barriers. For example, Quantiplex assay (Panomics)<br />
based on combination <strong>of</strong> branched DNA amplification and fluorescence-labeled<br />
beads may allow multiplexing as many as 30 genes at a time in a 96 well plate<br />
with reasonable amount <strong>of</strong> input RNA extracted from FFPET. cDNA-mediated<br />
annealing, selection, extension, and ligation (DASL) assays from Illumina<br />
is another promising example (18). These assays cost fraction <strong>of</strong> the cost for<br />
RT-PCR or microarray-based methods and can utilize FFPET as starting materials.<br />
Replacement <strong>of</strong> standard markers such as ER and HER2 by these assays or<br />
QRT-PCR is desirable since they will provide more linear prediction <strong>of</strong> response.
Predictors <strong>of</strong> Prognosis and Response to Chemotherapy 105<br />
Availability <strong>of</strong> gene expression pr<strong>of</strong>iling methods for FFPET opens the<br />
possibility that archived materials from adjuvant chemotherapy trials can be<br />
analyzed, and therefore current approach <strong>of</strong> using gene expression analysis in<br />
neoadjuvant setting as a mean to identify predictive markers will become less<br />
important. The latter approach always will require validation in adjuvant setting<br />
since the FDA will not approve marker based on pCR as a surrogate—therefore<br />
it makes more sense to perform discovery and validation <strong>of</strong> predictive markers<br />
purely in the adjuvant setting.<br />
REFERENCES<br />
1. Schena M, Shalon D, Davis RW, et al. Quantitative monitoring <strong>of</strong> gene expression<br />
patterns with a complementary DNA microarray. Science 1995; 270:467–470.<br />
2. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors<br />
for breast cancer. N Engl J Med 2006; 355:560–569.<br />
3. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns <strong>of</strong> breast carcinomas<br />
distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A<br />
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4. van ’t Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression pr<strong>of</strong>iling predicts<br />
clinical outcome <strong>of</strong> breast cancer. Nature 2002; 415:530–536.<br />
5. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits <strong>of</strong> human breast tumours.<br />
Nature 2000; 406:747–752.<br />
6. van de Vijver MJ, He YD, van ’t Veer LJ, et al. A gene-expression signature as a<br />
predictor <strong>of</strong> survival in breast cancer. N Engl J Med 2002; 347:1999–2009.<br />
7. Masuda N, Ohnishi T, Kawamoto S, et al. Analysis <strong>of</strong> chemical modification <strong>of</strong> RNA<br />
from formalin-fixed samples and optimization <strong>of</strong> molecular biology applications for<br />
such samples. Nucleic Acids Res 1999; 27:4436–4443.<br />
8. Cronin M, <strong>Ph</strong>o M, Dutta D, et al. Measurement <strong>of</strong> gene expression in archival<br />
paraffin-embedded tissues: development and performance <strong>of</strong> a 92-gene reverse<br />
transcriptase-polymerase chain reaction assay. Am J Pathol 2004; 164:35–42.<br />
9. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence <strong>of</strong> tamoxifentreated,<br />
node-negative breast cancer. N Engl J Med 2004; 351:2817–2826.<br />
10. Cobleigh MA, Tabesh B, Bitterman P, et al. Tumor gene expression and prognosis in<br />
breast cancer patients with 10 or more positive lymph nodes. Clin <strong>Cancer</strong> Res 2005;<br />
11:8623–8631.<br />
11. Habel LA, Shak S, Jacobs MK, et al. A population-based study <strong>of</strong> tumor gene<br />
expression and risk <strong>of</strong> breast cancer death among lymph node-negative patients.<br />
<strong>Breast</strong> <strong>Cancer</strong> Res 2006; 8:R25.<br />
12. Paik S, Tang G, Shak S, et al. Gene expression and benefit <strong>of</strong> chemotherapy in women<br />
with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 2006;<br />
24:3726–3734.<br />
13. Gianni L, Zambetti M, Clark K, et al. Gene expression pr<strong>of</strong>iles in paraffin-embedded<br />
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14. Sparano JA. TAILORx: trial assigning individualized options for treatment (Rx).<br />
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15. Hess KR, Anderson K, Symmans WF, et al. <strong>Ph</strong>armacogenomic predictor <strong>of</strong> sensitivity<br />
to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide<br />
in breast cancer. J Clin Oncol 2006; 24:4236–4244.<br />
16. Rouzier R, Perou CM, Symmans WF, et al. <strong>Breast</strong> cancer molecular subtypes respond<br />
differently to preoperative chemotherapy. Clin <strong>Cancer</strong> Res 2005; 11:5678–5685.<br />
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chemosensitivity <strong>of</strong> breast cancer subtypes. Clin <strong>Cancer</strong> Res 2007; 13:2329–2334.<br />
18. Bibikova M, Talantov D, Chudin E, et al. Quantitative gene expression pr<strong>of</strong>iling in<br />
formalin-fixed, paraffin-embedded tissues using universal bead arrays. Am J Pathol<br />
2004; 165:1799–1807.
8<br />
Utilization <strong>of</strong> Genomic Signatures for<br />
Personalized Treatment <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong><br />
P. Kelly Marcom, Carey K. Anders,<br />
Ge<strong>of</strong>frey S. Ginsburg, and Anil Potti<br />
Duke Institute for Genome Sciences & Policy, Department <strong>of</strong> Medicine,<br />
Duke University Medical Center, Durham, North Carolina, U.S.A.<br />
Joseph R. Nevins<br />
Duke Institute for Genome Sciences & Policy, Department <strong>of</strong> Molecular<br />
Genetics and Microbiology, Duke University Medical Center, Durham,<br />
North Carolina, U.S.A.<br />
INTRODUCTION<br />
The challenge <strong>of</strong> personalized cancer treatment lies in matching the most effective<br />
therapy to the characteristics <strong>of</strong> an individual patient’s tumor. The selection<br />
<strong>of</strong> therapy for both early- and late-stage breast cancer, as is the case for most<br />
cancer therapy, is still largely empiric and guided by large, population-based,<br />
randomized clinical trials. Although widely adopted, this approach is inadequate for<br />
the selection <strong>of</strong> individualized chemotherapy regimens. Estimates <strong>of</strong> benefit are<br />
extrapolations from the effects observed in these large trials and are not necessarily<br />
applicable to individual patients.<br />
The use <strong>of</strong> genomic data, in particular gene expression pr<strong>of</strong>iles derived from<br />
DNA microarray analysis, has the promise <strong>of</strong> providing the basis for truly individualized<br />
therapy. This approach has the potential to transform the treatment <strong>of</strong><br />
107
108 Marcom et al.<br />
cancer from a population-based approach to an individualized one directed toward<br />
unique patient characteristics. This includes the use <strong>of</strong> gene expression data to<br />
identify subtypes <strong>of</strong> cancer not previously recognized by traditional methods <strong>of</strong><br />
analysis. Pr<strong>of</strong>iling genomic patterns to identify new subclasses <strong>of</strong> tumors has<br />
previously been described in distinguishing differences between acute myeloid<br />
leukemia and acute lymphoblastic leukemia (1,2). Similar studies have employed<br />
gene expression data to identify distinct subtypes <strong>of</strong> breast cancer with prognostic<br />
significance (3).<br />
A landmark study in breast cancer published in 2002 described the use <strong>of</strong> a<br />
DNA microarray-based, 70-gene expression pr<strong>of</strong>ile as a prognostic factor in<br />
breast cancer (4). This genomic marker has demonstrated predictive value<br />
stratifying stage I and II breast cancer patients into two broad subgroups, relatively<br />
high-risk versus low-risk groups as defined by a 10-year risk <strong>of</strong> breast<br />
cancer recurrence. Various other studies have described the development <strong>of</strong><br />
expression pr<strong>of</strong>iles that have prognostic value in breast cancer (5–9).<br />
The Netherlands 70-gene predictor has provided the basis for an expanded<br />
European study that aims to measure its performance in a more diverse set <strong>of</strong><br />
patients (10). The parallel, prospective clinical trial MINDACT (Microarray In<br />
Node-negative Disease may Avoid ChemoTherapy) aims to measure the effectiveness<br />
<strong>of</strong> this genomic predictor in guiding adjuvant chemotherapy when<br />
compared to predictions based solely on the traditional clinicopathologic features.<br />
Likewise, a large prospective trial in the United States, TAILORx (Trial<br />
Assigning IndividuaLized Options for Treatment), is currently evaluating the<br />
Oncotype DX test (Genomic Health, Inc., Redwood City, California, U.S.),<br />
a polymerase chain reaction (PCR)-based assay <strong>of</strong> 21 genes correlated with<br />
10-year breast cancer recurrence risk (11), to guide the administration <strong>of</strong> hormonal<br />
therapy with or without chemotherapy among patients with estrogen<br />
receptor (ER)-positive, early-stage breast cancer.<br />
Of equal, if not greater, importance in achieving the goal <strong>of</strong> personalized<br />
treatment <strong>of</strong> breast cancer is the ability to predict response to specific chemotherapeutics,<br />
particularly the standard-<strong>of</strong>-care cytotoxic regimens incorporated<br />
into routine clinical practice. Various studies have described approaches to<br />
developing genomic predictors <strong>of</strong> response to chemotherapeutic agents promising<br />
a more effective strategy <strong>of</strong> assigning chemotherapeutic regimens to<br />
patients, as guided by unique characteristics <strong>of</strong> their breast tumors (12–14).<br />
The overall aim <strong>of</strong> this chapter is to highlight both opportunities presented<br />
by recent advances in the development <strong>of</strong> genomic-based approaches within the<br />
field <strong>of</strong> breast cancer and corresponding strategies for implementation <strong>of</strong> these<br />
techniques into clinical practice. Rather than relying on historic paradigms <strong>of</strong><br />
chemotherapeutic selection as guided by broad, population-based studies, the<br />
goal <strong>of</strong> this unique methodology is to improve patient outcome, and ultimately<br />
patient survival, through the optimal selection <strong>of</strong> patient-specific chemotherapeutic<br />
agents as guided by genomic-based strategies.
Genomic Signatures for Personalized Treatment <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> 109<br />
CYTOTOXIC THERAPIES FOR EARLY-STAGE BREAST CANCER<br />
Currently, the standard regimens for the adjuvant treatment <strong>of</strong> early-stage breast<br />
cancer combine anthracyclines, taxanes, and cyclophosphamide in limited permutations<br />
<strong>of</strong> combination, dose, and administration schedule. The addition <strong>of</strong> a<br />
taxane, either sequentially or in combination, to a core anthracycline/cyclophosphamide<br />
regimen in the setting <strong>of</strong> node-positive breast cancer has been<br />
shown to improve disease-free survival (DFS) by 4% to 7% in four separate<br />
randomized trials (15–18). However, it is unclear whether all breast cancer<br />
subtypes are equally affected by this therapeutic approach. Recent work examining<br />
a series <strong>of</strong> breast cancer adjuvant trials conducted over the past two decades<br />
further confirms the importance <strong>of</strong> defining breast cancer subtypes and their<br />
relationship to treatment benefit. This work demonstrated a lack <strong>of</strong> benefit for<br />
this broad treatment approach across a subset <strong>of</strong> ER-positive patients (19).<br />
Optimizing chemotherapy outcomes through the manipulation <strong>of</strong> dosing<br />
schedules remains controversial. In CALGB 9741 (<strong>Cancer</strong> and Leukemia Group<br />
B Intergroup 9741), decreasing the dosing interval from every three weeks to<br />
every two weeks [i.e., dose-dense (DD) scheduling] was shown to improve DFS<br />
and overall survival (OS) at three-year median follow-up [risk ratio (RR) ¼ 0.74,<br />
p ¼ 0.010] (20). However, an update at five-year median follow-up showed a<br />
less impressive overall improvement in DFS (RR ¼ 0.80, p ¼ 0.012) and<br />
perhaps no improvement in ER-positive disease (21). Moreover, a randomized<br />
<strong>Ph</strong>ase III trial from the Gruppo Oncologico Nord Ovest–Mammella Inter<br />
Gruppo (GONO–MIG) examining DD scheduling <strong>of</strong> a non-taxane-containing<br />
regimen did not show a benefit in DFS or OS (22). In an accompanying editorial<br />
to this publication, the ambiguity regarding the benefit <strong>of</strong> DD therapy<br />
was acknowledged and emphasis was placed on the need to define biologically<br />
relevant breast cancer subtypes so that individualized treatment strategies could<br />
be developed (23).<br />
A ROLE FOR PREOPERATIVE SYSTEMIC CHEMOTHERAPY<br />
TO EVALUATE GENOMIC STRATEGIES<br />
Preoperative, systemic chemotherapy (PST), also known as neoadjuvant therapy,<br />
is an effective means for assessing the sensitivity <strong>of</strong> an individual breast tumor<br />
to chemotherapy. Multiple trials <strong>of</strong> preoperative systemic chemotherapy have<br />
been performed. Initially, this approach was primarily used for locally advanced,<br />
inoperable, or inflammatory breast cancer. However, subsequent trials have<br />
evaluated PST in the setting <strong>of</strong> smaller, operable breast tumors both to increase<br />
breast conservation rates and to assess sensitivity to chemotherapy.<br />
Over the past two decades, PST for early-stage breast cancer has been<br />
shown to be a safe and effective method for assessing the chemosensitivity <strong>of</strong><br />
primary breast cancers. Sequencing systemic therapy first, with the consequent
110 Marcom et al.<br />
three- to six-month delay in surgery, has not been shown to adversely affect<br />
outcomes (24). Assessment <strong>of</strong> pathologic response to PST, both in the breast and<br />
regional lymph nodes, provides the most powerful prognostic information<br />
available. Patients who obtain a complete pathologic response in the breast and<br />
lymph nodes have a greater than 95% survival at seven years (24,25). Although<br />
PST provides a powerful tool for in vivo assessment <strong>of</strong> chemosensitivity,<br />
exploiting the information gained in the aforementioned trials to improve<br />
management <strong>of</strong> individual patients has been more difficult.<br />
Studies employing a PST approach have yielded conflicting results illustrating<br />
the heterogeneity <strong>of</strong> primary breast tumors and responsiveness to different<br />
chemotherapeutic agents. This observation begs the question on whether<br />
or not breast tumors commonly possess intrinsic ‘pan-chemotherapy’ responsiveness<br />
or resistance. For example, patients responsive to preoperative treatment<br />
with CVAP (Cytoxan, vincristine, Adriamycin, and prednisone) were<br />
either continued on the same regimen or crossed over to docetaxel (26).<br />
Responding patients who received docetaxel had a significantly higher pathologic<br />
complete response (pCR) rate than those who continued on the CVAP<br />
regimen, supporting differential breast tumor sensitivity to anthracyclines and<br />
taxanes. Additionally, the pCR rate in the NSABP (National Surgical Adjuvant<br />
<strong>Breast</strong> and Bowel Project) B-27 clinical trial was doubled by the addition <strong>of</strong><br />
preoperative docetaxel to preoperative doxorubicin/cyclophosphamide (AC)<br />
chemotherapy (26.1% vs. 12.9%, p < 0.0001) supporting this conclusion.<br />
However, the addition <strong>of</strong> preoperative docetaxel did not yield a statistically<br />
significant difference in overall DFS when added to preoperative AC alone<br />
[hazard ratio (HR) ¼ 0.9, p ¼ 0.22]. The question remains open on whether<br />
patients only partially responsive to AC, who achieved a pCR following<br />
administration <strong>of</strong> docetaxel, might have achieved a pCR with docetaxel alone,<br />
thus avoiding the risk <strong>of</strong> cardiotoxicity inherent to anthracyclines. Unfortunately,<br />
this study is somewhat flawed by the concurrent administration <strong>of</strong> tamoxifen<br />
with chemotherapy in patients with known ER-positive breast tumors (25). An<br />
additional study by the GEPARDUO (German Preoperative Adriamycin Docetaxel)<br />
study compared DD doxorubicin/docetaxel (AT) with AC followed by<br />
docetaxel, illustrating improved response among patients receiving cyclophosphamide.<br />
This study, however, was more widely interpreted as a comparison<br />
<strong>of</strong> sequential versus DD therapy (27).<br />
The largest trials investigating response to PST with AC come from the<br />
NSABP B-18 and the aforementioned B-27 clinical trials, where the pCR rates,<br />
allowing for residual in situ disease, were consistently approximately 13%<br />
(25,28). Data for single-agent paclitaxel are more limited; however, a study<br />
conducted at the M.D. Anderson <strong>Cancer</strong> Center reported pCR rates <strong>of</strong> 6% when<br />
excluding in situ diseases (29). A comprehensive review <strong>of</strong> these and other PST<br />
trials supports that the pCR rate in the breast with most doublet chemotherapies<br />
is approximately 10% to 15% (24). The National Comprehensive <strong>Cancer</strong>
Genomic Signatures for Personalized Treatment <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> 111<br />
Network (NCCN) guidelines support that standard adjuvant regimens can also be<br />
used as preoperative therapy (http://www.nccn.org).<br />
Although response to PST is a powerful tool for assessing tumor chemosensitivity,<br />
it is a post hoc evaluation and requires the exposure <strong>of</strong> individual<br />
patients to toxic and potentially ineffective therapies. Consequently, it has been<br />
difficult to exploit the knowledge gained about both tumor sensitivity and<br />
resistance to the patient’s benefit. In addition, to our knowledge, no studies have<br />
examined the influence <strong>of</strong> PST on the postoperative management <strong>of</strong> patients who<br />
have minimal or no response to PST.<br />
The relative importance <strong>of</strong> including either an anthracycline or taxane or<br />
both as part <strong>of</strong> the adjuvant treatment regimen <strong>of</strong> early stage breast cancer is an<br />
area <strong>of</strong> active research. These two classes <strong>of</strong> agents, in combination with<br />
cyclophosphamide, provide the foundation for the treatment <strong>of</strong> early-stage breast<br />
cancer, and several clinical trials provide insight into the current use <strong>of</strong> these<br />
agents. A recently published randomized phase III trial compared AC with<br />
docetaxel/cyclophosphamide (TC) (30). While this trial demonstrated improvement<br />
in DFS favoring TC (86% vs. 80%, p ¼ 0.015), the confidence interval (CI)<br />
approached 1.0 (CI ¼ 0.50–0.94) and there was no difference in OS despite<br />
median follow-up <strong>of</strong> 5.5 years. Interestingly, a previous trial comparing AT with<br />
AC showed no difference in DFS at four years (31). Although both <strong>of</strong> these trials<br />
enrolled a lower-risk population <strong>of</strong> patients, reconciling differences in outcome<br />
across these two studies is difficult. An ongoing trial in patients with fewer than<br />
four positive axillary nodes is comparing AC with single-agent paclitaxel as<br />
adjuvant therapy (CALGB 40101), in part on the basis <strong>of</strong> responses observed in<br />
the aforementioned PST trials. In summary, although many active chemotherapeutic<br />
agents suitable for the treatment <strong>of</strong> early-stage breast cancer exist,<br />
data available in 2007 do not provide the necessary guidance to select the most<br />
effective chemotherapeutic agent for each unique patient. This observation<br />
speaks to the importance <strong>of</strong> developing novel methods employing genomic<br />
analyses <strong>of</strong> tumor biology to individualize and optimize patient care.<br />
GENE EXPRESSION PROFILES TO PREDICT THERAPEUTIC RESPONSE<br />
Given the current status <strong>of</strong> neoadjuvant and adjuvant chemotherapy for breast<br />
cancer, an opportunity exists to use genomic information to improve the selection<br />
<strong>of</strong> patients who would derive the greatest benefit from treatment with cytotoxic<br />
chemotherapy. That is, prognostic tools that can identify patients clearly in need <strong>of</strong><br />
further treatment can help to refine the decisions based now on clinical information.<br />
Additionally, the development <strong>of</strong> predictive signatures specific for<br />
response to individual cytotoxic agents would provide an invaluable clinical tool<br />
to enable the better use <strong>of</strong> available standard-<strong>of</strong>-care agents. Historically, predictive<br />
biomarkers in the setting <strong>of</strong> breast cancer have played an important role in<br />
selecting patients for targeted therapies (i.e., trastuzumab and endocrine therapies
112 Marcom et al.<br />
including tamoxifen and aromatase inhibitors). However, validated biomarkers for<br />
commonly used cytotoxic agents are not yet clinically available. The importance<br />
<strong>of</strong> cytotoxic agents, even with the advent <strong>of</strong> the targeted molecular therapy era,<br />
cannot be discounted. To date, many targeted therapies demonstrate greatest<br />
efficacy when combined with chemotherapeutic agents. To address this relevant<br />
clinical need, a variety <strong>of</strong> studies have been performed with the aim <strong>of</strong> developing<br />
predictors <strong>of</strong> sensitivity for a spectrum <strong>of</strong> cytotoxic chemotherapies.<br />
Gene expression pr<strong>of</strong>iling <strong>of</strong> breast cancers in the context <strong>of</strong> preoperative<br />
chemotherapy has shown promise in providing predictive signatures <strong>of</strong> response<br />
to specific agents (12–14). The availability <strong>of</strong> clinical datasets provides an<br />
opportunity to move ahead with the prospective assessment <strong>of</strong> gene expression<br />
pr<strong>of</strong>iling as a means for guiding PST. This methodology can also simultaneously<br />
provide insight into breast cancer biology, as the investigation <strong>of</strong> pr<strong>of</strong>iles will<br />
shed light on the deregulation <strong>of</strong> molecular pathways.<br />
Several studies have correlated breast cancer gene expression pr<strong>of</strong>iles with<br />
response to preoperative chemotherapy. Chang et al. performed gene expression<br />
pr<strong>of</strong>iling on locally advanced breast cancers treated with docetaxel to identify<br />
genes predictive <strong>of</strong> response to this therapeutic modality (32). Similar work has<br />
been done with preoperative doxorubicin therapy and sequential taxane/anthracycline<br />
therapy (33,34). While some studies have suggested that single markers<br />
such as NF-kB may predict resistance to anthracycline therapy, most have found<br />
that a multigene classifier is needed. In comparing a signature for doxorubicin/<br />
cyclophosphamide sensitivity with a signature for docetaxel sensitivity, Cleator<br />
et al. concluded that unique signatures for sensitivity to different chemotherapy<br />
regimens are likely to exist (33).<br />
More recently, studies <strong>of</strong> PST have examined the pCR rates for clinically<br />
and molecularly defined breast cancer subtypes. These retrospective analyses <strong>of</strong><br />
PST trials demonstrate marked differences in pCR rates depending on breast<br />
cancer subtype (35,36). A recent publication by the M.D. Anderson <strong>Breast</strong> Group<br />
has retrospectively defined gene expression pr<strong>of</strong>iles that predict response to a<br />
sequential regimen <strong>of</strong> paclitaxel (Taxol) followed by 5-fluorouracil, doxorubicin<br />
(Adriamycin), and cyclophosphamide (T-FAC) (13).<br />
As an alternate strategy, drug sensitivity data from the NCI-60 panel <strong>of</strong><br />
cancer cell lines, coupled with baseline Affymetrix gene expression data unique<br />
to these cells, has been utilized to generate a series <strong>of</strong> gene expression signatures<br />
with the potential to predict sensitivity to various chemotherapeutic agents (37)<br />
(Fig. 1). To test the capacity <strong>of</strong> the in vitro docetaxel sensitivity predictor to<br />
accurately identify the patients who responded to docetaxel, we evaluated data<br />
from a previously published neoadjuvant study that linked gene expression data<br />
with clinical response to docetaxel (32). The in vitro–generated pr<strong>of</strong>ile correctly<br />
predicted docetaxel response in 22 <strong>of</strong> 24 patient samples, achieving an overall<br />
accuracy <strong>of</strong> 91.6%. Findings were further validated in an ovarian cancer dataset,<br />
where accuracy for predicting the response to salvage docetaxel therapy<br />
exceeded 85% (37).
Genomic Signatures for Personalized Treatment <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> 113<br />
Figure 1 Signatures that predict response to cytotoxic chemotherapies. Heatmaps <strong>of</strong><br />
expression pr<strong>of</strong>iles selected to predict sensitivity to the indicated chemotherapies. Each<br />
column in the figure represents individual samples. Each row represents an individual<br />
gene, ordered from top to bottom according to regression coefficients. Shown below is<br />
prediction <strong>of</strong> single agent therapy response in patient samples using cell line based<br />
expression signatures <strong>of</strong> chemosensitivity. Source: Adapted from Potti et al., Nature<br />
Medicine 12:1294 –1300, 2006.<br />
Given that the in vitro developed gene expression pr<strong>of</strong>iles predicted<br />
clinical response to docetaxel, the approach has been extended to test the ability<br />
<strong>of</strong> additional signatures to predict response to commonly used salvage therapies<br />
in an independent dataset <strong>of</strong> ovarian cancer patients previously treated with<br />
Adriamycin. As shown in Figure 1B, each <strong>of</strong> these predictors was capable <strong>of</strong><br />
accurately predicting the response to the drugs in patient samples, achieving an<br />
accuracy in excess <strong>of</strong> 81% overall. Importantly, in these examples, the overall<br />
clinical response rate varied from 25% to 45%. Using the expression signatures<br />
to predict patients likely to respond would essentially increase the “effective”<br />
response rate (the positive predictive value for chemosensitivity for any given<br />
drug) to greater than 85%, by selecting those patients likely to respond, based on<br />
the drug sensitivity predictor. These data suggest that the genomic-based predictors<br />
<strong>of</strong> chemotherapeutic response provide a unique opportunity to guide the<br />
selection <strong>of</strong> a cytotoxic agent most effective for an individual patient—the<br />
essence <strong>of</strong> personalized medicine.<br />
Importantly, it is also evident from further analyses that while there are<br />
overlaps in the predicted sensitivities to the chemotherapeutic agents among<br />
ovarian patients, there are also distinct groups <strong>of</strong> patients that are predicted to be<br />
sensitive to various single-agent salvage therapies (Fig. 2). Many therapeutic<br />
regimens make use <strong>of</strong> combinations <strong>of</strong> chemotherapeutic cytotoxic agents,<br />
raising a question on the extent that signatures <strong>of</strong> individual therapeutic response<br />
will also predict response to a combination <strong>of</strong> agents. To address this question,
114 Marcom et al.<br />
Figure 2 Patterns <strong>of</strong> predicted sensitivity to commonly used chemotherapies. A<br />
collection <strong>of</strong> breast cancer samples represented by microarray data was used to predict<br />
the likely sensitivity to the indicated agents based on the signatures at the top <strong>of</strong> the<br />
figure. Predictions are plotted as a heatmap representing probability <strong>of</strong> sensitivity.<br />
Adapted from Potti et al., Nature Medicine 12:1294 –1300, 2006.<br />
data from a breast neoadjuvant trial were utilized to evaluate the ability <strong>of</strong> singleagent<br />
signatures for T-FAC to predict patient response measured by pCR (34).<br />
Utilization <strong>of</strong> individual chemotherapy sensitivity signatures to assess patient<br />
response illustrated a significant distinction between the responders (n ¼ 13) and<br />
nonresponders (n ¼ 38), with the exception <strong>of</strong> 5-flourouracil. Importantly, the<br />
combined probability <strong>of</strong> sensitivity to all four agents in the T-FAC neoadjuvant<br />
regimen was calculated using the probability theorem. It was clear from this<br />
analysis that the prediction <strong>of</strong> response based on a combined probability <strong>of</strong><br />
sensitivity, built from individual chemosensitivity predictions, yielded a statistically<br />
significant (p < 0.0001, Mann–Whitney U test) distinction between the<br />
responders and nonresponders.<br />
A STRATEGY FOR USING CHEMOTHERAPY<br />
RESPONSE PREDICTORS IN PST<br />
Although promising, studies to date have included post hoc gene expression<br />
pr<strong>of</strong>iling <strong>of</strong> tumor samples. The ability to effectively select the most appropriate<br />
preoperative systemic cytotoxic agents for the treatment <strong>of</strong> early-stage breast<br />
cancer must ultimately be tested and validated in prospective clinical trials. This<br />
rationale approach will not only provide an opportunity to confirm biologically<br />
relevant subtypes, but also establish their predictive capacities regarding<br />
response to specific, conventional cytotoxic agents.
Genomic Signatures for Personalized Treatment <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> 115<br />
The development <strong>of</strong> genomic signatures predictive <strong>of</strong> response to commonly<br />
employed chemotherapeutics <strong>of</strong>fers an opportunity to significantly<br />
expand the identification <strong>of</strong> combinations <strong>of</strong> cytotoxic agents best suited for an<br />
individual patient. With this in mind, several prospective clinical studies have<br />
been proposed to test the capability <strong>of</strong> genomic predictors to most effectively<br />
match cytotoxic agents with individual patients. This concept is best evaluated in<br />
the context <strong>of</strong> neoadjuvant trials where hypotheses regarding the effectiveness <strong>of</strong><br />
genomic predictions can be accurately assessed through pathologic measurements<br />
<strong>of</strong> tumor response.<br />
We note that the availability <strong>of</strong> chemotherapy response predictors provides<br />
an opportunity to employ genomic predictors in present-day practice, where<br />
patients are commonly treated with one or more therapeutic regimens <strong>of</strong> equal<br />
efficacy (Fig. 3). The ability to select the most effective therapy, among a panel<br />
<strong>of</strong> standard-<strong>of</strong>-care agents, is a rational strategy because standard treatment<br />
procedures would not be greatly altered. The goal <strong>of</strong> these trials would be to<br />
more effectively utilize currently available standard agents. For example, a<br />
breast neoadjuvant study has been designed and is currently being implemented<br />
by employing this approach. Patients with early-stage breast cancer will be<br />
treated with one <strong>of</strong> two current standard-<strong>of</strong>-care therapies for which genomic<br />
predictors are available. This study design would compare the response rates<br />
for the randomly or doctor-selected A or B regimens versus assignment to<br />
therapy A or B via genomic predictor <strong>of</strong> response. The success <strong>of</strong> this study<br />
could then set the stage for larger studies utilizing genomic predictors <strong>of</strong><br />
chemotherapy sensitivity to more accurately predict response to cytotoxic<br />
therapies. The next phase <strong>of</strong> clinical studies would then focus on novel therapeutics<br />
(i.e., targeted therapies) for the individual patients unlikely to respond<br />
to either regimen.<br />
Figure 3 Schematic diagram <strong>of</strong> prospective clinical trial that would evaluate the<br />
capacity <strong>of</strong> predictors <strong>of</strong> chemotherapy response to improve response rates.
116 Marcom et al.<br />
OTHER GENOMIC OPPORTUNITIES TO GUIDE<br />
BREAST CANCER THERAPEUTICS<br />
The successful development and validation <strong>of</strong> predictors <strong>of</strong> response to standard<br />
chemotherapeutics will foster the more efficient use <strong>of</strong> these agents through the<br />
identification <strong>of</strong> patients with the greatest likelihood <strong>of</strong> response. Concurrently,<br />
these strategies will also identify a subgroup <strong>of</strong> patients resistant to all available<br />
standard-<strong>of</strong>-care chemotherapeutics, necessitating the implementation <strong>of</strong> targeted<br />
strategies. This is a critical point as it is <strong>of</strong> little benefit to patients to<br />
identify resistance if a plan for alternative therapeutic strategies does not exist.<br />
Currently, most patients resistant to standard chemotherapeutic agents are traditionally<br />
treated with second- or third-line chemotherapeutic agents or are<br />
enrolled on a clinical trial. The more effective alternative is to identify patients<br />
likely to be resistant prior to initiation <strong>of</strong> therapy so that opportunities for<br />
alternative treatments might be employed in the first-line setting.<br />
A viable option is to exploit the wealth <strong>of</strong> pathway-specific therapeutics<br />
that have been developed over the past two decades. To this goal, gene<br />
expression patterns reflecting deregulation <strong>of</strong> oncogenic pathways have been<br />
developed, as depicted in Figure 4 (38). These oncogenic pathway signatures<br />
have been shown to accurately predict the status <strong>of</strong> pathway deregulation in a<br />
series <strong>of</strong> both tumors derived from murine models and human tumors.<br />
These results demonstrate an ability to predict pathway pr<strong>of</strong>iles unique to<br />
individual human tumors, thus characterizing the genetic events associated with<br />
tumor development. Importantly, the assay <strong>of</strong> gene expression pr<strong>of</strong>iles provides<br />
a measure <strong>of</strong> the consequence <strong>of</strong> the oncogenic process, irrespective <strong>of</strong> how the<br />
pathway might have been altered. Thus, even if the known oncogene is not<br />
mutated, but rather another component <strong>of</strong> the pathway is altered, the gene<br />
expression pr<strong>of</strong>ile will detect this alteration.<br />
The analysis <strong>of</strong> oncogenic pathways through gene expression pr<strong>of</strong>iling<br />
<strong>of</strong>fers an opportunity to identify new therapeutic options for patients by providing<br />
a potential basis for guiding the use <strong>of</strong> pathway-specific drugs. To<br />
demonstrate the value <strong>of</strong> pathway prediction to guide drug selection, pathway<br />
deregulation was mapped among a series <strong>of</strong> breast cancer cell lines to screen<br />
potential therapeutic drugs. In parallel with pathway status mapping, the cell<br />
lines were treated with a variety <strong>of</strong> drugs known to target specific activities<br />
within given oncogenic pathways. A variety <strong>of</strong> available reagents known to<br />
target oncogenic pathways for which signatures have been developed [i.e., a farnesyl<br />
transferase inhibitor (FTS) and an Src inhibitor (SU6656)] were employed.<br />
In each case, a clear relationship was demonstrated between prediction <strong>of</strong> pathway<br />
deregulation and sensitivity to the respective targeted therapeutic. These preliminary,<br />
yet promising, results have been further validated in a larger set <strong>of</strong> 50 cancer<br />
cell lines, including lung, ovarian, and melanoma tumor types.<br />
Taken together, these data demonstrate a potential approach to identify<br />
therapeutic options for chemotherapy-resistant patients, as well as identify novel
Genomic Signatures for Personalized Treatment <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> 117<br />
Figure 4 Patterns <strong>of</strong> predicted cell signaling pathway activation. A collection <strong>of</strong><br />
breast cancer samples represented by microarray data was used to predict the likely state<br />
<strong>of</strong> activation <strong>of</strong> the indicated cell signaling pathways based on the signatures at the top <strong>of</strong><br />
the figure developed to represent pathway activation. Predictions are plotted as a heatmap<br />
representing probability <strong>of</strong> pathway activation. A Kaplan-Meier survival analysis is<br />
shown at the bottom reflecting the overall survival for patients in each cluster defined by<br />
pathway patterns. Source: Adapted from Bild et al., Nature 439:353–357, 2006.<br />
combinations for chemotherapy-sensitive patients. This unique approach represents<br />
a potential strategy to optimize effective treatments for cancer patients and<br />
warrants future, prospective, validations trials in both the adjuvant and metastatic<br />
settings.<br />
Pathway signatures have the potential to identify patients most sensitive<br />
to standard-<strong>of</strong>-care cytotoxic drugs, as well as those resistant to standard<br />
regimens. This approach creates an opportunity to combine chemotherapy<br />
sensitivity pr<strong>of</strong>iles with concurrent oncogenic pathway deregulation within an<br />
individual patient’s tumor. As depicted in Figure 5, predictions <strong>of</strong> sensitivity to
118 Marcom et al.<br />
Figure 5 Combining information regarding status <strong>of</strong> individual patient’s tumors.<br />
The figure depicts an analysis <strong>of</strong> a set <strong>of</strong> breast tumors for predicted sensitivity to various<br />
chemotherapies (top figure) and then the pattern <strong>of</strong> pathway activation in relation to one<br />
<strong>of</strong> these (docetaxel sensitivity).<br />
various cytotoxic agents can be combined with predictions <strong>of</strong> oncogenic<br />
pathway deregulation with the goal <strong>of</strong> defining the most optimal regimen. For<br />
example, a patient whose tumor is resistant to docetaxel may concurrently<br />
illustrate a high probability <strong>of</strong> Src pathway deregulation. Src inhibitors are<br />
currently being tested in clinical trials in a variety <strong>of</strong> solid tumors, including<br />
breast tumors. The selection <strong>of</strong> a therapy targeted against the Src pathway may<br />
prove more efficacious for this individual patient compared with the empiric<br />
selection <strong>of</strong> a cytotoxic agent. In addition, this strategy could be employed to<br />
identify novel combinations <strong>of</strong> therapies—either multiple cytotoxic agents or<br />
cytotoxic agents combined with targeted agents—as guided by a more rational<br />
and informed approach.<br />
IMPACT AND FUTURE<br />
The practice <strong>of</strong> oncology continually faces the challenge <strong>of</strong> matching the optimal<br />
therapeutic regimen with the most appropriate patient, balancing relative benefit<br />
with risk, to achieve the most favorable outcome. This challenge is <strong>of</strong>ten<br />
daunting with marginal success rates in many advanced disease contexts, likely<br />
reflecting the enormous complexity <strong>of</strong> the disease process coupled with an<br />
inability to properly guide the use <strong>of</strong> available therapeutics.<br />
The importance <strong>of</strong> selecting patients who will respond to a given therapeutic<br />
agent is perhaps best illustrated by the example <strong>of</strong> trastuzumab. In the<br />
absence <strong>of</strong> selection as guided by Her2 protein overexpression or gene amplification,<br />
the overall response rate among breast cancer patients is on the order <strong>of</strong>
Genomic Signatures for Personalized Treatment <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> 119<br />
10%. In contrast, for patients selected on the basis <strong>of</strong> Her2 overexpression or<br />
amplification, the overall response rate when combined with chemotherapy<br />
approached 50% to 60% (39–41). The use <strong>of</strong> trastuzumab in the treatment <strong>of</strong><br />
both early-stage and advanced breast cancer is guided by Her2 protein overexpression<br />
<strong>of</strong> gene amplification. Gene expression signatures predicting<br />
response to various cytotoxic chemotherapeutic agents provide an equally<br />
important opportunity to optimize response to various cytotoxic agents.<br />
Importantly, clinically-available, validated predictors <strong>of</strong> chemotherapy<br />
response could provide an invaluable tool in present day practice. Commonly,<br />
patients are treated with one or more therapeutic regimens that, on a population<br />
basis, have individually demonstrated equal efficacy. Validated chemotherapy<br />
predictive <strong>of</strong> chemotherapy sensitivity provides an opportunity to select the most<br />
effective therapy among a panel <strong>of</strong> standard-<strong>of</strong>-care agents individualized to a<br />
particular patient’s tumor biology. It is essential that this approach be incorporated<br />
into modern day clinical trials. Ultimately, this novel concept has the<br />
potential to augment future treatment strategies incorporating likelihood <strong>of</strong><br />
sensitivity to common cytotoxic agents, as well as various targeted therapeutic<br />
agents. Incorporating this approach into the routine management <strong>of</strong> patients with<br />
various malignancies has the potential to more rationally assign combination<br />
therapies, both cytotoxic and targeted agents, as rationally guided by each<br />
individual’s unique tumor biology.<br />
SUMMARY<br />
Treatment within the field <strong>of</strong> oncology, in particular that <strong>of</strong> breast cancer, is<br />
shifting away from a broad, population-based approach toward individualized<br />
care as driven by unique tumor biology. Validated signatures <strong>of</strong> chemotherapy<br />
sensitivity, coupled with an understanding <strong>of</strong> oncogenic pathway signaling<br />
within a given breast tumor, provides an opportunity to improve the utilization <strong>of</strong><br />
standard-<strong>of</strong>-care therapies. This approach also provides an opportunity to take<br />
the next step toward personalized cancer care—incorporation <strong>of</strong> targeted<br />
agents—as guided by advances in genomic technology.<br />
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15. Mamounas EP, Bryant J, Lembersky B, et al. Paclitaxel after doxorubicin plus<br />
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21. Hudis C, Citron M, Berry D, et al. Five year follow-up <strong>of</strong> INT C9741: dose-dense<br />
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9<br />
Personalized Medicine by the Use <strong>of</strong><br />
Microarray Gene Expression Pr<strong>of</strong>iling<br />
Stella Mook and Laura J. van ’t Veer<br />
Department <strong>of</strong> Pathology, The Netherlands <strong>Cancer</strong> Institute,<br />
Amsterdam, The Netherlands<br />
Fatima Cardoso<br />
Department <strong>of</strong> Medical Oncology, Institute Jules Bordet, Brussels, Belgium<br />
INTRODUCTION<br />
For accurate management <strong>of</strong> early breast cancer, several important decisions<br />
need to be made by patients and clinicians regarding the type <strong>of</strong> surgery, the<br />
need for adjuvant radiotherapy, and particularly the type <strong>of</strong> adjuvant systemic<br />
therapy (chemotherapy and/or endocrine therapy). The decision about adjuvant<br />
systemic therapy is <strong>of</strong> paramount importance since breast cancer is mainly a<br />
systemic disease, with a majority <strong>of</strong> deaths occurring as a consequence <strong>of</strong> distant<br />
metastases. This decision depends on the likelihood <strong>of</strong> recurrence; patientrelated<br />
factors such as biological age, menopausal status, performance status,<br />
and co-morbidities; and tumor-related factors such as size, grade, lymph node<br />
status, and hormonal and human epidermal growth factor receptor 2 (HER2)<br />
status. Most <strong>of</strong> these variables are combined into prognostic models or guidelines<br />
such as the St. Gallen consensus or the National Comprehensive <strong>Cancer</strong> Network<br />
(NCCN) guidelines, in order to assist patients and clinicians in the decisionmaking<br />
process (1–3). Although these guidelines provide valuable information<br />
about prognosis in certain breast cancer populations, the prediction <strong>of</strong> disease<br />
123
124 Mook et al.<br />
outcome for the individual patient is far from accurate. In reality, patients with<br />
similar clinicopathological characteristics can have strikingly different outcomes.<br />
Because <strong>of</strong> these limitations, and because <strong>of</strong> the recognition <strong>of</strong> the incurable<br />
nature <strong>of</strong> metastatic breast cancer, clinicians consider prescribing adjuvant<br />
chemotherapy in the majority <strong>of</strong> early-stage breast cancer patients to reduce the<br />
risk <strong>of</strong> relapse. However, a large proportion <strong>of</strong> these patients is probably cured<br />
with locoregional treatment alone or in combination with adjuvant endocrine<br />
therapy, thus unnecessarily exposing many patients to chemotherapy side effects.<br />
The identification <strong>of</strong> robust and reliable prognostic markers that accurately select<br />
patients not requiring adjuvant chemotherapy is essential to decrease the problem<br />
<strong>of</strong> overtreatment. Additionally, a more accurate patient selection will also contribute<br />
to the decrease in undertreatment, a less common but dangerous problem.<br />
The introduction <strong>of</strong> high-throughput techniques, such as microarray-based<br />
gene expression pr<strong>of</strong>iling and sequencing <strong>of</strong> the human genome, has led to a novel<br />
approach in breast cancer research. Measuring the expression level <strong>of</strong> thousands <strong>of</strong><br />
genes in one experiment using microarray technology enables us to better<br />
understand the biology and heterogeneity <strong>of</strong> breast cancer. The first study to<br />
examine gene expression patterns in breast cancer showed the existence <strong>of</strong> at least<br />
four molecular subtypes <strong>of</strong> breast cancer—luminal A, luminal B, basal, and HER2<br />
positive—distinguished by extensive differences in gene expression (4). Several<br />
subsequent studies confirmed these findings (5–8). In addition, microarray techniques<br />
have been used to identify pr<strong>of</strong>iles that correlate with disease outcome<br />
(prognostic pr<strong>of</strong>ile) or with response to treatment (predictive pr<strong>of</strong>ile) (9–16).<br />
One <strong>of</strong> the first prognostic pr<strong>of</strong>iles was the 70-gene pr<strong>of</strong>ile (Mamma-<br />
Print TM ) identified by van ’t Veer et al. (13). This dichotomous microarray<br />
classifier can accurately distinguish breast cancer patients who have a high<br />
likelihood <strong>of</strong> remaining free <strong>of</strong> distant metastases (good pr<strong>of</strong>ile) from those who<br />
are at high risk <strong>of</strong> developing distant metastases (poor pr<strong>of</strong>ile) (Fig. 1).<br />
DEVELOPMENT AND FIRST RETROSPECTIVE VALIDATION<br />
OF THE 70-GENE PROFILE (MAMMAPRINT TM )<br />
The 70-gene prognostic pr<strong>of</strong>ile was developed in a training set <strong>of</strong> 78 lymph<br />
node–negative, invasive breast tumors less than 5 cm in diameter. Patients were<br />
younger than 55 years at diagnosis, had no prior malignancies, and were treated<br />
in the Netherlands <strong>Cancer</strong> Institute (NKI) with breast conserving therapy or<br />
mastectomy. Five out <strong>of</strong> 78 patients received adjuvant systemic treatment<br />
comprising chemotherapy (n ¼ 3) or endocrine therapy (n ¼ 2). Thirty-four<br />
patients developed distant metastases within five years <strong>of</strong> diagnosis, while the<br />
remaining 44 patients remained free <strong>of</strong> distant metastases. The mean follow-up<br />
<strong>of</strong> patients who remained free <strong>of</strong> distant metastases was 8.7 years, and the mean<br />
time to metastases was 2.5 years.<br />
For all 78 samples, the percentage <strong>of</strong> tumor cells in a hematoxylin- and<br />
eosin-stained section, before and after cutting sections for RNA isolation, was at
Microarray Gene Expression Pr<strong>of</strong>iling 125<br />
Figure 1 Steps in 70-gene microarray analysis. Source: From Ref. 32. copyright # 2002<br />
Massachusetts Medical Society. All rights reserved. Adopted with permission.<br />
least 50%. RNA was isolated and labeled with a fluorescent dye. Reference RNA<br />
consisted <strong>of</strong> an equal amount <strong>of</strong> RNA from all tumors. Both sample RNA and<br />
reference RNA were hybridized on an oligonucleotide microarray platform<br />
containing approximately 25,000 genes (produced by Agilent Technologies, Palo<br />
Alto, California, U.S.) (17). After hybridization, microarray slides were washed<br />
and scanned using a confocal laser scanner (Agilent). Fluorescence intensities<br />
were quantified, corrected for background noise, and normalized (13).<br />
Using a statistical method called “supervised classification,” 231 genes<br />
that were differentially expressed in tumors that metastasized compared with<br />
tumors that did not were selected. These 231 genes were then ranked on the basis<br />
<strong>of</strong> their correlation coefficient with disease outcome. Using a leave-one-out<br />
cross-validation procedure, the top 70 <strong>of</strong> these genes appeared to be the optimal<br />
set <strong>of</strong> genes to accurately discriminate between tumors with a good disease<br />
outcome (free from distant metastases for at least five years) and tumors with a<br />
poor disease outcome (distant metastases within five years <strong>of</strong> diagnosis).<br />
All 78 tumors were ranked according to their correlation with the average<br />
expression <strong>of</strong> the 70 genes <strong>of</strong> the patients who did not develop distant metastases.<br />
The sensitivity was optimized by setting a threshold, which resulted in the
126 Mook et al.<br />
misclassification <strong>of</strong> 3 out <strong>of</strong> 34 patients with a poor disease outcome being<br />
classified as good prognosis and therefore would erroneously have had chemotherapy<br />
withheld (9% misclassification). Subsequently, the 70-gene pr<strong>of</strong>ile was<br />
validated in 19 breast cancer tumors—7 tumors from patients with a good<br />
clinical outcome (no distant metastases within five years <strong>of</strong> diagnosis) and<br />
12 tumors from patients who did develop distant metastases within five years <strong>of</strong><br />
diagnosis. Using the 70-gene pr<strong>of</strong>ile, only 2 out <strong>of</strong> 19 tumors were classified<br />
incorrectly, thereby confirming the initial results (13,18).<br />
The same group <strong>of</strong> investigators performed a first retrospective validation<br />
study using a consecutive series <strong>of</strong> 295 breast tumors from NKI (144 lymph<br />
node–positive and 151 lymph node–negative tumors). All patients were younger<br />
than 53 years at diagnosis and were treated with locoregional therapy alone<br />
(56%) or in combination with adjuvant systemic therapy comprising chemotherapy<br />
alone (31%), hormonal therapy alone (7%), or a combination (7%). In<br />
this consecutive series <strong>of</strong> 295 patients, 61 were also part <strong>of</strong> the previous series<br />
used to develop the 70-gene pr<strong>of</strong>ile (14).<br />
All 295 tumors were classified as good or poor pr<strong>of</strong>ile by calculating the<br />
correlation coefficient with the established average expression level <strong>of</strong> the<br />
70 genes in the previously studied good-outcome group. For the 234 samples that<br />
were not included in the previous study, tumors with a correlation coefficient<br />
above the threshold <strong>of</strong> 0.4 were classified as good pr<strong>of</strong>ile; for the 61 tumors<br />
that were also part <strong>of</strong> the previous study, the threshold was adapted to 0.55 to<br />
correct for overfitting (18).<br />
The 70-gene pr<strong>of</strong>ile accurately classified 115 tumors (39%) as a goodprognosis<br />
group (good pr<strong>of</strong>ile) with a 10-year overall survival <strong>of</strong> 95% (2.6%)<br />
and 180 tumors (61%) as a poor-prognosis group (poor pr<strong>of</strong>ile) with a 10-year<br />
overall survival <strong>of</strong> 55% (4.4%). Interestingly, the 70-gene pr<strong>of</strong>ile was not only<br />
associated with disease outcome in the lymph node–negative patients [hazard<br />
ratio (HR) for distant metastases 5.5; 95% confidence interval (CI) 2.5–12.2; p <<br />
0.001], but also strongly associated with disease outcome in the group <strong>of</strong> 144<br />
lymph node–positive patients (HR for distant metastases 4.5; 95% CI 2.0–10.2;<br />
p < 0.001). Furthermore, the only significant independent factors for prediction<br />
<strong>of</strong> distant metastases were a poor-prognosis pr<strong>of</strong>ile, a larger tumor, the presence<br />
<strong>of</strong> vascular invasion, and no chemotherapy treatment. The 70-gene pr<strong>of</strong>ile was<br />
by far the most powerful predictor <strong>of</strong> distant metastases, with an adjusted HR <strong>of</strong><br />
4.6 (95% CI 2.3–9.2; p < 0.001) (14,18).<br />
THE PROGNOSTIC VALUE OF THE 70-GENE<br />
PROFILE IN A CLINICAL CONTEXT<br />
To assess the potential added value <strong>of</strong> this prognostic tool in a clinical context,<br />
the 70-gene pr<strong>of</strong>ile was compared with the St. Gallen consensus guidelines or<br />
the NIH criteria used at that time (3,19). In comparison with these guidelines, the<br />
70-gene pr<strong>of</strong>ile assigned more patients to the good-prognosis group than the<br />
St. Gallen consensus guidelines or the NIH criteria (40% vs. 15% and 7%,
Microarray Gene Expression Pr<strong>of</strong>iling 127<br />
respectively). Moreover, patients identified by the 70-gene pr<strong>of</strong>ile as having<br />
good prognosis were more likely to remain free <strong>of</strong> distant metastases than<br />
patients identified as having good prognosis according to the St. Gallen consensus<br />
guidelines or the NIH criteria. On the other hand, patients who were<br />
assigned to the poor-prognosis group by the 70-gene pr<strong>of</strong>ile had a higher risk <strong>of</strong><br />
developing distant metastases as compared with the poor-prognosis group<br />
classified by the St. Gallen consensus guidelines or the NIH criteria.<br />
The 70-gene pr<strong>of</strong>ile was built to have a minimum <strong>of</strong> misclassified patients<br />
with a poor disease outcome; in other words, the low-risk group should be<br />
maximized without causing considerable undertreatment. Consequently, in this<br />
retrospective validation series, the 70-gene pr<strong>of</strong>ile had a high negative predictive<br />
value <strong>of</strong> 96% for the 234 new patients and a positive predictive value <strong>of</strong> only<br />
38%. That is to say, <strong>of</strong> all patients with a good prognosis pr<strong>of</strong>ile, 4% will still<br />
develop distant metastases and would erroneously have had chemotherapy<br />
withheld, whereas <strong>of</strong> all patients classified as having a poor prognosis, 38% will<br />
indeed develop distant metastases. Consequently, treatment decisions based on<br />
the 70-gene pr<strong>of</strong>ile would still lead to overtreatment; but since the total proportion<br />
<strong>of</strong> patients identified as having a poor pr<strong>of</strong>ile is much smaller than the<br />
proportion <strong>of</strong> high-risk patients according to the St. Gallen consensus guidelines<br />
or the NIH criteria, overtreatment would be reduced by 25% to 30%. Furthermore,<br />
the overall prediction <strong>of</strong> disease outcome and consequent treatment<br />
decision according to the 70-gene pr<strong>of</strong>ile seems to be more accurate.<br />
INDEPENDENT RETROSPECTIVE VALIDATION<br />
BY THE TRANSBIG CONSORTIUM<br />
To further substantiate the prognostic value <strong>of</strong> the 70-gene pr<strong>of</strong>ile, the European<br />
Union’s sixth Framework Project TRANSBIG (http://www.breastinternationalgroup.org/TransBIG.aspx)<br />
conducted an independent retrospective validation<br />
study (20). Frozen tumor material and clinical data were collected from<br />
302 patients from five different cancer centers in the United Kingdom, Sweden,<br />
and France. Patients were younger than 61 years at diagnosis, had a lymph node–<br />
negative T1 or T2 tumor, and had not received adjuvant systemic therapy. The<br />
patients were treated before 1999, and the median follow-up in this series was<br />
13.6 years. The frozen tumor samples were sent to Agendia, the microarray facility<br />
in Amsterdam, where RNA was isolated and hybridized on a custom-designed<br />
microarray, measuring the gene expression level <strong>of</strong> the 70 genes in triplicate.<br />
Tumors were classified as poor pr<strong>of</strong>ile if the correlation coefficient <strong>of</strong> the average<br />
expression was under 0.4. The microarray facility had no access to the clinical<br />
data, and the researchers collecting the clinical data were blinded for the 70-gene<br />
pr<strong>of</strong>ile results. Only an independent statistical <strong>of</strong>fice had access to both the clinical<br />
and the microarray data simultaneously. Eighty percent <strong>of</strong> the samples were<br />
centrally reviewed for estrogen receptor (ER) status and tumor grade, and the<br />
centers were independently audited for quality <strong>of</strong> data control.
128 Mook et al.<br />
This independent study confirmed that the 70-gene pr<strong>of</strong>ile can accurately<br />
select patients who are at high risk <strong>of</strong> developing distant metastases (poor prognosis)<br />
from patients who are at low risk <strong>of</strong> developing distant metastases and<br />
hence can be safely spared chemotherapy (good pr<strong>of</strong>ile), with HRs <strong>of</strong> 2.79 (95%<br />
CI 1.60–4.87) and 2.32 (95% CI 1.35–4.0) for distant metastases–free survival and<br />
overall survival, respectively. Moreover, patients with a good pr<strong>of</strong>ile had a 10-year<br />
overall survival <strong>of</strong> 89% compared with 69% in patients with a poor pr<strong>of</strong>ile. The<br />
70-gene pr<strong>of</strong>ile outperformed several prognostic methods such as the Nottingham<br />
Prognostic Index and the St. Gallen consensus guidelines. The performance <strong>of</strong> the<br />
70-gene pr<strong>of</strong>ile was also compared with Adjuvant! Online. The Adjuvant! S<strong>of</strong>tware—available<br />
at www.adjuvantonline.com—calculated a 10-year overall survival<br />
probability based on clinicopathological criteria (the patient’s age, tumor<br />
size, tumor grade, ER status, and comorbidity) (21,22). Patients were considered<br />
as having low clinical risk when the 10-year breast cancer–specific survival<br />
probability calculated by Adjuvant! was more than 88% if their tumors were ER<br />
positive and more than 92% if they were ER negative. The rationale for these two<br />
cut<strong>of</strong>fs was the assumption that patients with ER-positive tumors would now<br />
receive hormonal therapy, with an average absolute benefit <strong>of</strong> 4%. In the discordant<br />
cases, the 70-gene pr<strong>of</strong>ile provided much stronger prognostic information,<br />
with almost identical survival rates for the low and high clinical risk groups within<br />
each 70-gene pr<strong>of</strong>ile risk, suggesting that the 70-gene pr<strong>of</strong>ile predicts disease<br />
outcome independently <strong>of</strong> clinical and pathological characteristics (Fig. 2) (20).<br />
Figure 2 Kaplan–Meier curve by clinical and 70-gene expression signature risk groups.<br />
Overall survival. Source: From Ref. 20. By permission <strong>of</strong> Oxford University Press.
Microarray Gene Expression Pr<strong>of</strong>iling 129<br />
Another important finding <strong>of</strong> this validation study is the time dependency<br />
<strong>of</strong> the 70-gene pr<strong>of</strong>ile. The median follow-up in this series was twice as long as<br />
the follow-up <strong>of</strong> the initial series. To determine the influence <strong>of</strong> the time <strong>of</strong><br />
distant metastases on the prognostic value <strong>of</strong> the pr<strong>of</strong>ile, HRs were calculated<br />
with arbitrary censoring <strong>of</strong> all observations at different time points. The adjusted<br />
HR for distant metastases at 5 years was 4.7 in comparison with an adjusted HR<br />
<strong>of</strong> 3.5 at 10 years, suggesting a better prediction <strong>of</strong> early (within five years <strong>of</strong><br />
diagnosis) metastases by the 70-gene pr<strong>of</strong>ile (18,23,24).<br />
OTHER PROGNOSTIC PROFILES IN BREAST CANCER<br />
Several other prognostic gene expression signatures were developed, such as the<br />
76-gene pr<strong>of</strong>ile, the wound signature, and the genomic grade index (15,25,26).<br />
The 76-gene prognostic pr<strong>of</strong>ile was developed on a different microarray platform<br />
(Affymetrix, Santa Clara, California, U.S.) and using a slightly different methodology;<br />
the genes associated with relapse <strong>of</strong> the disease were selected separately<br />
for ER-positive and ER-negative patients. The genes selected in each<br />
group were then combined in a 76-gene pr<strong>of</strong>ile (15). A retrospective validation<br />
study confirmed the prognostic value <strong>of</strong> this pr<strong>of</strong>ile in early-stage breast cancer<br />
patients (27). Recently, the 76-gene pr<strong>of</strong>ile has also been validated independently<br />
by the TRANSBIG consortium in the same patient series, using the same<br />
methodology as the validation <strong>of</strong> the 70-gene pr<strong>of</strong>ile (24). Although only<br />
three genes were common to both pr<strong>of</strong>iles, the biological pathways represented<br />
are the same, and the prognostic value <strong>of</strong> both the 70-gene pr<strong>of</strong>ile and the<br />
76-gene pr<strong>of</strong>ile was validated in this patient series (20,24). Since the pr<strong>of</strong>iles<br />
perform equally, and the 70-gene pr<strong>of</strong>ile has proven to be reproducible and<br />
robust and is available as a Food and Drug Administration (FDA)-cleared<br />
diagnostic test (28), the TRANSBIG consortium decided to use the 70-gene<br />
pr<strong>of</strong>ile in a large prospective clinical trial, the Microarray In Node-negative<br />
Disease may Avoid ChemoTherapy (MINDACT) trial.<br />
IMPLEMENTATION OF THE 70-GENE PROFILE:<br />
THE RASTER TRIAL AND THE LOGISTICS PILOT STUDY<br />
In addition to scientific evidence provided by validation studies, implementation<br />
<strong>of</strong> these microarray pr<strong>of</strong>iles in both prospective clinical trials and in daily practice<br />
requires logistical feasibility. The requirement <strong>of</strong> fresh tissue as the source <strong>of</strong> highquality<br />
RNA has several consequences for tumor sample handling. Traditional<br />
fixation <strong>of</strong> fresh tissue in formaldehyde, for example, causes degradation <strong>of</strong> RNA,<br />
thereby making tumor samples unsuitable for microarray experiments. In addition,<br />
to avoid interlaboratory variability, fresh tumor samples have to be shipped to one<br />
microarray facility. Consequently, for the implementation <strong>of</strong> microarray pr<strong>of</strong>iles,<br />
local procedures have to be adapted, and close collaboration between pathologists,<br />
surgeons, and medical oncologists is <strong>of</strong> outmost importance.
130 Mook et al.<br />
Recently, two multicenter feasibility studies were conducted, the RASTER<br />
study and a logistics pilot study (18,29). The first study was coordinated by NKI<br />
with financial support from the Dutch Health Care Insurance Board. The aim <strong>of</strong><br />
the study was to assess the feasibility <strong>of</strong> collecting good-quality tissue from<br />
several community hospitals in the Netherlands to perform the 70-gene pr<strong>of</strong>ile<br />
test. For all patients included in this study, tumor samples from excised specimens<br />
were obtained and placed in a commercially available preservation fluid<br />
(RNAlater 1 , Qiagen GmBH, Hilden, Germany) at room temperature. Subsequently,<br />
the samples were sent by conventional mail to NKI, where they<br />
were snap-frozen and stored at –808C. The 70-gene pr<strong>of</strong>ile was performed at the<br />
microarray facility in Amsterdam. Preliminary results showed that it is feasible<br />
to adapt local procedures and to collect good-quality tissue for microarray testing<br />
in a multicentric setting (18,29,30).<br />
In the RASTER study, tissue was preserved temporarily in RNAlater and<br />
sent by conventional mail at room temperature. Preservation <strong>of</strong> tumor tissue in<br />
RNAlater may influence several processes in the tissue, such as protein levels.<br />
Since one <strong>of</strong> the additional aims <strong>of</strong> the MINDACT trial is the establishment <strong>of</strong> a<br />
biological materials bank for future research, including proteomics, storage <strong>of</strong><br />
tissue in RNAlater is not suitable for the MINDACT trial and fresh, frozen tissue<br />
will be mandatory (31). Therefore, a second feasibility study was conducted to<br />
test the collection, storage, and shipment <strong>of</strong> fresh, frozen tissue in six European<br />
hospitals. Women younger than 71 years, with early-stage breast cancer, were<br />
included. After surgery, the pathologist obtained representative tumor samples<br />
using a 6-mm biopsy puncher. Tumor samples were snap-frozen in liquid<br />
nitrogen and stored at –808C within one hour <strong>of</strong> surgery. The samples were<br />
shipped to Amsterdam on dry ice by a contracted courier specializing in the<br />
transportation <strong>of</strong> frozen tissue. At Agendia, RNA was isolated when the samples<br />
were representative <strong>of</strong> the tumor (i.e. tumor cells 50%). After measurement <strong>of</strong><br />
the quality and quantity <strong>of</strong> the RNA, the samples were hybridized on the 70-gene<br />
pr<strong>of</strong>ile platform. Results showed that in general it is feasible to collect goodquality<br />
fresh, frozen tissue for microarray tests in a multicentric and multinational<br />
setting. Additionally, in the course <strong>of</strong> this feasibility study, procedures<br />
were adapted and optimized for use in the MINDACT trial (18).<br />
PROSPECTIVE VALIDATION OF THE 70-GENE<br />
PROFILE IN THE MINDACT TRIAL<br />
Despite the completion <strong>of</strong> all necessary technical validation procedures and the<br />
FDA clearance as the in vitro diagnostic multivariate index assay (IVDMIA),<br />
including approval <strong>of</strong> clinical utility, the clinical validation <strong>of</strong> the 70-gene<br />
pr<strong>of</strong>ile can still be further optimized in currently diagnosed, adjuvantly treated<br />
populations to achieve level-1 evidence. The TRANSBIG consortium has<br />
decided to further validate the 70-gene prognostic alongside the classical clinicopathological<br />
factors (31). This trial, the MINDACT trial [EORTC (European
Microarray Gene Expression Pr<strong>of</strong>iling 131<br />
Organization for Research and Treatment <strong>of</strong> <strong>Cancer</strong>) 10041 BIG (<strong>Breast</strong> International<br />
Group) 3-04], will enroll 6000 women between 18 and 71 years <strong>of</strong> age,<br />
with primary invasive breast cancer at diagnosis. Tumors should be unilateral,<br />
stage T1, T2, or operable T3; carcinomas in situ are eligible provided invasive<br />
cancer is present. Patients should be treated with breast conserving treatment or<br />
mastectomy and should have a negative sentinel node or axillary clearance.<br />
Patients with primary malignancies or previous chemotherapy or radiotherapy<br />
will not be included. The main goal <strong>of</strong> the trial is to prove that the 70-gene<br />
pr<strong>of</strong>ile will safely assign chemotherapy to a smaller proportion <strong>of</strong> node-negative<br />
breast cancer patients. Therefore, all patients included in MINDACT will have a<br />
risk assessment based on the 70-gene pr<strong>of</strong>ile and according to the currently used<br />
clinicopathological criteria. This last risk assessment is provided by an updated<br />
version <strong>of</strong> Adjuvant! Online. If, according to both risk assessments, the patients’<br />
risk <strong>of</strong> developing distant metastases is low (an estimated 13% <strong>of</strong> all patients), no<br />
chemotherapy will be given. If both methods classify the patients’ risk <strong>of</strong> developing<br />
distant metastases as high (an estimated 55% <strong>of</strong> the patients), chemotherapy<br />
will be proposed. If the risk assessments are discordant (an estimated 32% <strong>of</strong><br />
all patients), patients will be randomized to the clinicopathological criteria or<br />
the 70-gene pr<strong>of</strong>ile to be used for adjuvant chemotherapy decision making<br />
(Fig. 3). The group <strong>of</strong> most interest to answer the primary research question<br />
comprises patients with a low-risk 70-gene pr<strong>of</strong>ile (good pr<strong>of</strong>ile) and high-risk<br />
Figure 3 MINDACT study design. Source: From Ref. 18. With permission.<br />
Abbreviations: R-T, treatment decision randomization: CT, chemotherapy.
132 Mook et al.<br />
clinicopathological criteria who will be randomized to use the 70-gene pr<strong>of</strong>ile<br />
for the treatment decision and thus receive no chemotherapy. In this group a<br />
null hypothesis <strong>of</strong> a five-year distant metastases–free survival <strong>of</strong> 92% will be<br />
tested (31).<br />
Additional study objectives are related to the type <strong>of</strong> chemotherapy and<br />
endocrine therapy. Patients who are to receive chemotherapy may be randomized<br />
between an anthracycline-based regimen and a regimen <strong>of</strong> docetaxel and capecitabine<br />
in order to compare the efficacy and safety <strong>of</strong> both regimens. The latter<br />
regimen may have increased efficacy with less long-term side effects such as<br />
cardiotoxicity and leukemia. Post-menopausal (spontaneous or induced) patients<br />
with hormone receptor–positive tumors will be randomized for endocrine therapy<br />
with two years <strong>of</strong> tamoxifen, followed by five years <strong>of</strong> letrozole or seven<br />
years <strong>of</strong> letrozole upfront.<br />
In addition to these three research questions, whole genome arrays will be<br />
performed for all 6000 patients, and frozen and paraffin-embedded tumor samples<br />
together with blood samples will be collected from all patients. These<br />
microarray data and the biological materials will be stored in an independent<br />
biobank and will provide a valuable source <strong>of</strong> information for future research<br />
that may result in the identification <strong>of</strong> new prognostic or predictive biomarkers<br />
and new drug targets.<br />
FUTURE PROSPECTS<br />
The MINDACT trial is the first randomized clinical trial that will prospectively<br />
evaluate the prognostic value <strong>of</strong> a microarray-based prognostic tool, the 70-gene<br />
pr<strong>of</strong>ile or Mammaprint, in an adjuvantly treated population. The implementation<br />
<strong>of</strong> molecular diagnostics based on the true biology <strong>of</strong> a patient’s tumor will<br />
greatly expedite the introduction <strong>of</strong> personalized medicine.<br />
The details <strong>of</strong> the rationale, design, and logistics <strong>of</strong> the MINDACT trial are<br />
described in references 18,23, and 31, and the authors strongly recommend these<br />
articles to the reader.<br />
ACKNOWLEDGMENTS<br />
The studies mentioned in this chapter were supported by the European Commission<br />
Framework Programme VI, the Center <strong>of</strong> Biomedical Genetics, the<br />
Dutch Health Care Insurance Board, the Dutch National Genomic Initiative:<br />
<strong>Cancer</strong> Genomics Program, the <strong>Breast</strong> <strong>Cancer</strong> Research Foundation, the EBCC-<br />
<strong>Breast</strong> <strong>Cancer</strong> Working Group–association sans but lucratif (asbl), the Fondation<br />
Belge Contre Le <strong>Cancer</strong>, the S. G. Komen for the Cure Foundation, Jacqueline<br />
Seroussi Memorial Foundation for <strong>Cancer</strong> Research, and the European Organisation<br />
for Research and Treatment <strong>of</strong> <strong>Cancer</strong> (EORTC)–<strong>Breast</strong> <strong>Cancer</strong> Group.<br />
S. Mook was partially supported by the traineeship program <strong>of</strong> the TRANSBIG<br />
network. The authors thank the numerous individuals who have contributed
Microarray Gene Expression Pr<strong>of</strong>iling 133<br />
to the studies mentioned in this review, especially those from the TRANSBIG<br />
consortium and the EORTC, and all the patients who have and still are participating<br />
in these studies.<br />
CONFLICTS OF INTEREST<br />
Dr. L. J. van ’t Veer is a named inventor on a patent application for Mammaprint<br />
and reports holding equity in Agendia BV.<br />
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21. Olivotto IA, Bajdik CD, Ravdin PM, et al. Population-based validation <strong>of</strong> the<br />
prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 2005; 23(12):<br />
2716–2725.<br />
22. Ravdin PM, Simin<strong>of</strong>f LA, Davis GJ, et al. Computer program to assist in making<br />
decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol<br />
2001; 19(4):980–991.<br />
23. Cardoso F, van ’t Veer L, Rutgers E, et al. Clinical application <strong>of</strong> the 70-gene pr<strong>of</strong>ile<br />
(Mammaprint TM ): the MINDACT trial. J Clin Oncol 2008; 26(5):729–735.<br />
24. Desmedt C, Piette F, Loi S, et al. Strong time dependence <strong>of</strong> the 76-gene prognostic<br />
signature for node-negative breast cancer patients in the TRANSBIG multicenter<br />
independent validation series. Clin <strong>Cancer</strong> Res 2007; 13(11):3207–3214.<br />
25. Chang HY, Nuyten DSA, Sneddon JB, et al. Robustness, scalability, and integration<br />
<strong>of</strong> a wound-response gene expression signature in predicting breast cancer survival.<br />
Proc Natl Acad Sci U S A. 2005; 102(10):3738–3743.<br />
26. Sotiriou C, Wirapati P, Loi S, et al. Gene expression pr<strong>of</strong>iling in breast cancer:<br />
understanding the molecular basis <strong>of</strong> histologic grade to improve prognosis. JNCI<br />
<strong>Cancer</strong> Spectrum 2006; 98(4):262–272.<br />
27. Foekens JA, Atkins D, Zhang Y, et al. Multicenter validation <strong>of</strong> a gene expressionbased<br />
prognostic signature in lymph node-negative primary breast cancer. J Clin<br />
Oncol 2006; 24(11):1665–1671.<br />
28. Couzin J. DIAGNOSTICS: amid debate, gene-based cancer test approved. Science<br />
2007; 315(5814):924.<br />
29. Bueno-de-Mesquita JM, van Hakten WH, Retel VP, et al. use <strong>of</strong> 70-gene signature to<br />
predict prognosis <strong>of</strong> patients with node-negative breast cancer: a prospective community-based<br />
feasibility study (RASTER). Lancet Oncol 2008; 8: 1079–1087.<br />
30. van de Vijver M. Gene-expression pr<strong>of</strong>iling and the future <strong>of</strong> adjuvant therapy.<br />
Oncologist 2005; 10(suppl 2):30–34.<br />
31. Bogaerts J, Cardoso F, Buyse M, et al. Gene signature evaluation as a prognostic<br />
tool: challenges in the design <strong>of</strong> the MINDACT trial. Nat Clin Pract Oncol 2006; 3(10):<br />
540–551.<br />
32. Sauter G, Simon R. Predictive molecular pathology. N Engl J Med 347 2002; (25):<br />
1995–1996.
10<br />
Predictive Markers for Targeted <strong>Breast</strong><br />
<strong>Cancer</strong> Treatment<br />
Hans Christian B. Pedersen a and John M. S. Bartlett<br />
Endocrine <strong>Cancer</strong> Group, <strong>Cancer</strong> Research Centre,<br />
Western General Hospital, Edinburgh, United Kingdom<br />
INTRODUCTION<br />
Currently selection <strong>of</strong> breast cancer treatment is guided predominantly by patient<br />
prognosis using classical pathological assessment <strong>of</strong> tumors, with higher risk<br />
patient groups being <strong>of</strong>fered more aggressive therapy. The choice <strong>of</strong> treatment<br />
regimen is guided by a very small number <strong>of</strong> predictive biomarkers (Table 1) (1).<br />
However, it is now clearly recognized that not only may the risks <strong>of</strong> treatment<br />
outweigh the benefits in some patient groups but that not all patients are equal<br />
with respect to their response to and benefit gained from exposure to novel and<br />
established therapies. There is increasing interest in the identification <strong>of</strong> biological<br />
markers to improve the targeting <strong>of</strong> established therapies to those cancers<br />
likely to respond. In addition, development <strong>of</strong> new molecular targeted drugs has<br />
led to an urgent need for research into predictive markers that can distinguish<br />
patients who benefit from treatment. New proteomic technologies are leading to<br />
markers being discovered at an increasing rate, with protein expression, cellular<br />
localization, and posttranslational modification being recognized as those <strong>of</strong><br />
potential importance. Many new immunohistochemical (IHC) markers are<br />
a Hans Christian B. Pedersen is employed on a Marie Curie Transfer <strong>of</strong> Knowledge project between<br />
Dako A/S, Glostrup, Denmark, and University <strong>of</strong> Edinburgh.<br />
135
136 Pedersen and Bartlett<br />
Table 1<br />
Current FDA-Approved Predictive Markers in <strong>Breast</strong><br />
Predictive Marker Method Drug<br />
ERa IHC Tamoxifen, AIs<br />
PR IHC Tamoxifen, AIs<br />
HER2/neu IHC Trastuzumab (Herceptin)<br />
HER2/neu FISH Trastuzumab (Herceptin)<br />
Abbreviations: ERa, estrogen receptor a; PR, progesterone receptor; AIs,<br />
aromatase inhibitors; FISH, fluorescence in situ hybridization.<br />
currently being tested for predictive potential for various therapies in breast<br />
cancer. Through large-scale gene expression studies using microarrays, it has<br />
been realized that tumors have molecular signatures that may determine response<br />
to treatment and patient prognosis. Many candidate-predictive and prognostic<br />
signatures in breast cancer are being explored, and some are already in use to aid<br />
patient management (e.g., OncoType Dx 1 , Genomic Health, Redwood City,<br />
California, U.S.). The challenge for the future is to identify both the biological<br />
and diagnostic determinates <strong>of</strong> treatment response in order to allow matching <strong>of</strong><br />
appropriate treatments to specific patients.<br />
Future breast cancer treatment is likely to be guided by predictive markers<br />
along with genomic signatures, treating patients with mixtures <strong>of</strong> molecular<br />
targeted therapeutics. This chapter will review current IHC and fluorescent in<br />
situ hybridization (FISH)-based predictive markers in breast and the challenges<br />
associated with the development and implementation <strong>of</strong> predictive markers in<br />
future.<br />
CURRENT PREDICTIVE MARKERS IN BREAST CANCER: LESSONS LEARNT<br />
A predictive marker is defined as a factor that indicates sensitivity or resistance<br />
to a specific treatment (2). In contrast, a prognostic marker predicts a tumor’s<br />
future behavior independent <strong>of</strong> systemic adjuvant treatment. Some markers can<br />
be both predictive and prognostic, as exemplified by the estrogen receptor a<br />
(ERa) expression, which both predicts response to endocrine treatment and<br />
correlates with prognosis.<br />
Hormonal Therapy and Hormone Receptors<br />
ERa and progesterone receptor (PR) status were the first predictive markers used<br />
in the clinic to select patients for breast cancer treatment. Today, IHC ERa or PR<br />
staining is done routinely on patient tumor samples to predict response to<br />
endocrine therapy. More correctly, the assay is used to exclude ERa- or PRnegative<br />
patients from ERa- or PR-targeted therapy, as they will have a low<br />
probability <strong>of</strong> responding to treatment (3). Historically however, ERa was first<br />
discovered in the late 1960s on its ability to bind radiolabeled ligand (4,5).
Predictive Markers for Targeted <strong>Breast</strong> <strong>Cancer</strong> Treatment 137<br />
Within 10 years <strong>of</strong> its initial discovery, assays had been developed that allowed<br />
ER to be used a marker for hormonal responsiveness and clinical aggressiveness<br />
<strong>of</strong> the tumor (6). Many years passed before it was realized that only ERapositive<br />
patients could benefit from treatment and for these finding to be applied<br />
as clinical practice (3). It is now almost universally accepted that ERa testing has<br />
a central role in breast cancer management, but it is worth noting that no prospective<br />
trial has yet tested the predictive value <strong>of</strong> ERa or PR as predictive<br />
biomarkers. Later evidence has emerged supporting testing for PR as well as<br />
ERa (7). PR is a transcriptional target <strong>of</strong> ligand-bound ERa, and it is has been<br />
proved that including the PR score in the ERa score provides stronger evidence<br />
for true ERa positivity and therefore makes patients more likely to respond to<br />
therapy (8–10). However, controversy remains over the value <strong>of</strong> supplementary<br />
markers to ERa, and in several countries, PR testing in not routinely performed.<br />
The drugs administered to ERa- or PR-positive patients have been through<br />
many generations through the last three decades, from pure ERa-antagonist<br />
(fulvestrant) to ERa–partial antagonist tamoxifen and drugs inhibiting the enzyme<br />
aromatase that synthesise oestrogen (11). Current clinical practice in breast cancer<br />
management utilizes a combination adjuvant tamoxifen followed up by aromatase<br />
inhibitors (AIs) or AIs alone. However, ER or PR do not select between these<br />
therapies, and despite evidence that PR is predictive <strong>of</strong> poor response on tamoxifen,<br />
there is no evidence, to date, that AIs circumvent this resistance.<br />
Despite the long history <strong>of</strong> the ERa or PR status as a predictive and<br />
prognostic marker, no standardized protocols for fixation and IHC staining are<br />
yet widely applied. Conflicting evidence exists for when patients are considered<br />
ER negative and therefore will not respond to treatment, but above 10% positivestaining<br />
tumor cells are generally considered ER positive (12). Perhaps because<br />
ERa has been available for testing as a predictive marker for such a long<br />
time, before emphasis was put on standardizing IHC testing, this has lead to the<br />
development <strong>of</strong> a multitude <strong>of</strong> different protocols using a variety <strong>of</strong> antibodies.<br />
ER IHC results have been shown to be highly influenced by the efficiency <strong>of</strong> the<br />
antigen-retrieval step (13). External quality assurance initiatives such as the<br />
United Kingdom National External Quality Assessment Scheme for Immunocytochemistry<br />
(UK NEQAS-ICC) exist now to improve quality across clinical<br />
laboratories through external assessment <strong>of</strong> analytical and interpretative performance;<br />
such schemes are however not uniformly successful.<br />
In addition to standardizing ERa IHC for scoring tumors as positive or<br />
negative, it has been suggested that quantification <strong>of</strong> ERa levels may be necessary<br />
in some clinical settings because tumors expressing low or intermediate<br />
ERa levels have been shown to benefit from chemotherapy in addition to<br />
endocrine therapy (14). Current ERa IHC protocols are semiquantitative, so<br />
alternatives such as radiolabeled ligand-binding assays or quantitative polymerase<br />
chain reaction (qPCR) have been suggested. Gene expression <strong>of</strong> ER and<br />
PR correlate well with protein expression, and it has been suggested to be an<br />
alternative to IHC as it is sensitive and quantitative over a large dynamic
138 Pedersen and Bartlett<br />
range (15). It has been suggested that more quantitative methods <strong>of</strong> measuring<br />
ERa and PR may provide additional information to guide choice <strong>of</strong> therapy. We<br />
will discuss this in detail later (12).<br />
HER2/neu and Trastuzumab (Herceptin 1 )<br />
HER2/neu or human epidermal growth factor receptor 2, a product <strong>of</strong> the protooncogene<br />
HER2 first discovered in 1985, belongs to the HER family <strong>of</strong> tyrosine<br />
kinase receptors (16–18). Numerous studies have documented HER2/neupositive<br />
breast cancers display more aggressive disease and shortened diseasefree<br />
survival (19). Using recombinant technologies, trastuzumab (Herceptin 1 ,<br />
Genentech, South San Francisco, California, U.S.), a monoclonal immunoglobulin<br />
G1 class, humanized murine antibody, was developed to specifically target<br />
patients with breast cancer that overexpressed the HER2/neu protein (20,21).<br />
Clinical trials demonstrated the efficiency <strong>of</strong> trastuzumab for treating metastatic<br />
breast cancers and subsequent trials in combination therapy (21–25) for HER2<br />
positive disease. Again, HER2/neu, like ERa, is more predictive <strong>of</strong> which<br />
patients will not respond to treatment.<br />
As in the case for endocrine therapy, most predictive marker development<br />
followed drug development, leading to some confusion. In initial clinical trials,<br />
patient selection was based on an IHC assay using the mouse monoclonal<br />
antibody that had been humanized to create trastuzumab (26,27). Today most<br />
patients are selected for trastuzumab therapy on the basis <strong>of</strong> HER2/neu<br />
expression levels in tumor cells (Fig. 1). In 2001, HER2/neu testing achieved<br />
standard care <strong>of</strong> practice in the American Society <strong>of</strong> Clinical Oncology (ASCO)<br />
breast cancer clinical practice guidelines. In most laboratories, tumors are<br />
screened with FDA-approved IHC tests, HercepTest 1 (Dako, Glostrup, Denmark)<br />
or HER2/neu (CB11) PATHWAY 1 (Ventana Medical Systems, Inc., Tucson,<br />
Arizona, U.S.). Ambiguous cases are then analyzed with FDA-approved HER2/<br />
neu FISH from Vysis (Downers Grove, Illinois, U.S.), Dako, or Ventana. Alternatively,<br />
in some laboratories, FISH is the only method used for HER2/neu testing.<br />
HER2/neu testing for trastuzumab therapy has undergone considerable scrutiny<br />
in numerous studies, because evidence emerged indicated that the IHC tests<br />
generated considerable amount <strong>of</strong> false positives and negatives (28–31). This<br />
discrepancy is due in part to lack <strong>of</strong> adherence to protocols, variable fixation<br />
methods, and subjectivity related to tumor scoring.<br />
Only 20% to 35% <strong>of</strong> HER2/neu positive tumors respond to treatment,<br />
depending on the context the drug is given in, and studies have demonstrated that<br />
this may be attributed to a combination <strong>of</strong> factors, including lack <strong>of</strong> pathway<br />
dependence, receptor being inactive despite overexpressed, or the induction <strong>of</strong><br />
resistance through various mechanisms (32). Controversy persists with regard to<br />
the optimal method for HER2/neu testing and interpretation <strong>of</strong> results (33). It has<br />
been reported that it may be more cost effective to test all tumors with HER2/neu<br />
FISH, avoiding false positives and false negatives (28). This is a possibility, as
Predictive Markers for Targeted <strong>Breast</strong> <strong>Cancer</strong> Treatment 139<br />
Figure 1 Current HER2/neu testing in breast cancer. IHC staining performed on patient<br />
tumor samples and scored using the HercepTest. Patient testing 0 to 1þ go on to receive<br />
other treatments. Patients testing 2þ go on to HER2/neu FISH testing and if amplified for<br />
HER2/neu receive trastuzumab. Patients scoring 3þ receive trastuzumab. Alternatively all<br />
patients undergo HER2/neu FISH and patients scored as amplified receive trastuzumab.<br />
Abbreviations: IHC, immunohistochemical; FISH, fluorescence in situ hybridization.<br />
FISH and, more critically, chromogenic in situ hybridization (CISH) technology<br />
become more widely distributed, and an agreement can be made on the best<br />
choice <strong>of</strong> HER2/neu testing.<br />
DEVELOPMENT OF PREDICTIVE MARKERS<br />
The successful targeting <strong>of</strong> HER2/neu with trastuzumab inspired the development<br />
<strong>of</strong> a wave <strong>of</strong> new therapeutic antibodies and kinase inhibitors that were<br />
aimed at inhibiting growth factor–regulated signaling. However, repeating the<br />
success <strong>of</strong> HER2/neu testing for trastuzumab therapy has proven difficult.<br />
Inhibitors <strong>of</strong> mammalian Target Of Rapamycin (mTOR, e.g., temsirolimus,<br />
Toricel 1 , Wyeth <strong>Ph</strong>armaceuticals, Inc., Madison, New Jersey, U.S.), epidermal<br />
growth factor receptor (EGFR, e.g., cetuximab, Erbitux 1 , ImClone Systems, Inc.,<br />
Branchburg, New Jersey, U.S.) and vascular endothelial growth factor receptor<br />
(VEGFR, e.g., bevacizumab, Avastin 1 , Genentech) have shown modest or no<br />
results in clinical trials as single agent therapies in breast cancer (34–36). This may<br />
be partly attributed to lack <strong>of</strong> predictive markers <strong>of</strong> response in combination with<br />
drugs simply not being effective as single agents. However, this failure may also
140 Pedersen and Bartlett<br />
reflect poorly designed approaches toward the discovery <strong>of</strong> predictive biomarkers.<br />
Predictive markers for EGFR-targeted therapy have been in focus for the past five<br />
years, and we will therefore briefly go through the history <strong>of</strong> EGFR-targeted<br />
therapy and what has been learnt for future predictive marker development. In<br />
addition, a model for codevelopment <strong>of</strong> predictive markers in relation to traditional<br />
drug development is presented.<br />
EGFR—A Cautionary Tale<br />
EGFR or HER1 belongs to the same family <strong>of</strong> tyrosine kinase receptors as<br />
HER2/neu and EGFR is considered an attractive target for therapeutic intervention<br />
in many cancer forms due to the receptor being frequently mutated and<br />
overexpressed (34). EGFR overexpression is associated with reduced diseasefree<br />
survival and resistance to chemotherapy (37). EGFR inhibitors (e.g., erlotinib<br />
and gefitinib) and therapeutic antibodies against the extracellular domain <strong>of</strong><br />
the receptor (e.g., cetuximab, matuzumab, and panitumumab) are currently in<br />
clinical trials or late-stage development (38). Most clinical trials have investigated<br />
EGFR inhibitors in cancers <strong>of</strong> the lung and colon, and cetuximab has<br />
been approved for treatment <strong>of</strong> colorectal cancer (34). Clinical trials have also<br />
been conducted in breast cancer, with limited success (39–47). In several <strong>of</strong> these<br />
trials, patients have been selected for trial on the basis <strong>of</strong> EGFR overexpression.<br />
However, this was later shown to be a mistake because EGFR expression, as<br />
currently evaluated, did not correlate with clinical response (48,49) or with<br />
breast cancer cell line inhibition (50). Clinical trials in breast cancer have clearly<br />
demonstrated that EGFR inhibitors and therapeutic antibodies are targeting<br />
EGFR receptors in both tumor and skin from patients, so the lack <strong>of</strong> effect is not<br />
due to the drug’s failure to reach the target or the target’s lack <strong>of</strong> inhibition (41).<br />
So EGFR inhibitors hit their target, and certain cancer patients clearly benefit<br />
from these drugs. Therefore, it is surprising that although EGFR-targeted therapy<br />
has been in focus for the past decade and drugs are now being administered to<br />
patients, no FDA-approved test or pr<strong>of</strong>ile exists at present to guide clinicians on<br />
the use <strong>of</strong> EGFR inhibitors. Studies have shown EGFR gene copy number,<br />
mutations, and downstream signalling to be associated with clinical response in a<br />
series <strong>of</strong> lung cancer trials (51–55). However, these were retrospective, poorly<br />
powered studies, which are <strong>of</strong> insufficient impact to influence molecular diagnostics.<br />
Taken together, what has emerged from clinical trials in breast and other<br />
cancers along with findings from in vitro studies is that EGFR inhibition is not<br />
similar to HER2/neu-targeted therapy in breast cancer, where a single, wellcharacterized<br />
molecular event can be used to target treatment. Therefore, what<br />
constitutes a good predictive marker for EGFR-targeted therapy in breast cancer<br />
is currently not clear. For future clinical trials with EGFR inhibitors to be successful<br />
and current, approved EGFR inhibitors to be administered to the right<br />
patients, it will be necessary to understand better the biology <strong>of</strong> EGFR-directed<br />
signaling and the relative importance <strong>of</strong> mutations, gene copy number, and
Predictive Markers for Targeted <strong>Breast</strong> <strong>Cancer</strong> Treatment 141<br />
downstream signaling pathways have on therapeutic response. These findings<br />
can then be used to setup adequately designed molecular pathology and translational<br />
studies within clinical trials that will address predictive markers for<br />
EGFR-targeted therapy.<br />
Predictive Markers in Drug Development<br />
Future clinical trials should utilize findings from HER2/neu and EGFR-targeted<br />
therapy. We have learnt that overexpression or amplification alone does not<br />
necessarily mean that protein can be targeted with drugs or that it is indicative <strong>of</strong><br />
aberrant activation. Posttranslational modifications such as phosphorylation,<br />
ubiquitination and acetylation, and localization are critical for protein function.<br />
Furthermore, what has become clear is that preclinical models do not reflect the<br />
complexity <strong>of</strong> human tumors well, and, therefore, predictive marker selection<br />
must be validated in clinical settings. Despite all these challenges, drug development<br />
must try and entail predictive marker development so that stratification<br />
<strong>of</strong> patients can be made possible, thus avoiding unnecessary side effects and<br />
improving the chance <strong>of</strong> a trial being successful. Without an increased focus on<br />
predictive marker–directed patient selection, many <strong>of</strong> the drugs currently in<br />
clinical trials run the risk <strong>of</strong> failing to reach the clinic despite being efficacious<br />
for a subpopulation <strong>of</strong> patients. Therefore, it should be cost effective for pharmaceutical<br />
companies to build predictive marker strategies in drug development,<br />
as this may improve the success rate <strong>of</strong> drug development in clinicals. In<br />
addition, it would be useful to develop predictive markers <strong>of</strong> response to many <strong>of</strong><br />
the drugs already in the market, thus improving patient care and reducing<br />
treatment costs by only treating patients who benefit from the treatment.<br />
The major risk we face at present is that we will continue to use poorly<br />
designed retrospective analyses <strong>of</strong> existing trial populations to seek to identify<br />
markers on the basis <strong>of</strong> incomplete understanding <strong>of</strong> the biology <strong>of</strong> drug<br />
response. This is, in part, due to the fact that we have no reference criteria<br />
against which to judge biomarker development. For ERa, despite no level-1<br />
evidence being available, it is unthinkable that this biomarker be regarded as<br />
anything less than essential to the management <strong>of</strong> breast cancer in the twentyfirst<br />
century. For HER2, only after the central importance <strong>of</strong> this biomarker was<br />
established were steps put in place to produce robust, accurate, and clinically<br />
viable test methods. However, clearly for future biomarkers, a structured<br />
approach will improve the likelihood <strong>of</strong> selecting appropriate markers. For this<br />
to succeed, we need to progress from the current model to one where, as with<br />
drug development, critical attention is paid to each step <strong>of</strong> the process by which<br />
novel biomarkers are developed (56).<br />
To develop and validate predictive markers, a number <strong>of</strong> hurdles have to be<br />
passed before they can be considered for clinical practice. These processes should<br />
be addressed in a structured manner to maximise the possibility <strong>of</strong> success. For<br />
example, the development <strong>of</strong> robust and accurate diagnostic assays should
142 Pedersen and Bartlett<br />
Figure 2 Development <strong>of</strong> IHC- and FISH-based predictive markers. Abbreviations: IHC,<br />
immunohistochemical; FISH, fluorescence in situ hybridization.<br />
precede, not follow, the identification <strong>of</strong> a potential clinical role for biomarkers<br />
(56). To illustrate this, we have adapted a biomarker development scheme from<br />
Pepe et al. and Hayes et al. (57,58) into four phases (Figure 2).<br />
l<br />
l<br />
<strong>Ph</strong>ase 1: Discovery stage. In order to discover predictive markers <strong>of</strong><br />
response, high-throughput gene expression microarrays or proteomic<br />
technologies are <strong>of</strong>ten used. The primary output <strong>of</strong> this phase is to develop<br />
a set <strong>of</strong> potential predictive markers that allow selecting patients who<br />
respond to treatment. Ideally, samples from the neoadjuvant setting should<br />
be used so that information about the influence that treatment has on the<br />
markers selected can be obtained.<br />
<strong>Ph</strong>ase 2: Development stage. In this stage, the predictive marker assay now<br />
needs make the transition from the research setting to the clinical diagnostic<br />
laboratory. At this stage, it is required to develop the assay for<br />
correct determination <strong>of</strong> predictive marker <strong>of</strong> response to a level where it
Predictive Markers for Targeted <strong>Breast</strong> <strong>Cancer</strong> Treatment 143<br />
can be used by clinical institutions, according to guidelines put forward by<br />
ASCO and EORTC (The European Organization for Research and<br />
Treatment <strong>of</strong> <strong>Cancer</strong>) (58,59). This means that proper instructions need to<br />
be in place for all aspects <strong>of</strong> predictive marker testing, including sample<br />
handling and pretreatment, protocols and standards for ensuring uniform<br />
testing across institutions, and external quality assurance. It is desirable<br />
that a set <strong>of</strong> positive controls are developed for interlaboratory variation to<br />
be accounted for in the same fashion as the HercepTest comes with positive<br />
control cell lines.<br />
l <strong>Ph</strong>ase 3: Validation phase. In order to validate markers found in phase 1,<br />
retrospective validation studies need to be set up. When setting up clinical<br />
trial at this stage, it is important that one <strong>of</strong> the endpoints <strong>of</strong> the trial is to<br />
assess predictive markers. One <strong>of</strong> the primary outcomes <strong>of</strong> this phase is to<br />
evaluate how well the predictive markers perform in independent<br />
validation studies and what predictive markers perform compared better<br />
with traditional diagnostic markers such as nodal status, grade, etc.<br />
l <strong>Ph</strong>ase 4: Clinical utility phase. If success is obtained in phase 3, the marker<br />
can be move to phase 4, with clinical studies utilizing randomized<br />
prospective screening with predictive markers. The primary outcome <strong>of</strong><br />
phase 4 is to evaluate if patients selected with predictive markers were the<br />
ones that benefited from treatment and if it is ethically defendable to select<br />
patients on the basis <strong>of</strong> the results <strong>of</strong> the clinical studies. The predictive<br />
marker then needs to go through regulatory approval for the defined<br />
indication before being applied in the clinic.<br />
Predictive marker development requires the joint effort <strong>of</strong> multiple academic<br />
institutions and industry to cover all the aspects <strong>of</strong> development. It takes<br />
many years and large investments, and the risk for failure is high because <strong>of</strong> the<br />
nature <strong>of</strong> drug development. That may be the reason why so few new markers<br />
have been introduced in recent years despite major research and development<br />
efforts and new techniques.<br />
Future Perspectives<br />
<strong>Cancer</strong> is a disease that involves the complex interplay <strong>of</strong> multiple genes in a<br />
framework <strong>of</strong> mutations in the cancer cell. In addition, tumor cells have interactions<br />
with other cells, thereby further promoting growth and invasiveness.<br />
Patients respond differently to treatment based on their tumor characteristics.<br />
Despite recent challenges, we foresee the strong likelihood that current research<br />
and drug development will introduce more predictive markers into IHC laboratories<br />
in the same fashion as trastuzumab has resulted in HER2/neu being<br />
routinely tested for (see above). This foresight is based on the premise that more<br />
care and more robust approaches are applied to the development <strong>of</strong> predictive<br />
biomarkers. It is likely that within two to five years, a panel <strong>of</strong> markers will
144 Pedersen and Bartlett<br />
be tested on every tumor sample in order to decide optimal therapy regimen.<br />
Currently only three predictive IHC markers (ERa, PR, and HER2/neu) are tested<br />
for routinely in breast, but already some pathologists use additional markers to<br />
inform decisions, e.g., p53, Ki67, and TOP2A. As more and more predictive IHC<br />
markers are introduced in clinical laboratories, this will lead to an increased<br />
pressure on pathology laboratories to deliver fast and accurate decisions. How can<br />
this be achieved and what technological advances can improve IHC testing?<br />
Current IHC testing in pathology laboratories is subjective and semiquantitative,<br />
as slides are scored manually and using chromogenic dyes (56–61).<br />
The use <strong>of</strong> computer-assisted analysis <strong>of</strong> predictive markers has been proven to<br />
reduce slide-scoring variability among pathologists (31). While FISH/CISH<br />
analysis is more robust, there remains room for improvement in this area also.<br />
We foresee the introduction <strong>of</strong> image analysis as a standard tool for IHC scoring,<br />
which will begin the process <strong>of</strong> moving the analytical phase from the microscope<br />
to the computer screen.<br />
As analytical challenges increase, accurate quantitation will become<br />
increasingly important. Already evidence suggests that additional information<br />
could be derived if it were possible to robustly quantify ERa and Ki67. There is,<br />
therefore, a strong opportunity for systems such as the Automated QUantitative<br />
Analysis (AQUA 1 ) from HistoRX, Inc., New Haven, Connecticut, U.S., to pave<br />
the way for a switch from traditional chromogenic staining techniques to fluorescent<br />
staining techniques which are potentially quantitative (wide dynamic<br />
range) (62). A further advantage is that fluorescent labels can be multiplexed<br />
together, yielding spatial information and quantization <strong>of</strong> proteins in different<br />
compartments <strong>of</strong> the cell. New techniques being developed, such as the proximity<br />
ligation in situ assay, show promising results for quantitative analysis <strong>of</strong><br />
specific protein interactions in FFPE, i.e., formalin-fixed, paraffin-embedded,<br />
tissue (63). In some cases, this may be superior to the measurement <strong>of</strong> protein<br />
concentrations or posttranslational modifications, as most proteins take part in<br />
multiprotein complexes and their inclusion in the complex indicates relevant<br />
activity. In addition, several protein-interaction inhibitors are currently in clinical<br />
trials, and, therefore, measurement <strong>of</strong> the amount <strong>of</strong> target (interacting proteins) in<br />
the tissue may be a predictive marker for responsiveness to the drug.<br />
There remains a key question: Will we be testing protein and DNA<br />
markers on slides at all in 5 to 10 years? There are a number <strong>of</strong> developments in<br />
process now miniaturizing assays onto small chips that will measure hundreds <strong>of</strong><br />
protein or DNA markers quantitatively and in a reproducible fashion. Such<br />
technological advances will change the pathology workflow in the future so that<br />
every cancer sample coming into the clinic will be analyzed by the pathologist,<br />
tumor cells microdissected into solution, and DNA, protein, and RNA extracted<br />
and applied onto analytical chips. The readout from these analyses could then be<br />
used to determine optimal therapy regimen for the patient and hopefully cure the<br />
disease. However, until these techniques have been properly validated and<br />
developed for clinical use and trials have proven their value, we are likely to rely
Predictive Markers for Targeted <strong>Breast</strong> <strong>Cancer</strong> Treatment 145<br />
on traditional in situ techniques for protein analysis (IHC) and molecular<br />
determination <strong>of</strong> gene amplifications (FISH/CISH).<br />
In summary, introduction <strong>of</strong> quality control, automated platforms, and<br />
computer-assisted scoring will lead to higher throughput and more accurate<br />
decisions with regard to treatment and facilitate the development <strong>of</strong> future<br />
predictive markers.<br />
CONCLUSION<br />
As our understanding <strong>of</strong> cancer as a disease progresses and the tools to investigate<br />
cancer biology develops, the complexity <strong>of</strong> the disease is unraveling.<br />
<strong>Breast</strong> cancers are now being classified not only on the basis <strong>of</strong> traditional<br />
pathology traits but also according to molecular phenotype and, more recently,<br />
gene expression pr<strong>of</strong>iles. New molecular targeted therapeutics will lead to more<br />
predictive markers being introduced, and these markers must be prospectively<br />
validated, internally and externally quality assured, and rigorously quantitative to<br />
allow correct decisions for treatment. Therefore, an evolution <strong>of</strong> the pathology<br />
laboratory is likely to take place in the near future, leading to a switch to<br />
fluorescent-based techniques, digital imaging <strong>of</strong> slides, and computerized scoring<br />
<strong>of</strong> predictive markers. On a longer time horizon, new technologies being<br />
developed and applied are likely to change cancer diagnostics so that molecular<br />
phenotyping <strong>of</strong> patients will take center stage in patient management. In the not<br />
too distant future, there is the possibility that pathologists will type and stage<br />
tumors and simultaneously identify key areas <strong>of</strong> tissue for automated microdissection<br />
and processing. The subsequent process <strong>of</strong> molecular phenotyping,<br />
which may include kinomic, proteomic, and transcriptomic assays performed<br />
simultaneously could be carried out in specialised laboratories using robotics and<br />
micr<strong>of</strong>luidics on high-density protein and gene arrays to determine optimal<br />
treatment regimens for tumors with specific molecular pr<strong>of</strong>iles linked to drug<br />
response. Such a future vision may represent the more radical view <strong>of</strong> what could<br />
be accomplished by linking <strong>of</strong> current concepts and technologies to maximize<br />
patient benefit from therapeutic options. However, we must dream <strong>of</strong> such<br />
futures if we are to maximize progress in the area <strong>of</strong> molecular pathology for the<br />
benefit <strong>of</strong> the most important people, our patients.<br />
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11<br />
Circulating Tumor Cells in Individualizing<br />
<strong>Breast</strong> <strong>Cancer</strong> Therapy<br />
James M. Reuben<br />
Department <strong>of</strong> Hematopathology, The University <strong>of</strong> Texas<br />
M. D. Anderson <strong>Cancer</strong> Center, Houston, Texas, U.S.A.<br />
Massimo Crist<strong>of</strong>anilli<br />
Department <strong>of</strong> <strong>Breast</strong> Medical Oncology, The University <strong>of</strong> Texas<br />
M. D. Anderson <strong>Cancer</strong> Center, Houston, Texas, U.S.A.<br />
INTRODUCTION<br />
<strong>Breast</strong> cancer is the most frequently diagnosed nonskin cancer among women<br />
in the United States and is second only to lung cancer in causing cancer-related<br />
deaths among them (1). The vast majority <strong>of</strong> deaths are due to recurrent<br />
metastatic disease. Occult dissemination <strong>of</strong> tumor cells is the main cause <strong>of</strong><br />
recurrent metastatic breast cancer (MBC) in patients who have undergone<br />
resection <strong>of</strong> their primary tumor (2). Approximately 5% <strong>of</strong> patients with breast<br />
cancer have clinically detectable metastases at the time <strong>of</strong> initial diagnosis, and<br />
a further 30% to 40% <strong>of</strong> patients who appear clinically free <strong>of</strong> metastases<br />
harbor occult metastases (3,4). The formation <strong>of</strong> metastatic colonies is a<br />
continuous process, commencing early during the growth <strong>of</strong> the primary tumor.<br />
Metastasis occurs through a cascade <strong>of</strong> linked sequential steps involving<br />
multiple host-tumor interactions. This complex process requires the cells to<br />
enter the circulation, arrest at the distant vascular bed, extravasate into the<br />
organ interstitium and parenchyma, and proliferate as a secondary colony.<br />
151
152 Reuben and Crist<strong>of</strong>anilli<br />
Several experimental studies suggest that during each stage <strong>of</strong> the process, only<br />
the fittest tumor cells survive (2).<br />
The first report on tumor cells in the peripheral circulation was attributed<br />
to Ashworth in 1869 (5). Since then, the existence, origin, and clinical significance<br />
<strong>of</strong> circulating tumor cells (CTCs) have been debated. The introduction <strong>of</strong><br />
sensitive and specific immunohistochemical techniques in the late 1970s led to<br />
renewed interest in the detection <strong>of</strong> CTCs and their possible association with<br />
minimal residual disease in solid malignancies (6,7). However, there is little<br />
understanding <strong>of</strong> the genetic and phenotypic characteristics <strong>of</strong> CTCs in peripheral<br />
blood. It has been speculated that mammary stem cells represent the cellular<br />
origins <strong>of</strong> cancer because they exist quiescently over long periods and can<br />
accumulate multiple mutations over the lifetime, ultimately giving rise to tumors<br />
when stimulated to proliferate (8–10). It was recently reported that highly<br />
tumorigenic cells possessing properties consistent with those <strong>of</strong> stem or progenitor<br />
cells could be isolated from human breast cancers (9). Furthermore,<br />
transplantation <strong>of</strong> as many as several hundred <strong>of</strong> these cells into the cleared<br />
mammary fat pads <strong>of</strong> etoposide-treated mice resulted in the growth <strong>of</strong> breast<br />
tumors (10). <strong>Cancer</strong> stem cells not only exist in human breast cancer but also<br />
have different biologic features than the more differentiated cells that constitute<br />
the majority <strong>of</strong> cells in human breast cancers. These cancer stem cells may be<br />
detectable in the peripheral blood and bone marrow <strong>of</strong> patients with MBC and<br />
may account for their worse prognosis. Hence the development <strong>of</strong> therapies<br />
specifically targeting cancer stem cells may have great benefits for patients with<br />
CTCs who possess the properties <strong>of</strong> cancer stem cells.<br />
When CTCs migrate to distal sites, they can potentially give rise to<br />
microscopic lesions that result in metastasis.Microscopic disease may be present in<br />
lymph nodes, bone marrow (primary breast cancer), and peripheral blood (metastatic<br />
disease) at the time <strong>of</strong> breast cancer diagnosis (3,4,11,12). Most studies have<br />
demonstrated that the detection <strong>of</strong> microscopic disease in patients with breast<br />
cancer contributes prognostic information and, in selected cases, can predict the<br />
efficacy <strong>of</strong> treatments (12). In patients with primary breast cancer, the detection <strong>of</strong><br />
microscopic disease in lymph nodes and bone marrow has led to a better understanding<br />
<strong>of</strong> the role <strong>of</strong> minimal residual disease in establishing metastasis.<br />
Over the past few years, immunomagnetic separation technology, with its<br />
higher level <strong>of</strong> sensitivity and specificity, has been used to improve the detection<br />
<strong>of</strong> CTCs compared with the detection <strong>of</strong> occult CTCs by reverse transcriptasepolymerase<br />
chain reaction (RT-PCR) (13–21). In this chapter, we review the<br />
methods <strong>of</strong> detecting CTCs, the prognostic implications <strong>of</strong> CTCs, and the<br />
potential means <strong>of</strong> exploiting CTCs in developing individualized therapy.<br />
DETECTION OF CIRCULATING TUMOR CELLS<br />
Advances in technology have facilitated the detection <strong>of</strong> even very small numbers<br />
<strong>of</strong> CTCs in the peripheral blood <strong>of</strong> cancer patients. The principle <strong>of</strong> these<br />
methods is based on the fact that breast cancer cells express epithelial cell
Circulating Tumor Cells in Individualizing <strong>Breast</strong> <strong>Cancer</strong> Therapy 153<br />
adhesion molecule-1 (EpCAM). EpCAM, a 40-kDa glycoprotein, is present on<br />
most epithelial carcinomas, and recent studies indicate that this molecule has a<br />
major morphoregulatory function, relevant not only to epithelial tissue development<br />
but also to carcinogenesis and tumor progression (22,23).<br />
Detection <strong>of</strong> CTCs by the CellSearch TM System<br />
The only assay approved by the U.S. Food and Drug Administration is the<br />
CellSearch TM system (Veridex, LLC, Warren, New Jersey, U.S.). With this assay,<br />
a small sample <strong>of</strong> peripheral blood is allowed to react with ferr<strong>of</strong>luids coated with<br />
antibodies to EpCAM. Next, the sample is processed through a magnetic field to<br />
retain the EpCAM-positive cells, while the EpCAM-negative cells, primarily <strong>of</strong><br />
hematopoietic origin, are eluted from the column and discarded. Thereafter, the<br />
enriched EpCAM-positive cells are allowed to react with the nucleic acid dye<br />
4 0 ,6-doamidino-2-phenylindole (DAPI), monoclonal antibodies specific for leukocytes<br />
(anti-CD45 antibodies conjugated with allophycocyanin), and epithelial<br />
cells (phycoerythrin-conjugated antibodies to cytokeratin 8, 18, and 19) and<br />
analyzed by the CellSpotter TM Analyzer (Veridex). The CellSpotter Analyzer is a<br />
semiautomated fluorescence-based microscopy system that enables computergenerated<br />
reconstruction <strong>of</strong> cellular images. The analyzer interrogates each cell<br />
image to determine if it meets the very stringent requirements <strong>of</strong> an algorithm to<br />
be classified as a CTC. The algorithm insures that a CTC expresses EpCAM and<br />
not leukocyte lineage-specific antigens (represented by CD45), exhibits cytoplasmic<br />
expression <strong>of</strong> cytokeratin, and contains a nucleus that binds DAPI.<br />
Absence <strong>of</strong> any <strong>of</strong> these characteristics disqualifies a cell image as a CTC. In<br />
identifying CTCs, the procedure identifies cell objects that possess some, but not<br />
all, <strong>of</strong> the required characteristics to be a CTC and are labeled as “unclassified”<br />
objects (24). The stringent criteria <strong>of</strong> CellSearch for identifying a CTC are<br />
laudable; however, the merits <strong>of</strong> the assay continue to be debated as it minimizes<br />
the scientific or clinical significance <strong>of</strong> unclassified objects.<br />
Although CellSearch is a very reproducible and reliable method for the<br />
identification and enumeration <strong>of</strong> CTCs in the peripheral blood <strong>of</strong> patients with<br />
breast cancer, its clinical utility has been limited by its high cost. The semiautomated<br />
assay is also somewhat labor intensive. In addition, the need to permeabilize<br />
the cell membrane <strong>of</strong> a viable cell to introduce DAPI and anticytokeratin<br />
antibodies to label intracellular structures further deters the possibility <strong>of</strong> interrogating<br />
viable cells for their growth potential, colony formation, and genomics,<br />
among others. To address these issues, the CellPr<strong>of</strong>ile TM kit was introduced that<br />
permits the retrieval <strong>of</strong> EpCAM-positive cells prior to cell permeabilization and<br />
interrogation for genomic or proteomic pr<strong>of</strong>iles <strong>of</strong> the CTCs.<br />
Isolation <strong>of</strong> CTCs by the AutoMACS TM System<br />
EpCAM, also known as human epithelial cell antigen (HEA) or CD326, is<br />
expressed on the majority <strong>of</strong> tumor cells <strong>of</strong> epithelial cell origin but not on
154 Reuben and Crist<strong>of</strong>anilli<br />
circulating B cells, T cells, or monocytes. In another assay that exploits this<br />
characteristic <strong>of</strong> CTCs, peripheral blood mononuclear cells are incubated with<br />
magnetic beads coated with anti-CD326 (Miltenyi Biotec, Inc., Auburn, California,<br />
U.S.) prior to passage through a magnetic column to enrich for CTCs. Typically,<br />
mononuclear cells are isolated from peripheral blood, bone marrow, pleural<br />
fluid, paracentesis fluid, or other tissues, mixed with anti-CD326 microbeads, and<br />
incubated at 48C to88C for 15 minutes. After washing, the cell pellet is resuspended<br />
and loaded onto the magnetic column <strong>of</strong> an automatic magnetic cell<br />
sorting (AutoMACS TM ) system (Miltenyi Biotec) (25). CD326-positive CTCs are<br />
isolated using the positive selection protocol and can then be centrifuged onto<br />
glass slides to determine the morphology, viability, and purity <strong>of</strong> the preparation.<br />
In one study, Papanicolaou (Pap)-stained cytospin smears were analyzed in<br />
detail to identify tumor cells on the basis <strong>of</strong> conventional cytologic criteria. In<br />
cytospin slides <strong>of</strong> enriched CTCs, large cells with cytomorphological features<br />
suspicious for tumor cells were detected, and subsequently the cytospin slides<br />
were immunocytochemically stained with the avidin-biotin complex peroxidase<br />
technique using pancytokeratin antibody for the identification <strong>of</strong> tumor cells<br />
(Fig. 1A). (The immunostains <strong>of</strong> CTCs were performed and interpreted by<br />
Dr. Savitri Krishnamurthy, Department <strong>of</strong> Pathology, The University <strong>of</strong> Texas<br />
M. D. Anderson <strong>Cancer</strong> Center).<br />
A second cytospin slide <strong>of</strong> CD326-positive bone marrow tumor cells from<br />
the same patient was stained with a cocktail <strong>of</strong> anticytokeratin antibodies using<br />
the avidin-biotin complex peroxidase technique for the identification <strong>of</strong> tumor<br />
cells (Fig. 1B). In the majority <strong>of</strong> cases, the suspicious cells were positive for<br />
cytokeratin, thereby confirming the presence <strong>of</strong> micrometastatic carcinoma.<br />
In addition to bone marrow, it is possible to isolate CTCs from the<br />
paracentesis fluid <strong>of</strong> a patient with MBC. Figure 2 shows a cluster <strong>of</strong> CTCs<br />
Figure 1A A cytospin slide <strong>of</strong> CTCs from the bone marrow <strong>of</strong> a patient with primary<br />
breast cancer using anti-CD326 antibody–coated microbeads and reacted with Pap stain.<br />
The figure shows a cluster <strong>of</strong> CTCs with cellular morphology consistent with that <strong>of</strong><br />
epithelial cells. Abbreviations: CTCs, circulating tumor cells; Pap, Papanicolaou.
Circulating Tumor Cells in Individualizing <strong>Breast</strong> <strong>Cancer</strong> Therapy 155<br />
Figure 1B CD326-positive CTCs are cytokeratin-positive epithelial cells. Abbreviation:<br />
CTCs, circulating tumor cells.<br />
Figure 2 CD326-positive CTCs from the paracentesis fluid <strong>of</strong> a patient with MBC.<br />
Abbreviations: CTCs, circulating tumor cells; MBC, metastatic breast cancer.<br />
isolated from the paracentesis fluid <strong>of</strong> a patient with MBC that were reacted with<br />
Pap stain.<br />
Isolation <strong>of</strong> CTCs by the EasySep TM System<br />
Another immunomagnetic cell selection system is EasySep TM (StemCell Technologies,<br />
Inc., Vancouver, Canada), which combines the specificity <strong>of</strong> monoclonal<br />
antibodies with the simplicity <strong>of</strong> a column-free magnetic system. In this<br />
procedure, highly enriched cells are obtained by positively selecting the cells <strong>of</strong><br />
interest with antibodies to a specific cell-surface antigen. The cells targeted for
156 Reuben and Crist<strong>of</strong>anilli<br />
selection are cross-linked to EasySep nanoparticles via the formation <strong>of</strong> tetrameric<br />
antibody complexes (StemCell Technologies) in a standard centrifuge<br />
tube, which is then placed in the EasySep magnetic chamber. The handheld<br />
magnetic chamber is gently inverted to discard the cells that are not bound to<br />
the specific nanoparticle complexes that adhere to the walls <strong>of</strong> the tube in the<br />
magnetic chamber. To obtain a highly pure population, the residual cells in the<br />
tube are rinsed twice and then harvested by removing the tube from the chamber.<br />
Enriched CTCs can then be used for further interrogation for the differential<br />
expression <strong>of</strong> EpCAM or other markers <strong>of</strong> interest.<br />
Positive selection is most effective when a specimen has a generous<br />
complement <strong>of</strong> the desired cell population. When a specimen, such as peripheral<br />
blood, contains few or inadequate numbers <strong>of</strong> the cells <strong>of</strong> interest, a negativeselection<br />
approach can be taken to enrich for these cells. One negative-selection<br />
technique is the RosetteSep TM (StemCell Technologies) method that uses a<br />
highly purified combination <strong>of</strong> mouse and rat monoclonal antibodies. When<br />
reacted with peripheral whole blood, these antibodies bind in bispecific tetrameric<br />
antibody complexes directed against cell surface antigens on human<br />
hematopoietic cells (CD2, CD16, CD19, CD36, CD38, CD45, and CD66b) and<br />
glycophorin A on red blood cells, or erythrocytes. The unwanted cells <strong>of</strong> hematopoietic<br />
origin form rosettes by cross-linking to multiple erythrocytes that can<br />
be easily removed by performing a typical Ficoll-Paque TM (GE Amersham-<br />
<strong>Ph</strong>armacia Diagnostics, Uppsala, Sweden) density gradient procedure for the<br />
separation <strong>of</strong> mononuclear cells from peripheral blood. The rosettes, free<br />
erythrocytes, and granulocytes form a pellet, while the unlabeled, desired CTCs<br />
are collected from the interface between the plasma and the buoyant density<br />
medium. Figures 3A and 3B represent cytospin slides <strong>of</strong> CTCs isolated from the<br />
Figure 3A Pap stain <strong>of</strong> a cluster <strong>of</strong> CTCs isolated from the peripheral blood <strong>of</strong> a patient<br />
with MBC using RosetteSep TM . Abbreviations: Pap, Papanicolaou; CTCs, circulating<br />
tumor cells; MBC, metastatic breast cancer.
Circulating Tumor Cells in Individualizing <strong>Breast</strong> <strong>Cancer</strong> Therapy 157<br />
Figure 3B Cytokeratin-positive CTCs from the peripheral blood <strong>of</strong> a patient with MBC<br />
using RosetteSep TM . Abbreviations: CTCs, circulating tumor cells; MBC, metastatic<br />
breast cancer.<br />
peripheral blood <strong>of</strong> a patient with MBC that were reacted with Pap stain<br />
(Fig. 3A) and anticytokeratin antibodies (Fig. 3A).<br />
Detection <strong>of</strong> CTCs by AdnaGen’s Technology<br />
In addition to magnetic separation methodologies, PCR greatly facilitates the<br />
detection <strong>of</strong> occult tumor cells through the use <strong>of</strong> nucleic acid analysis. The<br />
PCR-based assay, Adna Test <strong>Breast</strong> <strong>Cancer</strong> Select, developed by AdnaGen AG,<br />
Langenhagen, Germany, makes use <strong>of</strong> RT-PCR to identify putative transcripts <strong>of</strong><br />
genes in EpCAM-positive cells that are isolated by a magnetic separation<br />
method. This approach takes advantage <strong>of</strong> the prevailing state <strong>of</strong> the art <strong>of</strong> CTC<br />
isolation and enhances the chance <strong>of</strong> detecting very low numbers <strong>of</strong> CTCs<br />
or occult CTCs on the basis <strong>of</strong> their expression <strong>of</strong> tumor-associated genes.<br />
AdnaGen’s two-step “combination-<strong>of</strong>-combinations principle” involves initially<br />
the enrichment <strong>of</strong> CTCs using an antibody mix linked to magnetic particles (26).<br />
Thereafter, mRNA is extracted from the CTCs and transcribed into cDNA before<br />
being subjected to multiplex RT-PCR for the detection <strong>of</strong> tumor-associated<br />
genes. Owing to the combination <strong>of</strong> different selection and tumor markers, both<br />
the heterogeneity <strong>of</strong> the tumor cells and the possible therapy-induced deviations<br />
in the expression patterns are taken into account. The AdnaGen system has high<br />
analytical sensitivity (two cells in 5 mL <strong>of</strong> blood) and high specificity (>90%)<br />
achieved through the combination <strong>of</strong> multimarker tumor cell enrichment and<br />
multiplex gene expression pr<strong>of</strong>iling.
158 Reuben and Crist<strong>of</strong>anilli<br />
CLINICAL SIGNIFICANCE AND PROGNOSTIC VALUE OF CTCs in MBC<br />
Tumor cells can be detected in the locoregional lymph nodes <strong>of</strong> women with<br />
early breast cancer, and the presence <strong>of</strong> these cells has been shown to have a<br />
negative effect on long-term prognosis (3,12). Despite evidence <strong>of</strong> the prognostic<br />
value <strong>of</strong> CTCs in some studies, the detection <strong>of</strong> micrometastases was never<br />
incorporated into cancer-staging protocols or considered a valuable clinical tool.<br />
This may be the result <strong>of</strong> a combination <strong>of</strong> factors, such as variable antigen<br />
expression in poorly differentiated tumors and reports <strong>of</strong> cytokeratin and epithelial<br />
membrane antigen positivity in cells that are not <strong>of</strong> epithelial origin,<br />
which demonstrated a need for more sensitive and specific methods <strong>of</strong> detection<br />
than were available at the time. This need was filled to some degree by sensitive<br />
PCR techniques (14,16,17). In the past decade, a few studies have shown that the<br />
detection <strong>of</strong> occult disease by PCR has prognostic significance in some solid<br />
tumors (13). However, PCR-based assays for the detection <strong>of</strong> occult tumor cells<br />
have limitations, particularly contamination <strong>of</strong> samples, specificity <strong>of</strong> the assays,<br />
and inability to quantify tumor cells. Moreover, PCR-based methods cannot be<br />
used to perform functional assays. These factors have precluded the widespread<br />
use <strong>of</strong> PCR in in vitro diagnostic applications.<br />
Using an immunomagnetic detection approach, Austrup et al. reported the<br />
prognostic significance <strong>of</strong> genomic alterations (e.g., c-erbB-2 overexpression)<br />
present in CTCs purified from the blood <strong>of</strong> patients with breast cancer (27). The<br />
authors investigated genomic imbalances, such as mutation, amplification, and<br />
loss <strong>of</strong> heterozygosity, <strong>of</strong> 13 tumor suppressor genes and 2 proto-oncogenes<br />
using DNA isolated from minimal residual cancer cells. The presence and the<br />
number <strong>of</strong> genomic imbalances measured in disseminated tumor cells were<br />
significantly associated with a worse prognosis (27).<br />
Subsequently, Meng et al. demonstrated that CTCs recapitulate the human<br />
epidermal growth factor receptor 2 (HER2) status <strong>of</strong> the primary tumor (28).<br />
Furthermore, the authors demonstrated that a fraction <strong>of</strong> patients with HER2-<br />
negative primary tumors had detectable HER2 gene amplification in their CTCs,<br />
suggesting acquisition or selection <strong>of</strong> this phenotype during cancer progression<br />
(37.5%; 95% confidence interval, 18.8–59.4%). Intriguingly, the initiation <strong>of</strong><br />
trastuzumab-based therapy in a few cases with altered HER2 status in CTCs was<br />
associated with a clinical response (29). These important observations support<br />
the argument that detection <strong>of</strong> CTCs and determination <strong>of</strong> their gene amplification<br />
or expression can be used for better tailoring <strong>of</strong> therapies.<br />
The predictive and prognostic roles <strong>of</strong> CTCs were investigated in a prospective<br />
multicenter clinical trial led by researchers at the University <strong>of</strong> Texas<br />
M. D. Anderson <strong>Cancer</strong> Center (11). In this study, the CellSearch system was<br />
used to prospectively determine the prognostic and predictive value <strong>of</strong> CTCs in<br />
patients with MBC who were about to start a new systemic treatment. The 177<br />
enrolled patients underwent peripheral blood collection at monthly intervals for<br />
up to six months after enrollment. A cut<strong>of</strong>f <strong>of</strong> 5 CTCs/7.5 mL was used to
Circulating Tumor Cells in Individualizing <strong>Breast</strong> <strong>Cancer</strong> Therapy 159<br />
stratify patients into positive and negative groups (positive, 5 CTCs/7.5 mL;<br />
negative, 18 months; P <<br />
0.0001). On multivariate Cox hazards regression analysis, CTC levels, both at<br />
baseline and at first follow-up, were the most significant predictors <strong>of</strong> progression-free<br />
and overall survival (11).<br />
An analysis restricted to patients who were about to start first-line systemic<br />
therapy after the diagnosis <strong>of</strong> MBC had similar findings (30). A subsequent<br />
investigation including patients with either measurable or evaluable MBC confirmed<br />
that CTCs are an independent predictor <strong>of</strong> survival that is more powerful<br />
than standard prognostic measures, such as hormone receptor status, and measures<br />
<strong>of</strong> tumor burden (e.g., the Swenerton score or level <strong>of</strong> CA27-29) (31).<br />
The detection <strong>of</strong> changes in CTC status may also have predictive utility.<br />
The CTC detection rate at first follow-up was lower than at baseline, particularly<br />
in patients undergoing first-line treatment (25% versus 52%) and those<br />
with visceral disease (28% versus 50%) (11). In patients with newly diagnosed<br />
disease treated with chemotherapy alone (n ¼ 37), the percentage <strong>of</strong> patients<br />
who were CTC positive decreased significantly from baseline to first follow-up<br />
(57% to 37%, P ¼ 0.001) and first imaging-visit (at eight to nine weeks) blood<br />
draw (57% to 31%, P ¼ 0.004) (31). More importantly, in patients receiving<br />
trastuzumab-containing regimens, CTC-positive patients decreased from<br />
baseline to first follow-up (59% to 7%, P ¼ 0.600) and first imaging-visit<br />
blood draw (59% to 0%, P ¼ not applicable). This data indicates that patients<br />
with newly diagnosed disease who are about to start first-line therapy can have<br />
detectable CTCs and that the changes in CTC status at three to four weeks may<br />
indicate a benefit from systemic treatment (particularly trastuzumab-based<br />
regimens). Moreover, the determination <strong>of</strong> CTCs at baseline and follow-up<br />
appears to be a superior prognostic marker in patients with measurable MBC<br />
compared with standard imaging assessments (32). CTCs could be used for<br />
assessment <strong>of</strong> the clinical benefit <strong>of</strong> systemic treatments, for prognostic<br />
stratification, and for evaluation <strong>of</strong> patients with nonmeasurable disease in<br />
future prospective validation trials.<br />
STAGING MBC USING CTCs<br />
The American Joint Committee on <strong>Cancer</strong> (AJCC) classification system, based<br />
on a schema developed by the International Union Against <strong>Cancer</strong> (UICC)<br />
(33–35), includes much <strong>of</strong> the traditional prognostic information used by clinicians<br />
when developing a comprehensive treatment plan. In brief, the AJCC
160 Reuben and Crist<strong>of</strong>anilli<br />
system attempts to define the disease by incorporating all aspects <strong>of</strong> cancer<br />
distribution in terms <strong>of</strong> the primary tumor (T), lymph nodes (N), and distant<br />
metastasis (M). A “stage” group (0, I, II, III, or IV) is then assigned on the basis<br />
<strong>of</strong> the possible TNM permutations, with 0 reflecting minimal involvement and<br />
IV either the maximum tumor involvement or distant metastasis. This basic<br />
TNM staging is then further subdivided according to the time the evaluation is<br />
performed: c ¼ clinical (before surgical treatment) and p ¼ pathologic (after<br />
surgical specimen analysis) (10,35).<br />
Over the past 45 years, the AJCC has regularly updated its staging standards<br />
to incorporate advances in prognostic technology. The committee’s current<br />
work concerns the development <strong>of</strong> prognostic indices based on molecular<br />
markers. However, until these changes are incorporated, TNM staging continues<br />
to quantify only the physical extent <strong>of</strong> the disease. Although it covers the<br />
approximately 2% <strong>of</strong> women who present with metastatic disease (stage IV), the<br />
prognostic information is applicable only to in situ, local, and regional primary<br />
breast cancers. Given the heterogeneity <strong>of</strong> the disease in primary and metastatic<br />
settings, the potential for continued mutation, and the variety <strong>of</strong> treatment<br />
options available, the information acquired at the time <strong>of</strong> the initial diagnosis <strong>of</strong><br />
breast cancer may not be as relevant to planning the treatment for recurrence in<br />
the approximately 30% <strong>of</strong> women who have recurrent MBC years after their<br />
initial diagnosis.<br />
The recent demonstration that the presence <strong>of</strong> CTCs predicts the prognosis<br />
<strong>of</strong> two subgroups <strong>of</strong> patients with MBC raises the possibility that this method<br />
will allow for a true “biologic staging” <strong>of</strong> breast cancer (e.g., stages IVA and<br />
IVB) (11). The main limitations <strong>of</strong> the previous study were the sample size (only<br />
177 patients); the inclusion only <strong>of</strong> patients with measurable disease, which does<br />
not reflect the heterogeneity <strong>of</strong> MBC; and the inclusion <strong>of</strong> patients at different<br />
points in treatment—those with newly diagnosed disease and those undergoing<br />
second- or third-line treatment. To overcome these limitations, a larger international<br />
validation trial, the International Stage IV Stratification Study, was<br />
begun. The objective <strong>of</strong> this study was to confirm the prognostic value <strong>of</strong> CTC<br />
detection in a larger and more homogeneous cohort <strong>of</strong> patients with newly<br />
diagnosed MBC. The study aimed at enrolling 660 patients and providing a more<br />
detailed analysis <strong>of</strong> the association between CTC detection and other factors<br />
such as ethnicity (black compared with white and Hispanic groups) and specific<br />
disease sites (visceral versus nonvisceral disease).<br />
CLINICAL APPLICATION OF CTCs<br />
Despite years <strong>of</strong> clinical research, the odds <strong>of</strong> patients with MBC achieving a<br />
complete response remain extremely low. Thus, the main goal in the management<br />
<strong>of</strong> MBC is palliation (36). Only a few patients who experience a complete<br />
response after chemotherapy remain in this state for prolonged periods, although
Circulating Tumor Cells in Individualizing <strong>Breast</strong> <strong>Cancer</strong> Therapy 161<br />
some have remained in remission for more than 20 years (37). These long-term<br />
survivors are usually young, have an excellent performance status, and, more<br />
importantly, have limited metastatic disease (37,38). Most patients with MBC<br />
respond only transiently to conventional therapies and develop evidence <strong>of</strong><br />
progressive disease within 12 to 24 months <strong>of</strong> starting treatment. For these<br />
patients, systemic treatment does not result in a significant improvement in<br />
survival time but may improve their quality <strong>of</strong> life.<br />
At present, clinicians use three different systemic treatment modalities for<br />
advanced breast cancer: endocrine therapy, chemotherapy, and biologic targeted<br />
therapy (39–42). Appropriate selection <strong>of</strong> patients for these modalities is based<br />
mostly on tissue assessment <strong>of</strong> hormone receptor status (estrogen and progesterone<br />
receptors) and c-erbB-2 status. In patients who lack expression <strong>of</strong> hormone<br />
receptors or who demonstrate no amplification <strong>of</strong> c-erbB-2, cytotoxic<br />
chemotherapy is used. However, no standard therapeutic regimen has been<br />
defined. For example, the optimal schedule <strong>of</strong> chemotherapy administration in<br />
MBC (i.e., concurrent vs. sequential) remains controversial, and the decision<br />
must be individualized. Sledge et al. addressed this issue in a prospective study<br />
that included 739 chemotherapy-naïve patients who were randomly assigned to<br />
receive, at progression, doxorubicin, paclitaxel, or both (39). Although the<br />
response rates and times to treatment failure were improved with the combination<br />
regimen, the overall survival rate was comparable in all groups. Other trials<br />
have demonstrated a survival advantage with combination regimens over singleagent<br />
chemotherapy, but all <strong>of</strong> these studies have shown differences in the actual<br />
median overall survival time between 10 and 13 months across studies, suggesting<br />
that even with comparable inclusion criteria, the heterogeneity that is<br />
typical <strong>of</strong> MBC cannot be eliminated (41,42). This heterogeneity is indeed one<br />
<strong>of</strong> the major limitations to the development <strong>of</strong> more personalized treatments for<br />
patients. Therefore, although the data demonstrate that patients can benefit from<br />
combination therapy, they do not clearly identify the subsets <strong>of</strong> patients who will<br />
most benefit or who will experience only additional toxicity.<br />
In this context, the use <strong>of</strong> CTCs to stratify patients at the time <strong>of</strong> disease<br />
recurrence may be an appropriate way to design personalized therapeutic<br />
approaches. CTC detection may enable a more rational selection <strong>of</strong> treatments<br />
for patients with newly recurrent disease, and this approach could maximize<br />
the chance <strong>of</strong> a particular combination or single new drug showing clinical<br />
benefit and, eventually, prolonging survival. It is possible that CTC detection<br />
could be used in the design <strong>of</strong> efficacy trials <strong>of</strong> different therapeutic<br />
approaches. The efficacy <strong>of</strong> these treatments could be more easily assessed if<br />
patients were stratified by their prognosis, leading to more tailored treatment<br />
strategies. We believe that the challenge for the next generation <strong>of</strong> clinical<br />
trials, and the responsibility for both clinical investigators and the pharmaceutical<br />
industry will be to incorporate these concepts into the process <strong>of</strong> drug<br />
development.
162 Reuben and Crist<strong>of</strong>anilli<br />
FUTURE USES OF CTCs<br />
The process <strong>of</strong> sorting cancer cells from other cellular components (e.g., blood<br />
and stromal cells) in clinical samples is fundamentally important for the future <strong>of</strong><br />
genomic and proteomic analysis (43–45). Collecting representative tissue from<br />
solid tumor metastases usually requires more invasive procedures that increase<br />
the risk <strong>of</strong> complications and discomfort. Furthermore, these procedures may not<br />
provide an adequate specimen for detailed analysis and typically cannot be<br />
repeated for dynamic evaluation <strong>of</strong> the biologic changes during treatments.<br />
Theoretically, CTC detection would allow specific genes [e.g., c-erbB-2, epidermal<br />
growth factor receptor (EGFR), and mammaglobin B (MGB)] or more global<br />
gene expression to be analyzed while using specific targeted treatments for MBC<br />
based on the expression <strong>of</strong> the CTCs (29,46–48). This information could then be<br />
used to design specific treatments that more appropriately reflect the dynamics and<br />
heterogeneity <strong>of</strong> MBC.<br />
CONCLUSIONS<br />
The detection <strong>of</strong> microscopic disease in the peripheral blood <strong>of</strong> patients with<br />
MBC provides prognostic information. This information will allow appropriate<br />
risk stratification and modification <strong>of</strong> the current staging system for advanced<br />
disease. Furthermore, the intriguing hypothesis that CTCs are, at least in part,<br />
representative <strong>of</strong> tumorigenic cancer stem cells may present an additional<br />
challenge for translational research. In fact, the phenotypic evaluation <strong>of</strong> CTCs is<br />
indicating the possibility <strong>of</strong> more critical design <strong>of</strong> targeted therapies in the<br />
setting <strong>of</strong> MBC. Moreover, with the introduction <strong>of</strong> this novel concept, it is<br />
expected that investigators and the pharmaceutical industry will derive benefits<br />
from these sorting technologies that will contribute to more tailored treatments<br />
and sophisticated trial designs.<br />
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12<br />
Individualization <strong>of</strong> Endocrine Therapy<br />
in <strong>Breast</strong> <strong>Cancer</strong><br />
Amelia B. Zelnak and Ruth M. O’Regan<br />
Emory Winship <strong>Cancer</strong> Institute, Atlanta, Georgia, U.S.A.<br />
Clodia Osipo<br />
Loyola University, Maywood, Illinois, U.S.A.<br />
INTRODUCTION<br />
Discovery <strong>of</strong> estrogen receptors (ER) was critical for the development <strong>of</strong><br />
endocrine therapy in breast cancer. Expression <strong>of</strong> ERa, thepredominantis<strong>of</strong>orm,<br />
in breast tumors <strong>of</strong> both premenopausal and postmenopausal women is a<br />
highly predictive marker for response to antiestrogen treatment in women with<br />
ERa-positive breast cancer. Today, endocrine therapies include the use <strong>of</strong><br />
antiestrogens such as tamoxifen and aromatase inhibitors such as anastrozole,<br />
exemestane, and letrozole. Tamoxifen, the first antiestrogen to be used for the<br />
treatment <strong>of</strong> ERa-positive breast cancer, competitively blocks the actions <strong>of</strong><br />
17b-estradiol (E 2 ), the female hormone that binds and activates ERa in tumors.<br />
In postmenopausal women, peripheral aromatization <strong>of</strong> androgens to estrogens<br />
is the major source <strong>of</strong> plasma estrogen. Aromatase inhibitors inhibit this<br />
reaction and consequently suppress the production <strong>of</strong> circulating estrogen in<br />
postmenopausal women. Endocrine therapies are effective at reducing recurrence,<br />
increasing overall survival, and reducing contralateral breast cancer up<br />
to 50%. However, about 50% <strong>of</strong> patients with ERa-positive breast cancer have<br />
167
168 Zelnak et al.<br />
intrinsic resistance to antiestrogen therapy and therefore do not benefit. In<br />
contrast to patients with intrinsically resistant tumors, there are patients who do<br />
initially respond to antiestrogen therapy; however, most <strong>of</strong> these patients<br />
develop acquired resistance during the treatment regimen. Therefore, the<br />
current goal in breast cancer research is to elucidate the mechanisms <strong>of</strong> both<br />
intrinsic and acquired resistance to tamoxifen and the aromatase inhibitors in<br />
order to develop new therapeutic strategies to prevent and/or treat resistant<br />
breast cancer.<br />
ANTIESTROGENS<br />
Nonsteroidal antiestrogens were initially developed as contraceptives in the<br />
1960s. Walpole and colleagues synthesized tamoxifen (termed ICI 46, 474), a<br />
potent antiestrogen with antifertility properties in rats. However, in humans,<br />
tamoxifen induced ovulation in subfertile women. Therefore, the development<br />
<strong>of</strong> tamoxifen as a contraceptive was terminated. However, Walpole also<br />
patented the application <strong>of</strong> tamoxifen as a drug treatment for hormonedependent<br />
cancers. Thus, clinical trials were started to evaluate tamoxifen<br />
against the standard endocrine treatment at the time, diethylstilbestrol (DES),<br />
for the treatment <strong>of</strong> advanced breast cancer in postmenopausal women (1).<br />
Tamoxifen not only was as effective as DES for the treatment <strong>of</strong> advanced<br />
breast cancer but also had fewer side effects. Therefore, the advantage <strong>of</strong><br />
tamoxifen over DES was crucial for its subsequent evaluation as a treatment for<br />
all stages <strong>of</strong> breast cancer.<br />
In 1962, Jensen and colleagues discovered ERa. Jensen demonstrated<br />
that E 2 , the circulating female hormone that promoted breast cancer growth,<br />
binds to diverse tissue sites around a woman’s body but is retained in estrogentarget<br />
tissues, for example, the uterus and vagina (2). The identification <strong>of</strong><br />
ERa as the target <strong>of</strong> E 2 action in the breast and antiestrogens blocking the<br />
binding <strong>of</strong> E 2 to ERa provided a therapeutic target and an approach for the<br />
treatment <strong>of</strong> breast cancer. Although many antiestrogens were discovered and<br />
tested during the 1960s and 1970s, only tamoxifen was considered safe enough<br />
for extensive clinical evaluation (3). Clinical trials ultimately demonstrated<br />
that patients with ERa-positive breast cancer benefited the most from<br />
tamoxifen therapy, whereas women with ERa-negative breast cancer were<br />
found to be unaffected. During subsequent clinical trials, five years <strong>of</strong> adjuvant<br />
tamoxifen treatment was found to be more effective than less than five<br />
years <strong>of</strong> treatment in improving time to tumor recurrence and overall survival<br />
(4). In contrast to the beneficial effects <strong>of</strong> tamoxifen as a treatment for breast<br />
cancer, both laboratory and clinical results showed that tamoxifen increased<br />
the risk <strong>of</strong> endometrial cancer in the uterus fourfold in postmenopausal women<br />
compared with untreated women (5,6). These results strongly indicated that<br />
tamoxifen was not a pure antiestrogen but had selective functions depending<br />
on the target tissue.
Individualization <strong>of</strong> Endocrine Therapy in <strong>Breast</strong> <strong>Cancer</strong> 169<br />
Resistance to Tamoxifen<br />
Overview<br />
Five years <strong>of</strong> adjuvant tamoxifen therapy is a standard <strong>of</strong> care for early-stage ERapositive<br />
breast cancer. Although there is increasing use <strong>of</strong> aromatase inhibitors in<br />
postmenopausal women, tamoxifen is the only approved effective therapy in premenopausal<br />
women. Approximately 50% <strong>of</strong> patients with ERa-positive breast<br />
cancer benefit from tamoxifen treatment in the advanced- and early-stage settings<br />
(4,7). Unfortunately, the other 50% <strong>of</strong> patients do not respond, and the ones that do<br />
respond initially eventually progress. Thus, the problem with tamoxifen therapy is<br />
that breast tumors can either be resistant (intrinsic or de novo) prior to endocrine<br />
treatment or become resistant (acquired) during therapy. Numerous studies<br />
have looked at the mechanisms responsible for this resistant phenotype. They<br />
include the loss <strong>of</strong> the ERa gene and/or protein in tumors during short-term and<br />
long-term treatment, identification <strong>of</strong> mutations within the ERa gene, changes in<br />
the pharmacologic response <strong>of</strong> resistant breast cancer cells to tamoxifen, activation<br />
<strong>of</strong> ERa-mediated gene transcription in the absence <strong>of</strong> ligand, modifications in gene<br />
transcription by changes in interactions between ERa and coregulators, and the<br />
development <strong>of</strong> enhanced surface-to-intracellular signaling cross-talk between<br />
growth factor receptors and ERa (Fig. 1).<br />
Figure 1<br />
Mechanisms <strong>of</strong> hormone resistance.
170 Zelnak et al.<br />
ER-Negative <strong>Breast</strong> <strong>Cancer</strong> and Resistance<br />
<strong>Breast</strong> cancers initially lacking expression <strong>of</strong> ERa have intrinsic resistance to<br />
endocrine therapy. In addition, loss <strong>of</strong> ERa expression upon recurrence <strong>of</strong> breast<br />
cancer during tamoxifen therapy has been reported in as many as 25% <strong>of</strong> tumors<br />
(8–10). However, most <strong>of</strong> the acquired resistance during adjuvant tamoxifen<br />
treatment in ERa-positive tumors is not due to changes in ERa expression.<br />
Interestingly, most tamoxifen-resistant breast tumors retain ERa and respond to<br />
secondary endocrine treatments. Overall, a loss <strong>of</strong> ERa status does not seem to<br />
be the major mechanism for the development <strong>of</strong> acquired antiestrogen resistance.<br />
ER Mutations<br />
Several mutant forms <strong>of</strong> both the ERa and ERb, another ER is<strong>of</strong>orm, have been<br />
identified and previously reviewed (11–13). However, the functional significance<br />
<strong>of</strong> these mutants as a mechanism <strong>of</strong> antiestrogen resistance is yet to be<br />
elucidated. Most <strong>of</strong> the data demonstrate the existence <strong>of</strong> splice variants for ERa<br />
and ERb mRNAs without evidence <strong>of</strong> translation into functional proteins. Most<br />
<strong>of</strong> the tumors express both mutant and wild-type ER with the wild type being the<br />
predominant species. A single-point mutation in the ligand-binding domain<br />
(LBD) <strong>of</strong> ERa has been reported (D351Y) that converts tamoxifen and ralox<br />
ifene from antiestrogen to estrogens in in vivo and in vitro models <strong>of</strong> antiestrogenstimulated<br />
MCF-7 tumor cells (14,15). However, the D351Y ERa mutant has yet<br />
to be detected in either intrinsic or acquired resistant breast tumors from patients.<br />
In addition, mutations in the F-region <strong>of</strong> the ER have been shown to affect<br />
the activities <strong>of</strong> both E 2 and 4-hydroxytamoxifen (4OHT), the active metabolite<br />
<strong>of</strong> tamoxifen (16). Therefore, the clinical relevance <strong>of</strong> ER mutants is unclear to<br />
date as a mechanism <strong>of</strong> resistance to antiestrogens. However, our understanding<br />
<strong>of</strong> ER mutations in response to antiestrogens might lead to the development <strong>of</strong><br />
better antiestrogens or other drugs for breast cancer treatment.<br />
Role <strong>of</strong> <strong><strong>Ph</strong>armacogenetics</strong><br />
Tamoxifen undergoes extensive primary and secondary metabolism. The secondary<br />
metabolite 4-hydroxy-N-desmethyl tamoxifen (endoxifen) is formed by<br />
the CYP2D6-mediated oxidation <strong>of</strong> N-desmethyl tamoxifen and has been shown<br />
to play an important role in the anticancer effect <strong>of</strong> tamoxifen. CYP2D6 genotyping<br />
has revealed that women can be classified as either poor, intermediate, or<br />
extensive metabolizers <strong>of</strong> tamoxifen. Women who are poor metabolizers <strong>of</strong><br />
tamoxifen, and, therefore, have lower levels <strong>of</strong> endoxifen, have been shown to<br />
have worse disease-free survival. No difference in overall survival has been<br />
shown (17). Women who are extensive metabolizers <strong>of</strong> tamoxifen have also been<br />
shown to experience more hot flashes. A recent study showed that women who<br />
experience hot flashes while on tamoxifen had a hazard ratio <strong>of</strong> recurrence <strong>of</strong>
Individualization <strong>of</strong> Endocrine Therapy in <strong>Breast</strong> <strong>Cancer</strong> 171<br />
0.51 compared with women without hot flashes (18). This study suggests that<br />
vasomotor symptoms and efficacy <strong>of</strong> tamoxifen may be related to a women’s<br />
pharmacogenetics.<br />
Role <strong>of</strong> Coregulators in Resistance<br />
The recruitment <strong>of</strong> coactivators or corepressors to the ER determines the switch<br />
between ER activation and repression. In addition, the coordinated action <strong>of</strong><br />
ligand, ER, and coregulators determines which genes are transcribed or repressed<br />
depending on the cellular context and thus which cells will or will not proliferate.<br />
The overall data indicate that intricate modulation <strong>of</strong> the ER-to-coregulator ratio<br />
in breast cancer cells could determine resistance to antiestrogens. For example,<br />
the coactivator SRC-1 activates ER in a ligand-independent manner while<br />
increasing 4OHT’s agonist activity (19). On the other hand, the corepressor<br />
SMRT blocks the agonist activity <strong>of</strong> 4OHT-induced by SRC-1 (20). The other<br />
member <strong>of</strong> the p160 coactivator family is amplified in breast cancer (AIB1,<br />
SRC-3, and RAC3). AIB1 mRNA was found to be amplified in 60% <strong>of</strong> breast<br />
cancers, and the protein product was found to be overexpressed in about 10% <strong>of</strong><br />
tumors. Recently, an association was discovered between high AIB1 and human<br />
epidermal growth factor receptor 2 (HER2/neu), a member <strong>of</strong> the epidermal<br />
growth factor receptor (EGFR) family <strong>of</strong> receptor tyrosine kinases and tamoxifenresistant<br />
breast cancers (20). The potential mechanism for this resistance requires<br />
activation <strong>of</strong> the mitogen-activated protein kinase (MAPK) signaling cascade by<br />
HER2/neu, which in turn potentially phosphorylates and activates AIB1. Activated<br />
AIB1 in turn can convert 4OHT from an antiestrogen to an estrogen in<br />
regards to ERa activity. In contrast, low levels <strong>of</strong> N-CoR mRNA, a corepressor,<br />
have been implicated with resistance to tamoxifen (21). Therefore, the identification<br />
<strong>of</strong> abnormally expressed coregulators will probably assist in the design<br />
<strong>of</strong> future therapies that improve the efficacy <strong>of</strong> endocrine treatment without the<br />
development <strong>of</strong> resistance.<br />
EGFR and HER2/Neu and Antiestrogen Resistance<br />
Numerous laboratory and clinical studies indicate that overexpression and/or<br />
aberrant activity <strong>of</strong> the HER2/neu (erbB2) signaling pathway is associated with<br />
antiestrogen resistance in breast cancer (22). The HER2/neu receptor is a<br />
member <strong>of</strong> the EGFR family <strong>of</strong> receptor tyrosine kinases, which include HER3<br />
(erbB3) and HER4 (erbB4) (23–26). Upon dimerization, the tyrosine kinase<br />
domains located within the COOH-terminal regions <strong>of</strong> receptors are activated by<br />
an autophosphorylation cascade on specific tyrosine residues, which activate<br />
downstream effectors, such as MAPK and Akt, which promote cellular proliferation,<br />
survival, anti-apoptosis, and transformation. HER2/neu is overexpressed<br />
and/or amplified in 25% to 30% <strong>of</strong> breast tumors and is associated with a more<br />
aggressive phenotype and poor prognosis (27). Patients with breast tumors<br />
overexpressing HER2/neu exhibit much lower response rates to antiestrogen
172 Zelnak et al.<br />
therapy (27). Thus, it is suggested that one possible mechanism <strong>of</strong> resistance to<br />
tamoxifen is overexpression <strong>of</strong> HER2/neu in ERa-positive breast cancers. In<br />
addition, overexpression <strong>of</strong> EGFR and its ligands are observed in several human<br />
cancers including breast cancer (28–30). The increased expression <strong>of</strong> EGFR is<br />
frequently associated with tumor progression and resistance to antiestrogens.<br />
These data suggest that EGFR and/or HER2/neu are possible targets for preventing<br />
or treating antiestrogen resistance in breast cancer. Strategies to target<br />
EGFR and HER2/neu include the use <strong>of</strong> humanized monoclonal antibodies to<br />
the receptors (31), tyrosine kinase inhibitors that block reduction <strong>of</strong> adenosine<br />
triphosphate (ATP) to adenosine diphospohate (ADP) þ Pi, and receptor antisense<br />
molecules (32). Gefitinib, (ZD 1839, Iressa 1 ), an EGFR-specific tyrosine kinase<br />
inhibitor, has been shown to inhibit growth <strong>of</strong> breast cancer cell lines in vitro that<br />
are resistant to tamoxifen (33). More importantly, the combination <strong>of</strong> gefitinib<br />
and tamoxifen was shown to prevent resistance (34), demonstrating that (1)<br />
EGFR might be a key player in the development <strong>of</strong> antiestrogen resistance and<br />
(2) inhibiting the activity <strong>of</strong> this receptor might be therapeutically beneficial in<br />
preventing resistance to antiestrogens. In addition to gefitinib, trastuzumab<br />
(Herceptin 1 ) is a humanized monoclonal antibody directed against the ectodomain<br />
<strong>of</strong> the HER2/neu receptor. It has been shown to restore breast cancer cell<br />
sensitivity to tamoxifen in HER2/neu-overexpressing cells (35).<br />
Role <strong>of</strong> MAPK<br />
MAPK is a serine/threonine kinase that is activated by phosphorylation cascades<br />
originating from GTP-bound Ras, a downstream effector <strong>of</strong> EGFR and HER2/<br />
neu. MAPK has been shown to be hyperactivated in MCF-7 breast cancer cells<br />
overexpressing either EGFR or HER2/neu (36–38). This increased activity <strong>of</strong><br />
MAPK promoted an enhanced association <strong>of</strong> ERa with coactivators and<br />
decreased interaction with corepressors, thereby enhancing hormone-dependent<br />
gene transcription and possibly leading to tamoxifen-resistant breast cancer. The<br />
role <strong>of</strong> the MAPK signaling pathway in tamoxifen resistance has been demonstrated<br />
primarily in vitro using specific inhibitors such as U0126 (MAPKK,<br />
MEK1/2 inhibitor), AG1478 (EGFR and HER2/neu inhibitors), and PD98059<br />
(MAPK, ERK1/2 inhibitor) (36–38). The exact mechanism by which hyperactivated<br />
MAPK converts the antiestrogenic activity <strong>of</strong> the tamoxifen-ERa<br />
complex to more <strong>of</strong> an estrogenic one in resistant breast cancer cell is yet<br />
unclear. However, it has been demonstrated that activation <strong>of</strong> the Ras/MAPK<br />
pathway by EGFR/HER2/neu receptor activation could phosphorylate Ser-118 in<br />
the AF-1 domain <strong>of</strong> ERa, thus promoting ligand-independent transcription <strong>of</strong><br />
ERa and possibly loss <strong>of</strong> tamoxifen-induced inhibition <strong>of</strong> ERa-mediated gene<br />
transcription. On the basis <strong>of</strong> these preclinical models, several clinical trials are<br />
under way to determine whether treating patients with EGFR inhibitors (i.e.,<br />
Iressa) and/or MAPK-specific inhibitors (U0126) can effectively treat and/or<br />
prevent antiestrogen resistance in breast tumors.
Individualization <strong>of</strong> Endocrine Therapy in <strong>Breast</strong> <strong>Cancer</strong> 173<br />
Role <strong>of</strong> PI3-K/AKT Signaling<br />
<strong>Ph</strong>osphatidylinositol 3-kinase (PI3-K) is a heterodimer complex consisting <strong>of</strong><br />
a regulatory subunit, p85, and a catalytic subunit, p110. The regulatory subunit<br />
p85 is phosphorylated by either receptor tyrosine kinases such as EGFR/<br />
HER2/neu or intracellular adapter kinases such as Shc or Src and thereafter<br />
interacts and activates the catalytic subunit p110 (39). The activated PI3-K<br />
subsequently activates downstream effector kinases such as Akt (PKB), a<br />
serine/threonine kinase. Akt has been shown to activate either directly or<br />
indirectly proteins responsible for prosurvival and anti-apoptosis in cancer<br />
cells. Several reports indicate that the PI3-K/Akt pathway interacts with the<br />
ERa pathway, resulting in bidirectional cross talk. It has been shown that PI3-<br />
K/Akt can mediate E 2 -induced transcription <strong>of</strong> cyclin D1 and entry <strong>of</strong> MCF-7<br />
breast cancer cells into S-phase (40). In addition, the PI3-K/Akt pathway can<br />
induce ERa phosphorylation on Ser-167 to promote ERa-mediated transcription<br />
and thus protect cells against tamoxifen-induced apoptosis (41). The<br />
accumulating data suggest that ERa-positive breast tumors with alterations <strong>of</strong><br />
PI3-K and Akt signaling, whether dependent or independent <strong>of</strong> EGFR/HER2/<br />
neu, might be insensitive to antiestrogens and thus lead to resistance. Future<br />
studies are needed to confirm whether blocking PI3-K and/or Akt signaling in<br />
ERa-positive breast cancer will be beneficial in subverting resistance to<br />
antiestrogens.<br />
New SERMS<br />
New drug discovery for selective ER modulators (SERMs) is currently driven<br />
by the known side effects <strong>of</strong> tamoxifen, which include an increase in the<br />
incidence <strong>of</strong> endometrial cancer, development <strong>of</strong> resistance, and the recent<br />
report <strong>of</strong> the negative effects <strong>of</strong> hormone replacement therapy (HRT) on coronary<br />
heart disease (42). Several approaches are being pursued by altering the<br />
antiestrogenic side chain or improving the pharmacokinetics <strong>of</strong> existing molecules.<br />
Several novel antiestrogen compounds have been developed to replace<br />
tamoxifen for ERa-positive breast cancer without the agonist actions. These<br />
compounds have the potential to be more effective than tamoxifen, having<br />
the advantage <strong>of</strong> reducing the incidence <strong>of</strong> endometrial cancer and possibly<br />
acquired resistance during therapy. Two groups <strong>of</strong> antiestrogenic drugs exist<br />
today with the potential to replace tamoxifen: (1) The SERMs that include<br />
tamoxifen-like compounds (triphenylethylenes) such as toremifene, idoxifene,<br />
and GW 5638 and fixed-ring compounds (benzothiophenes) such as raloxifene,<br />
arzoxifene, EM 652, and CP 336,156, and (2) selective ER downregulators<br />
(SERDs and pure antiestrogen) such as ICI 182,780 (Fulvestrant 1 ). In phase II<br />
trials in patients with tamoxifen-resistant metastatic breast cancer, the new<br />
SERMs showed low response rates (0–15%) (43,44), suggesting crossresistance<br />
to tamoxifen. No difference in outcome was noted between<br />
tamoxifen and toremifene in patients with metastatic or early-stage breast
174 Zelnak et al.<br />
cancer (45,46). In contrast, few clinical trials exist today evaluating the<br />
effectiveness <strong>of</strong> the fixed-ring compounds to tamoxifen. The Study <strong>of</strong><br />
Tamoxifen and Raloxifene (STAR) trial compared the efficacy <strong>of</strong> raloxifene<br />
over tamoxifen as a chemopreventive for women at high risk for breast cancer.<br />
Both drugs reduced the risk <strong>of</strong> invasive breast cancer by approximately 50%.<br />
Tamoxifen also lowered the incidence <strong>of</strong> lobular carcinoma in situ (LCIS) and<br />
ductal carcinoma in situ (DCIS) by about 50 percent; however, raloxifene did<br />
not have an effect on the incidence <strong>of</strong> LCIS and DCIS (47). Of note, the<br />
incidence <strong>of</strong> uterine cancer was 36% lower, and the incidence <strong>of</strong> thromboembolic<br />
events was 29% lower in the raloxifene arm (47). Fulvestrant and<br />
other SERDs have been shown to have high affinity for ERs compared with<br />
tamoxifen with none <strong>of</strong> the agonist activities. In clinical trials, fulvestrant<br />
initially showed significant promise for the treatment <strong>of</strong> advanced breast<br />
cancer (48,49). However, in a phase III clinical trial versus tamoxifen for firstline<br />
therapy <strong>of</strong> advanced breast cancer, fulvestrant was not superior to<br />
tamoxifen in terms <strong>of</strong> time-to-treatment failure (50). Therefore, more work is<br />
needed to evaluate the efficacy <strong>of</strong> SERDs in comparison with SERMs for ERapositive<br />
breast cancer.<br />
AROMATASE INHIBITORS<br />
An alternate strategy to endocrine therapy, which specifically inhibits binding <strong>of</strong><br />
E 2 to the ER, is to inhibit the production <strong>of</strong> E 2 by blocking the cytochrome p450<br />
aromatase enzyme, the rate-limiting enzyme that converts androgens (i.e., testosterone<br />
and androstenedione) to estrogens (i.e., E 2 and estrone) in the adrenal<br />
gland, surrounding stroma, and adipose tissue <strong>of</strong> the breast tumor. The main<br />
drugs <strong>of</strong> this type are aromatase inhibitors, which include Type I (steroidal) or<br />
Type II (nonsteroidal) (Fig. 2). Steroidal inhibitors are competitive-substrate<br />
mimics <strong>of</strong> androstenedione. These include formestane and exemestane, which<br />
are irreversible inhibitors that bind with high affinity to the binding site <strong>of</strong><br />
aromatase and are converted to a covalently bound intermediate. Nonsteroidal<br />
inhibitors include the first-generation aromatase inhibitor aminoglutethimide and<br />
the second-generation compounds anastrozole and letrozole. All nonsteroidal<br />
aromatase inhibitors act by binding reversibly to the enzyme and competitively<br />
inhibiting binding <strong>of</strong> the substrate androstenedione. The benefits <strong>of</strong> using aromatase<br />
inhibitors over tamoxifen are believed to be the complete deprivation <strong>of</strong><br />
E 2 and thus better efficacy for ERa-positive breast cancer (51). Recent clinical<br />
data have clearly demonstrated that anastrozole (52), letrozole (53), and<br />
exemestane (54) are more effective than tamoxifen as first-line treatments in<br />
patients with metastatic breast cancer. On the basis <strong>of</strong> clinical results (52,53),<br />
currently both anastrozole and letrozole are approved by the Food and Drug<br />
Administration for first-line treatment <strong>of</strong> postmenopausal, ERa-positive<br />
advanced breast cancer. The data from advanced breast cancer trials provided the<br />
rationale to perform large-scale clinical trials to determine whether there is an
Individualization <strong>of</strong> Endocrine Therapy in <strong>Breast</strong> <strong>Cancer</strong> 175<br />
Figure 2<br />
Structures <strong>of</strong> selective aromatase inhibitors.<br />
advantage <strong>of</strong> using aromatase inhibitors over tamoxifen in the adjuvant setting.<br />
The anastrozole, tamoxifen, and the combination <strong>of</strong> anastrozole and tamoxifen<br />
(ATAC) trial has sufficient follow-up data to confirm that anastrozole is superior<br />
to tamoxifen as a first-line adjuvant therapy in ERa-positive breast cancer with<br />
regard to disease-free survival and incidence <strong>of</strong> contralateral breast cancer (55).<br />
The <strong>Breast</strong> International Group (BIG) 1-98 study demonstrated a similar<br />
improvement in event-free survival to confirm that letrozole is superior to<br />
tamoxifen as first-line adjuvant hormonal therapy (56). Other trials have demonstrated<br />
an improved outcome for postmenopausal patients with early-stage<br />
breast cancer treated with two to three years <strong>of</strong> tamoxifen, followed by an<br />
aromatase inhibitor, compared with five years <strong>of</strong> tamoxifen (57,58). The BIG 1-98<br />
trial will help to determine whether these sequencing/switching approaches are<br />
equivalent to upfront aromatase inhibitors.<br />
Mechanisms <strong>of</strong> Resistance<br />
Clinically, patients that relapse after a previous response to tamoxifen usually<br />
have a clinical response to aromatase inhibitors (59,60). These results strongly<br />
indicate that the ERa continues to be expressed and is functional in breast tumors<br />
that are resistant to antiestrogens. However, although estrogen deprivation<br />
treatment might be more effective than tamoxifen in delaying resistance, eventually<br />
resistance to aromatase inhibitors will also develop. To date, it is unclear
176 Zelnak et al.<br />
whether similar mechanisms <strong>of</strong> actions that have been identified for tamoxifen<br />
resistance are also involved in resistance to aromatase inhibitors. The exact<br />
mechanisms contributing to aromatase inhibitor resistance has yet to be fully<br />
elucidiated. However, in vitro studies have identified mutations within the aromatase<br />
gene that confers resistance to aromatase inhibitors (61). These mutations<br />
have not yet been identified in human breast carcinomas (62). Other studies have<br />
demonstrated that estrogen deprivation supersensitizes the breast cancer cell to<br />
low levels <strong>of</strong> estrogen, thus creating a hypersensitive environment to overcome<br />
estrogen deprivation resulting in resistance (63–65). In addition, results suggest<br />
that there is increased cross talk between growth factor receptor signaling<br />
pathways and ERa. ERa has been shown to become activated and supersensitized<br />
by several different intracellular kinases, including MAPKs, insulin-like growth<br />
factors, and the PI3-K/Akt pathway (41,66–69). Therefore, the data suggest that<br />
ERa continues to be an integral part <strong>of</strong> the breast cancer cell signaling pathway<br />
even after resistance to aromatase inhibitors has developed.<br />
Role <strong>of</strong> Progesterone Receptor and HER2/Neu<br />
There is emerging evidence to suggest that ER-positive cancers that do not<br />
express the progesterone receptor (PR) and/or HER2/neu are somewhat intrinsically<br />
resistant to tamoxifen and perhaps hormonal therapy in general. Arpino et<br />
al. have demonstrated an increased relapse rate in patients with ER-positive, PRnegative<br />
cancers compared with ER-positive, PR-positive cancers, treated with<br />
tamoxifen (70). Patients treated with tamoxifen with ER-positive, HER2-positive<br />
metastatic breast cancers have a shorter time to treatment failure compared with<br />
ER-positive, HER2-negative cancers (71). In fact, Arpino et al. have demonstrated<br />
an increase in both HER1 and HER2 in ER-positive, PR-negative cancers<br />
compared to ER-positive, PR-positive cancers, suggesting an interplay between<br />
the ER and epidermal growth factor pathways (70).<br />
Two very small trials demonstrated a significantly increased clinical<br />
response rate in patients with ER-positive, HER2-positive cancers treated with<br />
preoperative aromatase inhibitors compared to preoperative tamoxifen (72,73).<br />
This led to a widely accepted hypothesis that aromatase inhibitors were a better<br />
choice than tamoxifen in patients with ER-positive, HER2-positive cancers.<br />
However, an analysis <strong>of</strong> the BIG-1-98 trial demonstrates that letrozole improves<br />
outcome compared to tamoxifen in both ER-positive, HER2-positive cancers<br />
(HR 0.68) and in ER-positive, HER2-negative cancers (HR 0.72) (74). A recent<br />
subanalysis <strong>of</strong> the ATAC trial demonstrated a significantly improved outcome in<br />
ER-positive, HER2-negative cancers (HR 0.66) but not in ER-positive, HER2-<br />
positive cancers (HR 0.92), but this may have been due to the small number <strong>of</strong><br />
patients in the HER-positive group (75). Are HER2-positive cancers somewhat<br />
resistant to not just tamoxifen but also to aromatase inhibitors? As outlined<br />
above, a recent trial randomized patients with HR-positive, HER2-positive<br />
metastatic breast cancers to anastrozole alone or to anastrozole plus trastuzumab
Individualization <strong>of</strong> Endocrine Therapy in <strong>Breast</strong> <strong>Cancer</strong> 177<br />
(76). Although there was no significant difference in overall survival, possibly<br />
because patients randomized to anastrozole alone could receive trastuzumab at<br />
disease progression, the time to progression was doubled from 2.4 months in the<br />
anastrozole-alone arm to 4.8 months in the combined arm (p ¼ 0.0016) (76). The<br />
clinical benefit rate in the combination arm was 42%—significantly higher than<br />
in the anastrozole alone arm. A trial that evaluated single-agent trastuzumab as<br />
first-line therapy for patients with HER2-positive cancers demonstrated a clinical<br />
benefit rate <strong>of</strong> 48% (77). This suggests the intriguing possibility that HRpositive,<br />
HER2-positive cancers are driven by the HER2 pathway, which renders<br />
the cancers partly resistant to hormonal therapies.<br />
An initial evaluation <strong>of</strong> the ATAC trial using case report forms revealed<br />
that TTR was longer for anastrozole in both ER-positive/PR-positive and ERpositive/PR-negative<br />
subgroups, but the benefit was more pronounced in the ERpositive/PR-negative<br />
subgroup [HR 0.84, 95% confidence interval (CI) 0.69–<br />
1.02 vs. 0.43, 95% CI 0.31–0.61] (78). Importantly, the ER and PR analyses were<br />
not performed centrally. More recently, a central analysis <strong>of</strong> about 2000 patients<br />
on the ATAC trial demonstrated similar improvements with the use <strong>of</strong> anastrozole<br />
compared to tamoxifen, regardless <strong>of</strong> PR status (HR anastrozole vs.<br />
tamoxifen 0.72 for ER-positive, PR-positive subgroups and 0.66 for ER-positive,<br />
PR-negative subgroups) (75). In the BIG-1-98 trial, similar benefits for letrozole<br />
compared to tamoxifen were seen in the ER-positive/PR-positive and ERpositive/PR-negative<br />
subgroups (74).<br />
On the basis <strong>of</strong> this data, decisions regarding whether to start a patient on<br />
tamoxifen or an aromatase inhibtor should not be based on PR or HER2 status.<br />
Further molecular pr<strong>of</strong>iling may help in the future in making decisions regarding<br />
optimal hormonal therapies.<br />
Role <strong>of</strong> Gene Expression Analysis<br />
Recent studies have shown that that gene expression pr<strong>of</strong>iling <strong>of</strong> breast tumors<br />
can be used to predict outcome. The initial gene expression signatures were<br />
developed using snap-frozen tissue, limiting their clinical application (79). An<br />
assay (Oncotype DX, Genomic Health, Inc., Redwood City, California, U.S.) has<br />
been developed and validated in women with early-stage, node-negative breast<br />
cancer using fixed, paraffin-embedded tissue. The 21-gene panel includes genes<br />
involved with proliferation, invasion, and hormone response and classifies a<br />
tumor as being low, intermediate, or high risk <strong>of</strong> recurrence based on the level <strong>of</strong><br />
expression (80). Additional studies have shown that a woman’s recurrence score<br />
also predicts response to chemotherapy: women with low recurrence scores<br />
derive little benefit from adjuvant chemotherapy, whereas women with high<br />
recurrence scores benefit significantly from chemotherapy in addition to hormonal<br />
therapy. Most strikingly are the findings that breast cancers with a high<br />
recurrence score obtain minimal, if any, benefit from five years <strong>of</strong> tamoxifen<br />
(81). Given the fact that these cancers likely have higher expression <strong>of</strong> HER2
178 Zelnak et al.<br />
and lower quantitative hormone receptors, this is in keeping with the data outlined<br />
above, suggesting that HER2-positive cancers have intrinsic resistance to<br />
tamoxifen. It is not known if women with intermediate recurrence scores benefit<br />
from chemotherapy in addition to hormonal therapy (82). The TAILORx (Trial<br />
Assigning Individualized Options for Treatment) trial is under way, randomizing<br />
early-stage breast cancer patients with intermediate recurrence scores to chemotherapy<br />
plus hormonal therapy versus hormonal therapy alone.<br />
FUTURE RESEARCH AND THERAPEUTICS<br />
It is clear that the central regulator <strong>of</strong> growth for ERa-positive breast tumors is<br />
ERa. ER signaling is evidently not isolated from other cellular signaling pathways.<br />
Research studies demonstrate that there is bidirectional cross talk between<br />
EGFR/HER2/neu, MAPK, PI3-K/Akt, and ERa, which ultimately affects the<br />
cellular response <strong>of</strong> a cell to estrogens and mitogens. ERa integrates numerous<br />
signals from hormones, growth factors, and intracellular kinases along with the<br />
array <strong>of</strong> coregulators to modulate cellular physiology and tumor pathology. The<br />
first generation antiestrogen/SERM, tamoxifen, has been effective at increasing<br />
overall survival <strong>of</strong> patients with ERa-positive breast cancer. However, the<br />
eventual development <strong>of</strong> resistance to tamoxifen is common because <strong>of</strong> the<br />
multiple signaling networks affecting the function <strong>of</strong> ERa. As a result <strong>of</strong> this<br />
limitation, intense research over the past 25 years has revealed the need for<br />
alternate treatment strategies to tamoxifen such as newer and better SERMs with<br />
less agonist activity in the uterus while being full antagonists in the breast.<br />
In addition to SERMs, other therapeutic approaches have emerged such as<br />
the use <strong>of</strong> aromatase inhibitors to prevent the synthesis <strong>of</strong> E 2 and thus deprive<br />
breast cancer cells and other cells <strong>of</strong> E 2 . The strategy <strong>of</strong> estrogen deprivation<br />
may also have consequences such as resistance, bone loss, and/or dementia. In<br />
the ATAC and BIG 1-98 adjuvant trials, changes in bone mineral density (BMD)<br />
and increased incidence <strong>of</strong> bone fractures were reported. Longer follow-up in<br />
these studies will help determine if the decrease in BMD stabilizes over time or<br />
if it continues throughout the course <strong>of</strong> treatment.<br />
Therapeutic opportunities also exist from research studies investigating the<br />
role <strong>of</strong> growth factor receptors EGFR and HER2/neu and their intracellular<br />
signaling communicators MAPK and PI3-K/Akt in the development <strong>of</strong> tamoxifen<br />
resistance. Trastuzumab, the HER2/neu-specific antibody, has been shown<br />
to be highly effective in treating breast tumors overexpressing HER2/neu.<br />
Moreover, research data indicate a role for HER2/neu in antiestrogen resistance,<br />
demonstrating a rationale for using trastuzumab to treat or prevent antiestrogenresistant<br />
breast cancer. EGFR inhibitors and other tyrosine kinase inhibitors may<br />
also prove to be beneficial in preventing resistance when given in combination<br />
with tamoxifen or other SERMs or SERDs such as fulvestrant.<br />
In addition to EGFR and HER2/neu as important modulators <strong>of</strong> tamoxifen<br />
action, dissecting the specific interrelationship between ERa and its coregulators
Individualization <strong>of</strong> Endocrine Therapy in <strong>Breast</strong> <strong>Cancer</strong> 179<br />
may open the door for novel therapeutics. An understanding <strong>of</strong> how ERamediated<br />
gene transcription is dysregulated in breast cancer and how this<br />
changes in resistance during endocrine therapy will hopefully lead to improved<br />
strategies for the use <strong>of</strong> current and future antiestrogen therapies.<br />
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52. Bonneterre J, Buzdar A, Nabholtz JM, et al. Anastrozole is superior to tamoxifen as<br />
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53. Mouridsen H, Gershanovich M, Sun Y, et al. Superior efficacy <strong>of</strong> letrozole versus<br />
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2006; 24:3796–3834.
13<br />
TAILORx: Rationale for the Study Design<br />
Joseph A. Sparano<br />
Albert Einstein College <strong>of</strong> Medicine, Montefiore Medical Center,<br />
Bronx, New York, U.S.A.<br />
INTRODUCTION<br />
There have been substantial technical and analytical advances within the past<br />
decade that have facilitated high-throughput analysis <strong>of</strong> clinical specimens, a<br />
process that has been referred to as “molecular pr<strong>of</strong>iling.” This term, as applied<br />
here, may refer to evaluating various “markers,” including genomic, proteomic,<br />
and epigenomic expression patterns, or a combination <strong>of</strong> these patterns, in<br />
clinical specimens. Technological advances have led to the ability to measure<br />
thousands <strong>of</strong> markers with a variety <strong>of</strong> validated methods (1), and analytical<br />
models have been developed that facilitate analysis <strong>of</strong> the voluminous amount <strong>of</strong><br />
data that are generated (2). The technology has also led to an ability to perform<br />
“discovery-based research,” in which large volumes <strong>of</strong> data are generated and<br />
analyzed without a specific hypothesis, in contrast to the traditional scientific<br />
paradigm <strong>of</strong> “hypothesis-based research,” in which a limited number <strong>of</strong> genes or<br />
proteins are based upon a specific hypothesis and rationale (3). Discovery-based<br />
research and hypothesis-based research are not mutually exclusive; however,<br />
pr<strong>of</strong>iling may also be used to test specific hypotheses that are based on sound,<br />
scientific rationale. A series <strong>of</strong> studies have been reported over the past few<br />
years that have utilized gene expression pr<strong>of</strong>iling to discover “molecular<br />
markers,” which may identify (i) distinct molecular subtypes <strong>of</strong> breast cancer<br />
185
186 Sparano<br />
(i.e., genotypic-phenotypic correlation), (ii) molecular signatures–associated<br />
prognosis (i.e., prognostic factors), and (iii) molecular signatures that predict<br />
the benefit from specific therapies (i.e., predictive factors) (4). When a<br />
“molecular marker” is shown to perform more reliably than clinical features in<br />
predicting prognosis or the benefit from a specific intervention, then there is<br />
interest in further evaluating the utility <strong>of</strong> the marker in clinical practice (5).<br />
PROGRAM FOR THE ASSESSMENT OF CLINICAL CANCER TESTS<br />
In order to address the issue <strong>of</strong> integrating molecular diagnostic testing into<br />
clinical practice, the U.S. National <strong>Cancer</strong> Institute initiated the program for the<br />
assessment <strong>of</strong> clinical cancer tests (PACCT) (http://www.cancerdiagnosis.nci<br />
.nih.gov/assessment/index.html). The goals <strong>of</strong> PACCT are to ensure translation<br />
<strong>of</strong> new knowledge about cancer and new technologies to clinical practice and<br />
development <strong>of</strong> more informative laboratory tools to help maximize the impact<br />
<strong>of</strong> cancer treatments. This effort has led to the publication <strong>of</strong> the REMARK<br />
(REporting recommendations for tumour MARKer prognostic studies)<br />
guidelines (6) and to the TAILORx (Trial Assigning IndividuaLized Options<br />
for Treatment) trial developed by the North American <strong>Breast</strong> <strong>Cancer</strong> Intergroup<br />
(http://www.cancer.gov/clinicaltrials/digestpage/TAILORx). In reviewing<br />
the rationale for design <strong>of</strong> the TAILORx trial in breast cancer, the following<br />
questions were considered by the <strong>Breast</strong> <strong>Cancer</strong> Intergroup and are reviewed<br />
herein:<br />
l<br />
l<br />
l<br />
l<br />
What therapeutic intervention should the marker be used to select?<br />
What patient population should be evaluated?<br />
Which marker should be evaluated?<br />
What trial design would integrate current knowledge about the marker and<br />
address gaps in our knowledge about the current marker and future potential<br />
markers?<br />
WHICH TREATMENT INTERVENTION?<br />
Therapeutic options are used in an adjunctive manner for patients with earlystage<br />
breast cancer after surgery, irradiation, endocrine therapy, chemotherapy,<br />
and trastuzumab (Table 1). In contrast to irradiation, endocrine therapy, and<br />
trastuzumab, there are no clinical factors that may be used to predict the benefit<br />
from adjuvant chemotherapy. Chemotherapy is usually recommended on the<br />
basis <strong>of</strong> the risk <strong>of</strong> recurrence (i.e., prognostic factors) and age (7). In contrast<br />
to other therapies, the treatment effect <strong>of</strong> chemotherapy is modest, acute toxicities<br />
are frequent, and the cost is high (8). Therefore, evaluation <strong>of</strong> a marker<br />
that has the potential for predicting benefit from chemotherapy would be<br />
desirable.
TAILORx 187<br />
Table 1<br />
Standard Adjuvant Therapies for Operable <strong>Breast</strong> <strong>Cancer</strong><br />
Treatment<br />
Selection<br />
Percent<br />
eligible<br />
Treatment<br />
effect (%)<br />
Acute<br />
toxicity<br />
Relative<br />
cost<br />
Chemotherapy Recurrence risk Up to 90 25–35 High þþþ<br />
> 5–10%<br />
Endocrine ER- and/or PRpositive<br />
70 50 Low þþ<br />
therapy<br />
tumor<br />
Trastuzumab HER2/neu-positive 15 50 Low þþþþ<br />
tumor<br />
Radiation <strong>Breast</strong>-sparing surgery 40–60 90 Low þ<br />
Abbreviations: ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth<br />
factor receptor 2.<br />
WHICH PATIENT POPULATION?<br />
In 2006, approximately 124,000 women in the United States were diagnosed<br />
with estrogen receptor (ER)–positive breast cancer associated with negative<br />
axillary lymph nodes, accounting for about 50% <strong>of</strong> all breast cancer diagnosed<br />
each year and nearly 9% <strong>of</strong> all new cases <strong>of</strong> cancer (9). Treatment recommendations<br />
have generally been based on prognostic factors, with chemotherapy<br />
recommended if the residual risk <strong>of</strong> recurrence exceeds approximately 5% to<br />
10% despite adjuvant hormonal therapy (i.e., the tumor size exceeds 1 cm or<br />
smaller tumors are associated with unfavorable histologic features) (7). By these<br />
criteria, adjuvant chemotherapy could be recommended annually for up to<br />
75,000 women younger than 70 years in the United States. Currently, it is estimated<br />
that approximately 25% <strong>of</strong> all women, or about 30,000 annually, with earlystage<br />
ER-positive breast cancer receive adjuvant chemotherapy. Its use has<br />
increased substantially over the past 15 years (10) is highly dependent on age, and<br />
is estimated to be given to 75% <strong>of</strong> those younger than 50 years, 30% between the<br />
ages <strong>of</strong> 50 and 69 years, and about 5% <strong>of</strong> those 70 years or older.<br />
Approximately 85% <strong>of</strong> women with early-stage ER-positive breast cancer<br />
are therefore adequately treated with adjuvant hormonal therapy. Although<br />
adding chemotherapy reduces the risk <strong>of</strong> recurrence on average by about 30%,<br />
the absolute benefit for an individual patient is small, ranging from 1% to 5%<br />
(11). The vast majority <strong>of</strong> patients with ER-positive breast cancer are therefore<br />
overtreated with chemotherapy, since most would have been cured with hormonal<br />
therapy alone. Decision aids such as Adjuvant! Online (12), decision<br />
boards (13), and other tools, are <strong>of</strong>ten useful to assist patients and caregivers with<br />
information regarding absolute benefits that might be expected from chemotherapy<br />
(14). Although such decision aids may assist some patients in making a<br />
more informed decision regarding whether to accept adjuvant chemotherapy,<br />
when faced with a choice, many patients and their clinicians err on the side <strong>of</strong><br />
overtreatment because <strong>of</strong> the imprecise nature <strong>of</strong> predicting the treatment benefit
188 Sparano<br />
(15). Therefore, developing a clinical trial that employs a molecular marker to<br />
define the chemotherapy benefit was judged by the <strong>Breast</strong> <strong>Cancer</strong> Intergroup to<br />
be the preferred setting. Recent evidence demonstrating that the incremental<br />
benefits associated with contemporary chemotherapy regimens had minimal<br />
impact in high-risk ER-positive disease reinforces this approach (16).<br />
WHICH MARKER?<br />
A number <strong>of</strong> molecular diagnostic tests that provide more accurate prognostic<br />
information that standard clinical features have been developed and externally<br />
validated and are summarized in Table 2, including the Amsterdam 70-gene<br />
pr<strong>of</strong>ile (17,18), Rotterdam 76-gene pr<strong>of</strong>ile (19,20), and a 21-gene pr<strong>of</strong>ile (21,22).<br />
Recently, these assays have been shown to have comparable discriminatory<br />
value in early-stage breast cancer (23).<br />
The 21-gene Oncotype DX TM (Genomic Health, Inc., Redwood City,<br />
California U.S.) includes 16 tumor genes and 5 reference genes, with the result<br />
expressed as a computed “recurrence score” (RS) (Fig. 1) (21). The genes<br />
utilized in the assay were identified in several training set trials (24), followed by<br />
a validation study that was performed in patients with ER-positive, node-negative<br />
breast cancer who received a five-year course <strong>of</strong> tamoxifen (TAM) in<br />
National Surgical Adjuvant <strong>Breast</strong> and Bowel Project (NSABP) trial B-14 (21).<br />
A higher RS was associated with an elevated risk <strong>of</strong> distant recurrence, whether<br />
evaluated as a categorical or a continuous variable (Fig. 2). This assay was<br />
selected for evaluation in the TAILORx trial for several scientific and practical<br />
reasons that are summarized in Table 3 and discussed herein. In addition to the<br />
prospective validation study illustrated by Figure 2, a population-based external<br />
validation study performed in patients treated in the Kaiser Permanente health<br />
care system provided further evidence for the robustness <strong>of</strong> the assay (22).<br />
Moreover, subsequent studies in patients treated with TAM with or without<br />
Table 2<br />
Externally Validated Multigene <strong>Breast</strong> <strong>Cancer</strong> Markers<br />
Number<br />
Population<br />
Commercially<br />
Refs. <strong>of</strong> genes Tissue ER/PR Nodes Adjuvant therapy available<br />
27,28 534 Frozen þ/– þ/– Preoperative chemo Not available<br />
17,18 70 Frozen þ/– þ/– þ/– Chemo; MammaPrint 1<br />
þ/– hormones<br />
21,22 21 Paraffin þ TAM Oncotype DX<br />
embedded<br />
19,20 76 Frozen þ/– No therapy Not available<br />
Abbreviations: ER, estrogen receptor; PR, progesterone receptor; Chemo, chemotherapy; TAM,<br />
tamoxifen.
TAILORx 189<br />
Figure 1<br />
Oncotype DX RS: genes and algorithm. Abbreviation: RS, recurrence score.<br />
Figure 2 Oncotype DX RS predicts distant recurrence as either a categorical variable<br />
(left) or continuous variable (right). Abbreviation: RS, recurrence score.<br />
adjuvant chemotherapy in NSABP trial B20 indicated that only patients with an<br />
elevated RS (RS > 30) derived benefit from adjuvant chemotherapy (25). Other<br />
analyses also indicated that an RS also predicts local recurrence, which may<br />
occur in up to 20% <strong>of</strong> patients with operable breast cancer and which may be<br />
reduced by adjuvant chemotherapy (26). Furthermore, an RS more accurately<br />
predicted recurrence than the Adjuvant! Online model, providing evidence that it<br />
<strong>of</strong>fers information that is complementary to Adjuvant! (12). Finally, although<br />
Oncotoype DX includes a reverse transcriptase polymerase chain reaction<br />
(RT-PCR) assay-based evaluation <strong>of</strong> ER expression, a tumor with high ER<br />
content by RT-PCR may have either a low or a high RS, indicating that it reflects<br />
a measure other than simply ER expression (25).
190 Sparano<br />
Table 3<br />
Scientific Rationale for Selecting Oncotype DX Assay for TAILORx<br />
Rationale<br />
Ref.<br />
Validation study demonstrating that an RS is a prognostic factor for risk <strong>of</strong> distant 21<br />
recurrence in tamoxifen-treated patients, which outperforms clinical criteria in<br />
the multivariate model<br />
External population-based validation study demonstrating an RS predictive <strong>of</strong> 22<br />
breast cancer death<br />
A high RS predictive <strong>of</strong> chemotherapy benefit in tamoxifen-treated patients 25<br />
An RS correlating with risk <strong>of</strong> local recurrence 26<br />
An RS predicting the outcome more accurately than Adjuvant! Online 29<br />
Concordance in the predictive value <strong>of</strong> Oncotype DX compared with other 23<br />
multigene markers<br />
Abbreviation: RS, recurrence score.<br />
There were also several practical reasons for selecting Oncotype DX. First<br />
and foremost is the fact that it involves an RT-PCR-based technique that may be<br />
employed on as little as three 10-mm sections <strong>of</strong> routinely processed and stored<br />
formalin-fixed, paraffin-embedded tissue. In addition, the assay was approved by<br />
Clinical Laboratory Improvement Amendments (CLIA) in the United States and<br />
has received favorable local coverage decisions regarding reimbursement by<br />
Medicare, indicating acceptance <strong>of</strong> the value <strong>of</strong> the assay by third-party payers.<br />
Finally, selecting Oncotype DX for further evaluation by the <strong>Breast</strong> <strong>Cancer</strong><br />
Intergroup represents a logical extension <strong>of</strong> the successful public-private collaboration<br />
between NSABP and Genomic Health.<br />
WHAT TRIAL DESIGN?<br />
In designing a trial integrating a molecular diagnostic test into clinical decision<br />
making, three factors must be considered. First, it is essential to incorporate<br />
features already known about the prognostic and predictive value <strong>of</strong> the test into<br />
the trial design. In this regard, it is clear that patients with a very low RS do very<br />
well with hormonal therapy alone, and patients with a very high RS derive great<br />
benefit from chemotherapy, thereby providing sufficient information to make<br />
definitive treatment recommendations for patients who fall into these groups.<br />
Second, the trial must be designed to address the clinical utility <strong>of</strong> the test in<br />
circumstances where its utility is uncertain or result uninformative. This scenario<br />
applies to patients with a tumor that has a mid-range RS, in which the risk <strong>of</strong><br />
relapse may be sufficiently high to recommend chemotherapy, but in which<br />
chemotherapy may be not be beneficial. Third, it is important to design the trial<br />
in a manner that <strong>of</strong>fers an opportunity to evaluate other clinical cancer tests as<br />
they evolve, obviating the need to repeat a large-scale clinical trial and providing<br />
an opportunity to evaluate the test within a very short time frame.
TAILORx 191<br />
Figure 3<br />
TAILORx schema. Abbreviation: RS, recurrence score.<br />
The factors outlined in the previous paragraph were taken into account<br />
while designing TAILORx. Only patients who meet established clinical criteria<br />
for recommending adjuvant chemotherapy, who are medically suitable candidates<br />
for chemotherapy, and who agree to have their treatment assigned or<br />
randomized on the basis <strong>of</strong> the molecular test are eligible. After informed consent<br />
and “preregistration,” a tumor specimen is forwarded to Genomic Health for the<br />
Oncotype DX assay, with the result reported to the site within 10 to 14 days. After<br />
the Oncotype DX RS is known, patients are then registered and have their treatment<br />
assigned or randomized on the basis <strong>of</strong> the RS (Fig. 3), as described below:<br />
l<br />
l<br />
l<br />
RS is 10 or less: Hormonal therapy alone<br />
RS is 26 or higher: Chemotherapy plus hormonal therapy<br />
RS is 11 to 25: Patients randomized to receive chemotherapy plus hormonal<br />
therapy (the standard treatment arm) versus hormonal therapy alone<br />
(the experimental treatment arm)<br />
Because the study includes only women who meet standard clinical criteria<br />
for adjuvant chemotherapy and are medically suitable candidates for therapy,<br />
chemotherapy plus hormonal therapy is considered the standard treatment arm<br />
for patients with a mid-range RS (i.e., 11–25). Hormonal therapy is considered<br />
the experimental treatment arm for the mid-range group. The primary objective
192 Sparano<br />
<strong>of</strong> the trial is to determine whether hormonal therapy alone is not inferior to<br />
chemotherapy plus hormonal therapy. The trial utilizes a “noninferiority” design<br />
for the mid-range group and is adequately powered to detect a 3% or greater<br />
difference between the two treatment arms.<br />
The chemotherapy regimen and hormonal agents used are prescribed at the<br />
discretion <strong>of</strong> the treating physician but must be consistent with one <strong>of</strong> several<br />
standard options described in the protocol document. Patients may also enroll in<br />
other Intergroup trials as long as the protocol treatment is consistent with the<br />
TAILORx-assigned treatment arm (i.e., hormonal therapy or chemotherapy plus<br />
hormonal therapy). In addition, patients who have already had the Oncotype DX<br />
assay performed are eligible to enroll if the RS is 11 to 25.<br />
At the time <strong>of</strong> registration and treatment assignment, patients are asked to<br />
provide blood samples for banking <strong>of</strong> plasma and peripheral blood mononuclear<br />
cells. Tissue specimens are collected by the Eastern Cooperative Oncology<br />
Group (ECOG) Pathology Coordinating Office, with central testing and confirmation<br />
<strong>of</strong> ER and progesterone receptor (PR) expression, creation <strong>of</strong> tissue<br />
microarrays, and RNA extraction for future studies.<br />
The trial is available through the <strong>Cancer</strong> Trials Support Unit (www.ctsu.org)<br />
and is being coordinated by ECOG. Other participants include NSABP and all<br />
members <strong>of</strong> the North American <strong>Breast</strong> Intergroup, including the Southwest<br />
Oncology Group (SWOG), <strong>Cancer</strong> and Acute Leukemia Group B (CALGB), North<br />
Central <strong>Cancer</strong> Treatment Group (NCCTG), National <strong>Cancer</strong> Institute <strong>of</strong> Canada<br />
(NCIC), and the American College <strong>of</strong> Surgeons Oncology Group (ACOSOG).<br />
RATIONALE FOR THE RS RANGES IN TAILORx<br />
The RS ranges used in TAILORx are different from those originally described<br />
for the low- (30) risk groups as<br />
originally reported for the assay. The range was adjusted in order to minimize<br />
the potential for undertreatment in both the high-risk group (also called “secondary<br />
study group 2”) and the randomized group (also called the “primary study<br />
group”). When the NSABP B20 data were analyzed using the RS ranges used in<br />
TAILORx, the treatment effect <strong>of</strong> chemotherapy was similar to the original<br />
analysis (Table 4). For the analyses described in Table 4, disease-free survival<br />
(DFS) is defined as time to first local, regional, or distant recurrence, second<br />
primary cancer, or death due to any cause, and distant recurrence-free survival<br />
(DRFS) is defined as time to first distant recurrence or death from breast cancer<br />
(contralateral breast cancers, other second primary cancers, and deaths due to<br />
other cancers are censored, and local and regional recurrences are ignored). DFS<br />
has been the primary endpoint evaluated in most breast cancer trials and is the<br />
primary endpoint for TAILORx, whereas DRFS was the primary endpoint in<br />
the NSABP trials evaluating RS. Although a trend favoring the addition <strong>of</strong><br />
chemotherapy becomes evident at an RS <strong>of</strong> approximately 11 when the risk <strong>of</strong><br />
relapse is analyzed in a linear fashion, the 95% confidence intervals completely
TAILORx 193<br />
Table 4 RS and Response to Chemotherapy in B20 Trial (n ¼ 651) by RS<br />
RS<br />
Number <strong>of</strong><br />
patients<br />
TAM 10-yr<br />
DRFS (%)<br />
TAM þ<br />
chemo<br />
10-yr<br />
DRFS (%)<br />
HR (95% CI) for<br />
recurrence by<br />
addition <strong>of</strong><br />
chemo P-value<br />
TAM<br />
10-yr<br />
DFS (%)<br />
TAM þ<br />
chemo<br />
10-yr<br />
DFS (%)<br />
HR (95% CI) for<br />
recurrence by<br />
addition <strong>of</strong> chemo P-value<br />
25 195 (30%) 63 88 0.285 (0.148, 0.551)
194 Sparano<br />
Figure 4 Treatment effect <strong>of</strong> chemotherapy by Oncotype DX RS. Abbreviation: RS,<br />
recurrence score.<br />
overlap in the 11 to 25 RS range (Fig. 4). Finally, a high RS has also been<br />
associated with an elevated risk <strong>of</strong> local relapse, which may also be reduced by<br />
giving adjuvant chemotherapy (26). An RS <strong>of</strong> 11 is associated with a risk <strong>of</strong> both<br />
local and distant relapse <strong>of</strong> about 10%, a threshold that has been typically used<br />
for recommending adjuvant chemotherapy.<br />
CONCLUSION<br />
TAILORx represents a major step forward into the era <strong>of</strong> personalized medicine<br />
for breast cancer. By integrating a molecular diagnostic test into clinical decision<br />
making, patients and clinicians will be able to make more informed decisions<br />
regarding the most appropriate treatment options for a subset <strong>of</strong> patients for<br />
whom the test results in a clear treatment path and refine the utility <strong>of</strong> the assay<br />
for those for whom the result may be uninformative. This trial will also serve as<br />
an important resource for evaluating new molecular signatures and other technologies,<br />
such as proteomics, epigenomics, and pharmacogenomics, as these<br />
technologies evolve.<br />
REFERENCES<br />
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2. Simon R. Diagnostic and prognostic prediction using gene expression pr<strong>of</strong>iles in<br />
high-dimensional microarray data. Br J <strong>Cancer</strong> 2003; 89:1599–1604.
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4. Sparano JA, Fazzari MJ, Childs G. Clinical application <strong>of</strong> molecular pr<strong>of</strong>iling in<br />
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8. Hassett MJ, O’Malley AJ, Pakes JR, et al. Frequency and cost <strong>of</strong> chemotherapyrelated<br />
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11. Fisher B, Jeong JH, Dignam J, et al. Findings from recent National Surgical Adjuvant<br />
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12. Olivotto IA, Bajdik CD, Ravdin PM, et al. Population-based validation <strong>of</strong> the prognostic<br />
model ADJUVANT! for early breast cancer. J Clin Oncol 2005; 23:2716–2725.<br />
13. Whelan T, Levine M, Willan A, et al. Effect <strong>of</strong> a decision aid on knowledge and<br />
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14. Whelan TJ, Loprinzi C. <strong>Ph</strong>ysician/patient decision aids for adjuvant therapy. J Clin<br />
Oncol 2005; 23:1627–1630.<br />
15. Simes RJ, Coates AS. Patient preferences for adjuvant chemotherapy <strong>of</strong> early breast<br />
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16. Berry DA, Cirrincione C, Henderson IC, et al. Estrogen-receptor status and outcomes<br />
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295:1658–1667.<br />
17. van de Vijver MJ, He YD, van ’t Veer LJ, et al. A gene-expression signature as a<br />
predictor <strong>of</strong> survival in breast cancer. N Engl J Med 2002; 347:1999–2009.<br />
18. Buyse M, Loi S, van ’t Veer LJ, et al. Validation and clinical utility <strong>of</strong> a 70-gene<br />
prognostic signature for women with node-negative breast cancer. J Natl <strong>Cancer</strong> Inst<br />
2006; 98:1183–1192.<br />
19. Wang Y, Klijn JG, Zhang Y, et al. Gene-expression pr<strong>of</strong>iles to predict distant<br />
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20. Foekens JA, Atkins D, Zhang Y, et al. Multicenter validation <strong>of</strong> a gene expressionbased<br />
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28. Sorlie T, Tibshirani R, Parker J, et al. Repeated observation <strong>of</strong> breast tumor subtypes<br />
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http://www.oncoconferences.ch/2005/program/overview.htm.
14<br />
Taking Prognostic Signatures to the Clinic<br />
Michail Ignatiadis and Christos Sotiriou<br />
Department <strong>of</strong> Medical Oncology, Translational Research Unit,<br />
Institut Jules Bordet, Universite´ Libre de Bruxelles, Brussels, Belgium<br />
INTRODUCTION<br />
The advent <strong>of</strong> microarray technology and the sequencing <strong>of</strong> the human genome<br />
have enabled scientists to simultaneously interrogate the expression <strong>of</strong> thousands<br />
<strong>of</strong> genes. These gene expression pr<strong>of</strong>iling studies have provided (i) a molecular<br />
classification <strong>of</strong> breast cancer into clinically relevant subtypes, (ii) new tools to<br />
predict disease recurrence and response to different treatments, and (iii) novel<br />
insight into various oncogenic pathways and the process <strong>of</strong> metastatic progression.<br />
<strong>Breast</strong> cancer prognosis has been extensively studied by gene expression<br />
pr<strong>of</strong>iling, resulting in different signatures with little overlap in their constituent<br />
genes. In a large meta-analysis <strong>of</strong> all the above studies, despite the disparity in<br />
their gene lists, molecular subtyping and different prognostic signatures were<br />
shown to carry similar prognostic information. In all these signatures, proliferation<br />
genes were the common driving force with regard to prognostication (1).<br />
Gene expression signatures, despite their current limitations may further<br />
refine breast cancer prognosis and prediction <strong>of</strong> benefit from systemic therapy,<br />
beyond conventional clinicopathological prognostic and predictive factors (2). In<br />
this chapter, we will not refer to gene expression signatures that predict response<br />
to systemic therapy. We will focus only on genomic predictors related to prognosis<br />
and the way these predictors are expected to impact patient management in the<br />
future and accelerate the transition from the “empirical” to “tailored” oncology.<br />
197
198 Ignatiadis and Sotiriou<br />
MOLECULAR CLASSIFICATION OF BREAST CANCER<br />
BY GENE EXPRESSION PROFILING<br />
Clinicians have long recognized that the diagnosis <strong>of</strong> breast cancer includes tumor<br />
types with different natural histories and responses to various treatments. Nevertheless,<br />
traditional histopathological characteristics have been unable to capture the<br />
biological heterogeneity <strong>of</strong> breast tumors. The first studies employing microarray<br />
technology to address this issue were performed by Perou and Sorlie (3–5). They<br />
proposed a molecular classification <strong>of</strong> breast cancer with at least five subgroups on<br />
the basis the expression patterns <strong>of</strong> 500 “intrinsic genes.” Three subgroups were<br />
characterized by low to absent expression <strong>of</strong> the estrogen receptor (ER) and ERrelated<br />
genes, the basal-like, the HER2þ, and the normal breast-like subgroup. The<br />
first subtype was characterized by high expression <strong>of</strong> genes found in myoepithelial<br />
cells in the outer basal layer <strong>of</strong> a normal breast duct, like keratins 5/6 and 17, and<br />
was therefore named basal-like. The HER2þ subgroup was characterized by high<br />
expression <strong>of</strong> several genes <strong>of</strong> the ERBB2 amplicon. The normal breast-like subgroup<br />
showed genes expressed by adipose and nonepithelial tissues and also<br />
revealed a strong expression <strong>of</strong> basal epithelial genes. The other two subtypes<br />
consisted <strong>of</strong> ER-positive tumors and expressed genes <strong>of</strong> the luminal epithelial cells<br />
that are found in the inner layer <strong>of</strong> a normal breast duct, and, as a result, they were<br />
termed luminal. The luminal A subgroup had the highest expression <strong>of</strong> the ERa and<br />
the GATA-binding protein 3 gene, and the second subgroup (luminal B and C)<br />
showed low to moderate expression <strong>of</strong> these genes. To assign each sample to one <strong>of</strong><br />
the five molecular subtypes (basal-like, HER2þ/ER , luminal A, luminal B, normal<br />
breast-like), Hu et al. have created a single sample predictor by calculating the<br />
mean expression pr<strong>of</strong>iles (centroids) <strong>of</strong> a new “intrinsic set <strong>of</strong> 300 genes” for each<br />
subgroup (6). In a multivariate analysis, the basal-like, HER2þ/ER , luminal A,<br />
and luminal B subgroups provided important prognostic information beyond those<br />
provided by traditional predictors like tumor size and grade.<br />
Despite using a different microarray platform, Sotiriou et al. confirmed the<br />
consistency and the prognostic relevance <strong>of</strong> the above molecular classification <strong>of</strong><br />
breast cancer in a different patient population (7). Again, ER status was the most<br />
important discriminator <strong>of</strong> gene expression patterns. Interestingly, although<br />
tumor grade was also strongly reflected in the transcriptome pr<strong>of</strong>ile <strong>of</strong> the primary<br />
tumor, this was not the case for tumor size and axillary nodal invasion.<br />
These results confirmed the important role <strong>of</strong> ER and tumor grade biology in<br />
defining breast cancer molecular subtypes. In conclusion, microarray studies<br />
with different technology platforms and in different patient populations treated<br />
with heterogeneous treatments have been remarkably consistent in reproducing a<br />
similar molecular classification <strong>of</strong> breast cancer.<br />
However, it should be pointed out that the number <strong>of</strong> robust breast cancer<br />
molecular subtypes is not certain, and there is no standardized method for assigning<br />
a molecular subtype to a new case (8). Nevertheless, the breast cancer molecular<br />
classification has begun to alter the way clinical trials are designed. As an example,
Taking Prognostic Signatures to the Clinic 199<br />
new trials have been designed to test the efficacy <strong>of</strong> targeted agents only in the<br />
basal-like breast tumors.<br />
GENE EXPRESSION SIGNATURES AS TOOLS FOR IMPROVING<br />
PROGNOSIS IN BREAST CANCER<br />
Besides the above studies aiming at providing a molecular taxonomy <strong>of</strong> breast<br />
tumors, several independent groups, including our own, have conducted gene<br />
expression pr<strong>of</strong>iling studies with the objective <strong>of</strong> improving upon traditional<br />
prognostic markers used in the clinic for prediction <strong>of</strong> outcome. Two conceptually<br />
different, supervised approaches for prognostic marker discovery on a<br />
genome-wide scale have been applied so far, the “top-down” approach and the<br />
“bottom-up” or “hypothesis-driven” approach. The former derives from a<br />
prognostic model simply by looking for gene expression patterns associated with<br />
the clinical outcome without any a priori biological scenario, whereas the latter<br />
approach first identifies gene expression pr<strong>of</strong>iles linked with a specific biological<br />
phenotype and subsequently correlates these findings to survival.<br />
Using the top-down approach, researchers from the Netherlands <strong>Cancer</strong><br />
Institute (NKI) first identified a 70-gene prognostic signature in a series <strong>of</strong> 78<br />
systemically untreated node-negative breast cancer patients. This signature<br />
(MammaPrint 1 Agendia, Amsterdam, the Netherlands) was then validated on a<br />
larger set <strong>of</strong> 295 young treated and untreated patients, with either node-negative<br />
or -positive breast tumors, from the same institution (9,10). The Rotterdam group<br />
later identified, using a training set <strong>of</strong> 115 breast cancer patients, a 76-gene<br />
signature to predict the development <strong>of</strong> distant metastases in untreated nodenegative<br />
breast cancer patients (11). Interestingly, this group used the ER status<br />
to divide the training set into two subgroups, and the genes selected from each<br />
subgroup (60 and 16 genes for the ER-positive and ER-negative subgroups,<br />
respectively) were combined to form a single signature. The 76-gene signature<br />
(VDX2 array, Veridex LLC, Warren, New Jersey, U.S.) was recently validated in<br />
180 lymph node–negative, untreated patients from multiple institutions (12). The<br />
two groups have used different technology platforms (the NKI group used the<br />
Agilent platform, Santa Clara, California, U.S. and the Rotterdam group used<br />
the Affymetrix platform, Santa Clara, California, U.S.), and although there was<br />
only a three-gene overlap between them, both signatures outperformed the<br />
National Institutes <strong>of</strong> Health (NIH) (13) and St. Gallen criteria (14) in stratifying<br />
patients according to risk <strong>of</strong> relapse. Indeed, while the prognostic signatures<br />
correctly identified those patients who will relapse, they could also identify<br />
many low-risk patients who could be spared from unnecessary chemotherapy.<br />
However, the identification <strong>of</strong> high-risk patients could still be improved, since<br />
half <strong>of</strong> the patients assigned to the poor-signature group, in fact, did not relapse.<br />
A major challenge for gene expression pr<strong>of</strong>iling studies, especially those<br />
with clear clinical implications, is independent validation. Consequently,<br />
TRANSBIG, the translational research network <strong>of</strong> the <strong>Breast</strong> International Group
200 Ignatiadis and Sotiriou<br />
(BIG), conducted an independent validation study <strong>of</strong> these two prognostic signatures<br />
in a series <strong>of</strong> 302 untreated patients from five different centers and across<br />
different statistical facilities (15,16). The two signatures were robust, and their<br />
performance was reproducible and similar in this retrospective series. Interestingly,<br />
both signatures were strong predictors <strong>of</strong> the development <strong>of</strong> metastasis<br />
within five years <strong>of</strong> diagnosis (early metastasis), but their performance decreased<br />
for the detection <strong>of</strong> late metastasis. This could be easily explained, since the<br />
training set in both studies consisted <strong>of</strong> patients who have all relapsed within five<br />
years. However, an intriguing question arises about the potential differences in<br />
the biology and the oncogenic pathways that drive early and late metastasis.<br />
Another group explored the correlation between the expression <strong>of</strong> 250<br />
genes (selected from publicly available microarray data linked with breast cancer<br />
biology) and the clinical outcome in 447 patients from three studies including the<br />
tamoxifen-only group <strong>of</strong> the NASBP trial B-20 to build the Oncotype DX 1<br />
(Genomic Health Inc, Redwood City, California, U.S.) Recurrence Score 1 (RS)<br />
(17). This score (low, intermediate, and high) was based on the expression <strong>of</strong> a<br />
final selected panel <strong>of</strong> 16 cancer-related genes and five reference genes, as<br />
assessed by reverse transcriptase-polymerase chain reaction (RT-PCR) in paraffin-embedded<br />
tumor tissue. Then, the Oncotype DX RS was validated for its<br />
ability to quantify the likelihood <strong>of</strong> distance recurrence in the tamoxifen treated,<br />
ER-positive, node-negative group <strong>of</strong> the NSABP trial B-14 (17). Using the RS,<br />
around 50% <strong>of</strong> the patients in the above study were characterized as low risk<br />
(low RS), with a 6.8% probability <strong>of</strong> distant recurrence at 10 years.<br />
Using the second approach, the hypothesis-driven approach, Chang et al.<br />
tried to identify the molecular similarities between cancer invasion/metastasis<br />
and wound healing (18). They developed a gene signature to characterize the<br />
response <strong>of</strong> fibroblasts to serum. When they applied these genes to primary<br />
gene expression pr<strong>of</strong>iling data from multiple data sets, they observed that the<br />
presence <strong>of</strong> a wound-healing phenotype in the primary tumor was related with<br />
worse clinical outcome in various cancers, including breast cancer. Similar to the<br />
70- and 76-gene signatures, the wound-response signature provided better risk<br />
stratification than the NIH and St. Gallen criteria. Furthermore, it was able to<br />
refine prognosis in the 295 patient data sets <strong>of</strong> van de Vijver et al. (10) beyond<br />
existing clinicopathological risk factors and the 70-gene signature (19). In<br />
conclusion, Chang et al., using a hypothesis-driven approach, apart from identifying<br />
a gene expression prognostic signature, have <strong>of</strong>fered novel insight into<br />
the molecular pathogenesis <strong>of</strong> metastasis by linking this process with wound<br />
healing.<br />
Another example <strong>of</strong> deriving a prognostic gene expression signature using<br />
a hypothesis-driven approach is the study by our group (20) that focused on<br />
histological grade, a well-established pathological parameter rooted in the cell<br />
biology <strong>of</strong> breast cancer. Applying a supervised analysis, we developed a Gene<br />
expression Grade Index (GGI) score based on 97 genes. These genes were<br />
mainly involved in cell cycle regulation and proliferation and were consistently
Taking Prognostic Signatures to the Clinic 201<br />
differentially expressed between low- and high-grade breast carcinomas. Interestingly,<br />
the GGI, which essentially quantifies the degree <strong>of</strong> similarity between<br />
the tumor expression pattern <strong>of</strong> these 97 genes and tumor grade, was able to<br />
reclassify patients with histological grade 2 tumors into two groups with distinct<br />
clinical outcomes similar to those <strong>of</strong> histological grades 1 and 3, respectively<br />
(20). This observation challenges the existence and clinical relevance <strong>of</strong> an<br />
intermediate grade classification. When we explored the implications <strong>of</strong> the joint<br />
distribution <strong>of</strong> ER status and GGI in predicting clinical outcome, we found that<br />
almost all ER-negative tumors with poor clinical outcome were associated with a<br />
high GGI score (high grade), whereas ER-positive tumors were associated with a<br />
heterogeneous mixture <strong>of</strong> gene expression grade values. Recently, our group has<br />
reported that GGI can be used to define two clinically relevant molecular subtypes<br />
in ER-positive breast cancer (21). These two ER-positive molecular subgroups<br />
(high and low genomic grade) were highly comparable to the luminal A<br />
and B classifications and were associated with distinct clinical outcome in<br />
systemically untreated or tamoxifen-only-treated ER-positive breast cancer<br />
populations involving more than 650 patients (21). GGI appeared to be the<br />
strongest predictor <strong>of</strong> clinical outcome, highlighting the prognostic importance<br />
<strong>of</strong> proliferation genes in ER-positive subgroups, as have been already reported<br />
(17). Indeed, proliferation was one <strong>of</strong> the five components associated with the<br />
highest weight in the Oncotype DX RS.<br />
Four additional studies have used the hypothesis-driven approach to build<br />
gene predictors <strong>of</strong> clinical outcome. Oh et al. used estrogen-induced genes,<br />
identified by treating the MCF-7 cell line with 17b-estradiol to build a predictor<br />
<strong>of</strong> clinical outcome on a training set <strong>of</strong> 65 patients with ER-positive breast<br />
tumors (22). A total <strong>of</strong> 822 genes, mainly proliferation and antiapoptosis genes<br />
could separate these tumors into two subgroups with different clinical outcomes.<br />
This gene list was then mapped in three published datasets (4,5,19,23) and an<br />
average expression index or “centroid” was created that could classify the ERpositive<br />
tumors into two clinically relevant subgroups. In another study, Miller et<br />
al. generated a 32-gene expression signature that distinguishes p53 mutant and<br />
wild type tumors. The aim <strong>of</strong> this research was to provide a more accurate<br />
assessment <strong>of</strong> the functional status <strong>of</strong> the p53 pathway than that provided by<br />
mutation analysis using sequencing (24). It should be noted that none <strong>of</strong> the<br />
signature genes were known transcriptional targets <strong>of</strong> p53, nor have they been<br />
related to the p53 pathway. Instead, they were mainly proliferation-related genes,<br />
various transcription factors, and ion transport genes. Once more, the proliferation<br />
genes were a prominent part <strong>of</strong> this signature. The signature outperformed<br />
p53 mutation status, as identified by sequencing in prognosis and prediction <strong>of</strong><br />
response to hormonal therapy and chemotherapy. By combining the hypothesisdriven<br />
approach with comparative cross-species functional genomics, Glinsky<br />
et al. identified an 11-gene “death-from-cancer” signature (25). This signature<br />
can identify a lethal subset <strong>of</strong> human cancers <strong>of</strong> various origins (including breast<br />
cancer) with a high propensity to metastasize. These tumors display the
202 Ignatiadis and Sotiriou<br />
“phenotype” <strong>of</strong> an activated BMI-1 oncogenic pathway that is essential for the<br />
self-renewal <strong>of</strong> normal stem cells. Again, proliferation- and cell cycle–regulating<br />
genes comprise the most essential part <strong>of</strong> this signature. Along the same lines,<br />
Liu et al. isolated and compared at the transcriptional-level tumorigenic CD44þ/<br />
CD24 /low breast cancer cells derived from a small set <strong>of</strong> six breast cancer<br />
patients with normal breast epithilium (26). They developed an “invasiveness”<br />
gene signature (IGS) that was associated with the risk <strong>of</strong> death and metastasis not<br />
only in breast cancer but also in other tumor types. Again, there was a small gene<br />
overlap between IGS and other prognostic signatures, and, not surprisingly, its<br />
prognostic power was significant only in ER-positive and intermediate-grade<br />
tumors, a feature that had already been observed for other prognostic pr<strong>of</strong>iles.<br />
CAN WE INTEGRATE THE DIFFERENT GENE EXPRESSION SIGNATURES?<br />
The above gene expression signatures developed to improve breast cancer<br />
prognostication have little overlap in their constituent genes. A representative<br />
list <strong>of</strong> these signatures is depicted in Table 1. The inevitable question is whether<br />
the “intrinsic subtypes” developed by unsupervised cluster analysis and the<br />
other prognostic signatures have also little overlap in the prognostic information<br />
they convey. Fan et al. recently compared the prognostic ability <strong>of</strong> five gene<br />
expression–based models—intrinsic subtypes (6), 70-gene pr<strong>of</strong>ile (9), wound<br />
response (19), RS (17), and two-gene ratio (23)—in a single dataset <strong>of</strong><br />
295 patients and found that four <strong>of</strong> five predictors had significant agreement in<br />
outcome predictions for individual patients (27). However, this study was limited<br />
to only one dataset <strong>of</strong> 295 patients, examined only five gene expression signatures,<br />
and did not investigate the biology that may drive prognosis among the five<br />
signatures. To address this issue, a large meta-analysis <strong>of</strong> publicly available gene<br />
expression and clinical data from 2833 patients (4–7,9,12,15,20,23,24,28–39) with<br />
breast cancer was performed by the Swiss Institute <strong>of</strong> Bioinformatics in collaboration<br />
with our group (Table 1). In this meta-analysis, the concept <strong>of</strong> “coexpression<br />
modules” (comprehensive list <strong>of</strong> genes with highly correlated<br />
expression) was used to describe important biological processes in breast cancer,<br />
like proliferation, ER and HER2 signaling, tumor invasion, and immune response.<br />
In this study, we sought to depict the connection between these modules, the<br />
previously reported molecular classification, several gene prognostic classifiers,<br />
and the most established clinicopathological variables. A number <strong>of</strong> interesting<br />
conclusions were drawn from this collaborative effort. First, the disparity <strong>of</strong> gene<br />
lists <strong>of</strong> the various gene signatures may be attributed to (i) heterogeneity in patient<br />
populations studied, (ii) the use <strong>of</strong> different microarray platforms with different<br />
probe sets and different methods for data normalization, and (iii) sampling variation<br />
due to small sample size relative to the number <strong>of</strong> genes examined. Second,<br />
using these co-expression modules, instead <strong>of</strong> the five molecular portraits reported<br />
with the intrinsic genes, breast tumors were now grouped into three main subgroups<br />
namely ER /HER , HER2þ, and ERþ/HER2 . Third, the ER /HER ,
Taking Prognostic Signatures to the Clinic 203<br />
Table 1 Gene Expression Signatures Related to <strong>Breast</strong> <strong>Cancer</strong> Prognosis<br />
Gene expression<br />
signatures Biological scenario Microarray platform<br />
Number <strong>of</strong><br />
genes in the<br />
signature<br />
Independent<br />
validation<br />
Prospective<br />
clinical validation Ref.<br />
70-gene signature Clinical outcome Agilent (oligonucleotides) 70 yes Yes (MINDACT<br />
Trial)<br />
76-gene signature Clinical outcome Affymetrix<br />
(oligonucleotides)<br />
(9,10,15)<br />
76 yes No (11,12,16)<br />
Recurrence Score Clinical outcome RT-PCR 21 yes Yes (TAILORX<br />
trial)<br />
Wound-response<br />
signature<br />
Wound healing and tumor<br />
progression<br />
Genomic grade Histological grade and tumor<br />
progression<br />
(17,37)<br />
CDNA (custom made) 512 yes No (18,19)<br />
Affymetrix<br />
(oligonucleotides)<br />
P-53 signature Functional status <strong>of</strong> p-53 Affymetrix<br />
(oligonucleotides)<br />
Death-from-cancer<br />
signature<br />
Invasiveness gene<br />
signature<br />
Bmi-1 oncogenic pathway<br />
self-renewal<br />
Tumorigenic cancer cells<br />
CD44þ/CD24 , low<br />
Affymetrix<br />
(oligonucleotides)<br />
Affymetrix<br />
(oligonucleotides)<br />
97 yes No (20)<br />
32 yes No (24)<br />
11 Yes No (25)<br />
186 Yes No (26)
204 Ignatiadis and Sotiriou<br />
Figure 1 Despite the disparity in their gene lists, different prognostic signatures showed<br />
similar prognostic performance. Proliferation is the common driving force responsible for<br />
their prognostic performance.<br />
HER2þ tumors were characterized by high proliferation, whereas the<br />
ERþ/HER2 was divided in two subgroups, the ERþ/low proliferation and the<br />
ERþ/high proliferation tumors resembling to luminal A and B subtypes,<br />
respectively. Fourth, 10 prognostic signatures [RS (17), 70-gene signature (9),<br />
76-gene signature (11), wound response (18), p53 signature (24), GGI (20),<br />
NCH70 (33), CON52 (38), CCYC (39), ZCOX], despite the disparity in their<br />
gene lists, showed similar prognostic performance (Fig. 1). It is interesting to<br />
note that they all identified as “low risk” the ERþ subtype associated with low<br />
proliferation. Fifth, proliferation was the common driving force responsible for<br />
the performance <strong>of</strong> the various prognostic signatures (Fig. 1). Indeed, to further<br />
investigate the role <strong>of</strong> proliferation genes in relation to the different prognostic<br />
signatures, we divided each signature into two “partial signatures”—one with only<br />
proliferation genes and the other with the complementary nonproliferation genes.<br />
Interestingly, when only proliferation genes were used, the total performance was<br />
not degraded. In contrast, the nonproliferation partial signatures typically show<br />
degraded performance. Sixth, combining the signatures did not improve the performance<br />
as expected from the high concordance in their classifications. Finally,<br />
in the multivariate analysis, including the genomic predictor derived from coexpression<br />
modules, the standard clinicopathological variables, namely tumor size<br />
and nodal status, retained their prognostic value.<br />
PROSPECTIVE VALIDATION OF TWO GENOMIC PREDICTORS<br />
The initial promising results that breast cancer prognostication can be improved<br />
using gene expression signatures need to be validated by well-designed prospective<br />
clinical trials. Two gene expression predictors, the 70-gene expression
Taking Prognostic Signatures to the Clinic 205<br />
signature, (MammaPrint) discussed earlier and the Oncotype DX RS have<br />
reached the final step <strong>of</strong> prospective clinical testing. The BIG and the EORTC<br />
have jointly launched the MINDACT (Microarray for Node-Negative Disease<br />
may Avoid Chemotherapy) trial that directly compares two approaches for<br />
deciding whether to <strong>of</strong>fer adjuvant chemotherapy in node-negative breast cancer<br />
patients, i.e., either using the prognostic information provided by standard<br />
clinicopathological criteria (control arm) or by the 70-gene expression signature<br />
(MammaPrint) (experimental arm). Six thousand women will participate, and<br />
the primary endpoint is to prove that five-year disease-free survival in the<br />
experimental arm will not be inferior to that <strong>of</strong> the control arm; at the same time,<br />
it is estimated that 10% to 15% fewer women will be treated with chemotherapy<br />
in the experimental arm.<br />
A similarly ambitious initiative is taking place in the United States. The<br />
Oncotype DX RS is being evaluated in the TAILORx [Trial Assigning<br />
IndividuaLized Options for Treatment (Rx)] trial, which is part <strong>of</strong> the<br />
National <strong>Cancer</strong> Institute (NCI) Program for the Assessment <strong>of</strong> Clinical<br />
<strong>Cancer</strong> Tests (PACCT). This trial randomizes patients with intermediate RS<br />
to receive either hormonal therapy alone or hormonal therapy plus chemotherapy.<br />
In it, patients with low RS will be treated with hormonal therapy<br />
alone and those with high RS will receive chemotherapy plus hormonal<br />
therapy.<br />
These two trials will provide level I evidence that these gene expression<br />
predictors provide prognostic information beyond standard clinicopathological<br />
factors and are ready for use in clinical practice. Both trials will provide an<br />
excellent opportunity to address issues related to tissue handling and shipping,<br />
reproducibility, quality controls, and standardization <strong>of</strong> the microarray experiments.<br />
Furthermore, they will provide a valuable database for future translational<br />
research.<br />
FUTURE DIRECTION–CONCLUSIONS<br />
The real challenge, once the above trials prospectively validate the two genomic<br />
predictors, will be to integrate these predictors into a comprehensive clinical<br />
decision-making algorithm that will lead toward personalized medicine.<br />
Parameters that need to be included in this algorithm include the following:<br />
Traditional Clinicopathological Parameters<br />
These include patient comorbidities, age, and primary tumor characteristics like<br />
tumor size, tumor grade, nodal status, ER status, HER2/neu status, and lymphovascular<br />
invasion. Many <strong>of</strong> these parameters are incorporated in various<br />
prognostic tools like the Nottingham Prognostic Index (40), Adjuvant! Online<br />
(41), and the NIH (13) and St. Gallen (42) Guidelines.
206 Ignatiadis and Sotiriou<br />
Primary Tumor Gene Expression Signatures<br />
From the above discussion, it is apparent that two genomic predictors are in the<br />
late phase <strong>of</strong> clinical validation. However, apart from the question <strong>of</strong> who can<br />
be spared unnecessary adjuvant chemotherapy (prognosis), a more important<br />
question is which therapy works better in an individual patient (prediction).<br />
Genomic predictors <strong>of</strong> response to multidrug regimens and newer targeted agents<br />
have also been reported (43–49), but their description is beyond the scope <strong>of</strong> this<br />
chapter. Other important technology platforms are being developed to analyze<br />
epigenetic changes in DNA (50), the role <strong>of</strong> microRNAs (51) that will provide us<br />
with a more comprehensive picture <strong>of</strong> the biology <strong>of</strong> the tumor and further refine<br />
prognosis and prediction.<br />
Micrometastatic Cells<br />
The presence <strong>of</strong> cytokeratin positive tumor cells in the bone marrow <strong>of</strong> early breast<br />
cancer patients has been correlated with worse outcome in a meta-analysis<br />
involving more than 4500 patients (52). We have reported that the detection with a<br />
real-time RT-PCR assay <strong>of</strong> Cytokeratin-19 (CK19) mRNA-positive circulating<br />
tumor cells (CTCs) in patients with early breast cancer before (53,54) and after<br />
(55,56) the initiation <strong>of</strong> adjuvant systemic treatment is associated with poor outcome.<br />
However, the additional prognostic information <strong>of</strong> micrometastatic cells to<br />
primary tumor gene expression pr<strong>of</strong>iling data has not yet been examined. Moreover,<br />
there are pilot studies where HER2-positive CTCs and/or disseminated tumor cells<br />
(DTCs) were successfully eliminated with trastuzumab (57), suggesting that CTCs<br />
and/or DTCs may be tested also as tools to predict response to a particular drug.<br />
Molecular Imaging<br />
PET (positron emission tomography) scan using novel PET tracers that target<br />
critical cellular processes like angiogenesis may provide important staging and<br />
functional information and is under investigation in breast cancer (58).<br />
<strong><strong>Ph</strong>armacogenetics</strong> (Host)<br />
The importance <strong>of</strong> pharmacogenetics in predicting clinical outcome in breast<br />
cancer has recently been illustrated in studies focused on the polymorphism <strong>of</strong><br />
enzymes that metabolize tamoxifen (59,60). The study <strong>of</strong> the host is an important<br />
piece <strong>of</strong> the clinical decision-making algorithm.<br />
Proteomics<br />
Proteomics provides the hope <strong>of</strong> discovering novel biological markers that can<br />
be used for early detection, disease diagnosis, prognostication, and prediction <strong>of</strong><br />
response to therapy in early breast cancer (61).
Taking Prognostic Signatures to the Clinic 207<br />
Adjuvant Treatment<br />
It is well documented that adjuvant treatment affects clinical outcome <strong>of</strong> breast<br />
cancer patients (62). From that perspective, the positive adjuvant trials with the<br />
aromatase inhibitors (63) and trastuzumab (64,65), the concept <strong>of</strong> dose-dense<br />
adjuvant chemotherapy (66), and the emerging data about the use <strong>of</strong> taxanes<br />
(67,68) and anthracyclines (69) in the adjuvant setting are rendering both the<br />
treatment decision-making process and prediction <strong>of</strong> outcome increasingly more<br />
complex.<br />
Several efforts have been made to combine at least some <strong>of</strong> the above<br />
parameters to predict the clinical outcome <strong>of</strong> breast cancer patients. For instance,<br />
Pittman et al. have used a modeling approach based on classification trees to<br />
combine clinical and genomic data (70). They nicely showed that by combining<br />
multiple metagenes (defined as summary measures <strong>of</strong> the expression <strong>of</strong> subsets<br />
<strong>of</strong> potentially related genes) with nodal status, they could create integrative<br />
clinicogenomic models. These models provided improved prediction accuracy<br />
compared with either clinical or genomic data alone. Another example <strong>of</strong> such an<br />
effort is the Adjuvant! Online for breast cancer, Genomic Version 7.0, which<br />
combines traditional clinical data, the RS, and information about adjuvant<br />
treatment to provide an estimate <strong>of</strong> clinical outcome in patients with early breast<br />
cancer (71).<br />
However, the accumulation <strong>of</strong> highly complex and multilevel information<br />
will ultimately impose specific data integration requirements. So far these different<br />
types <strong>of</strong> data are not only maintained in a variety <strong>of</strong> different data sources<br />
but are also managed by different complex data management and analytical<br />
systems. Additionally, they involve many institutions and numerous complex<br />
workflows. To address all these critical issues, two initiatives have been<br />
launched that intend to integrate all this information into one comprehensive<br />
biomedical platform—the cancer Biomedical Informatics Grid, “caBIG” (https://<br />
cabig.nci.nih.gov/) and the Advancing Clinico Genomic Trials, “ACGT” (http://<br />
www.eu-acgt.org/) funded by the NCI and European Union, respectively.<br />
In the future, medical oncologists should use a clinically validated treatment<br />
decision-making algorithm that will take into account all this highly<br />
complex and rapidly evolving, multilevel information. In this way, we will move<br />
from the “empirical” to the new exciting era <strong>of</strong> “individual-tailored” oncology.<br />
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signature predicting therapy failure in patients with multiple types <strong>of</strong><br />
cancer. J Clin Invest 2005; 115(6):1503–1521.<br />
26. Liu R, Wang X, Chen GY, et al. The prognostic role <strong>of</strong> a gene signature from<br />
tumorigenic breast-cancer cells. N Engl J Med 2007; 356(3):217–226.<br />
27. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors<br />
for breast cancer. N Engl J Med 2006; 355(6):560–569.<br />
28. Calza S, Hall P, Auer G, et al. Intrinsic molecular signature <strong>of</strong> breast cancer in a<br />
population-based cohort <strong>of</strong> 412 patients. <strong>Breast</strong> <strong>Cancer</strong> Res 2006; 8(4):R34.<br />
29. Pawitan Y, Bjohle J, Amler L, et al. Gene expression pr<strong>of</strong>iling spares early breast<br />
cancer patients from adjuvant therapy: derived and validated in two population-based<br />
cohorts. <strong>Breast</strong> <strong>Cancer</strong> Res 2005; 7(6):R953–R964.<br />
30. Huang E, Cheng SH, Dressman H, et al. Gene expression predictors <strong>of</strong> breast cancer<br />
outcomes. Lancet 2003; 361(9369):1590–1596.<br />
31. Huang E, Ishida S, Pittman J, et al. Gene expression phenotypic models that predict<br />
the activity <strong>of</strong> oncogenic pathways. Nat Genet 2003; 34(2):226–230.<br />
32. Korkola JE, DeVries S, Fridlyand J, et al. Differentiation <strong>of</strong> lobular versus ductal breast<br />
carcinomas by expression microarray analysis. <strong>Cancer</strong> Res 2003; 63(21): 7167–7175.<br />
33. Naderi A, Teschendorff AE, Barbosa-Morais NL, et al. A gene-expression signature<br />
to predict survival in breast cancer across independent data sets. Oncogene 2007;<br />
26(10):1507–1516.<br />
34. Farmer P, Bonnefoi H, Becette V, et al. Identification <strong>of</strong> molecular apocrine breast<br />
tumours by microarray analysis. Oncogene 2005; 24(29):4660–4671.<br />
35. Richardson AL, Wang ZC, De NA, et al. X chromosomal abnormalities in basal-like<br />
human breast cancer. <strong>Cancer</strong> Cell 2006; 9(2):121–132.<br />
36. Espinosa E, Vara JA, Redondo A, et al. <strong>Breast</strong> cancer prognosis determined by gene<br />
expression pr<strong>of</strong>iling: a quantitative reverse transcriptase polymerase chain reaction<br />
study. J Clin Oncol 2005; 23(29):7278–7285.<br />
37. Paik S, Tang G, Shak S, et al. Gene expression and benefit <strong>of</strong> chemotherapy in<br />
women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol<br />
2006; 24(23):3726–3734.<br />
38. Teschendorff AE, Naderi A, Barbosa-Morais NL, et al. A consensus prognostic gene<br />
expression classifier for ER positive breast cancer. Genome Biol 2006; 7(10):R101.<br />
39. Whitfield ML, George LK, Grant GD, et al. Common markers <strong>of</strong> proliferation. Nat<br />
Rev <strong>Cancer</strong> 2006; 6(2):99–106.
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40. Galea MH, Blamey RW, Elston CE, et al. The Nottingham Prognostic Index in<br />
primary breast cancer. <strong>Breast</strong> <strong>Cancer</strong> Res Treat 1992; 22(3):207–219.<br />
41. Olivotto IA, Bajdik CD, Ravdin PM, et al. Population-based validation <strong>of</strong> the<br />
prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 2005; 23(12):<br />
2716–2725.<br />
42. Goldhirsch A, Glick JH, Gelber RD, et al. Meeting highlights: international expert<br />
consensus on the primary therapy <strong>of</strong> early breast cancer 2005. Ann Oncol 2005;<br />
16(10):1569–1583.<br />
43. Gianni L, Zambetti M, Clark K, et al. Gene expression pr<strong>of</strong>iles in paraffin-embedded<br />
core biopsy tissue predict response to chemotherapy in women with locally advanced<br />
breast cancer. J Clin Oncol 2005; 23(29):7265–7277.<br />
44. Chang JC, Wooten EC, Tsimelzon A, et al. Patterns <strong>of</strong> resistance and incomplete<br />
response to docetaxel by gene expression pr<strong>of</strong>iling in breast cancer patients. J Clin<br />
Oncol 2005; 23(6):1169–1177.<br />
45. Jansen MP, Foekens JA, van Staveren IL, et al. Molecular classification <strong>of</strong> tamoxifen-resistant<br />
breast carcinomas by gene expression pr<strong>of</strong>iling. J Clin Oncol 2005;<br />
23(4):732–740.<br />
46. Hess KR, Anderson K, Symmans WF, et al. <strong>Ph</strong>armacogenomic predictor <strong>of</strong> sensitivity<br />
to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin,<br />
and cyclophosphamide in breast cancer. J Clin Oncol 2006; 24(26):4236–4244.<br />
47. Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in human cancers as<br />
a guide to targeted therapies. Nature 2006; 439(7074):353–357.<br />
48. Potti A, Dressman HK, Bild A, et al. Genomic signatures to guide the use <strong>of</strong> chemotherapeutics.<br />
Nat Med 2006; 12(11):1294–1300.<br />
49. Solit DB, Garraway LA, Pratilas CA, et al. BRAF mutation predicts sensitivity to<br />
MEK inhibition. Nature 2006; 439(7074):358–362.<br />
50. Herman JG, Baylin SB. Gene silencing in cancer in association with promoter<br />
hypermethylation. N Engl J Med 2003; 349(21):2042–2054.<br />
51. Iorio MV, Ferracin M, Liu CG, et al. MicroRNA gene expression deregulation in<br />
human breast cancer. <strong>Cancer</strong> Res 2005; 65(16):7065–7070.<br />
52. Braun S, Vogl FD, Naume B, et al. A pooled analysis <strong>of</strong> bone marrow micrometastasis<br />
in breast cancer. N Engl J Med 2005; 353(8):793–802.<br />
53. Xenidis N, Perraki M, Kafousi M, et al. Predictive and prognostic value <strong>of</strong> peripheral<br />
blood cytokeratin-19 mRNA-positive cells detected by real-time polymerase chain<br />
reaction in node-negative breast cancer patients. J Clin Oncol 2006; 24(23):3756–3762.<br />
54. Ignatiadis M, Xenidis N, Perraki M, et al. Different prognostic value <strong>of</strong> Cytokeratin-<br />
19 mRNA-positive Circulating Tumor Cells according to estrogen receptor status in<br />
early breast cancer. J Clin Oncol 2007; 25(33):5194–5202.<br />
55. Xenidis N, Vlachonikolis I, Mavroudis D, et al. Peripheral blood circulating cytokeratin-19<br />
mRNA-positive cells after the completion <strong>of</strong> adjuvant chemotherapy in<br />
patients with operable breast cancer. Ann Oncol 2003; 14(6):849–855.<br />
56. Xenidis N, Mavroudis D, Apostolaki S, et al. Effect <strong>of</strong> adjuvant treatment on the<br />
circulating CK19mRNA positive tumor cells in patients with early stage breast<br />
cancer. Proc Am Soc Clin Oncol 2006; 24(No18S):20012 (abstr).<br />
57. Bozionellou V, Mavroudis D, Perraki M, et al. Trastuzumab administration can<br />
effectively target chemotherapy-resistant cytokeratin-19 messenger RNA-positive<br />
tumor cells in the peripheral blood and bone marrow <strong>of</strong> patients with breast cancer.<br />
Clin <strong>Cancer</strong> Res 2004; 10(24):8185–8194.
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58. Quon A, Gambhir SS. FDG-PET and beyond: molecular breast cancer imaging.<br />
J Clin Oncol 2005; 23(8):1664–1673.<br />
59. Goetz MP, Rae JM, Suman VJ, et al. <strong><strong>Ph</strong>armacogenetics</strong> <strong>of</strong> tamoxifen biotransformation<br />
is associated with clinical outcomes <strong>of</strong> efficacy and hot flashes. J Clin<br />
Oncol 2005; 23(36):9312–9318.<br />
60. Nowell S, Sweeney C, Winters M, et al. Association between sulfotransferase 1A1<br />
genotype and survival <strong>of</strong> breast cancer patients receiving tamoxifen therapy. J Natl<br />
<strong>Cancer</strong> Inst 2002; 94(21):1635–1640.<br />
61. Somiari RI, Somiari S, Russell S, Shriver CD. Proteomics <strong>of</strong> breast carcinoma.<br />
J Chromatogr B Analyt Technol Biomed Life Sci 2005; 815(1–2):215–225.<br />
62. Early <strong>Breast</strong> <strong>Cancer</strong> Trialists’ Collaborative Group (EBCTCG). Effects <strong>of</strong> chemotherapy<br />
and hormonal therapy for early breast cancer on recurrence and 15-year<br />
survival: an overview <strong>of</strong> the randomised trials. Lancet 2005; 365(9472):1687–1717.<br />
63. Winer EP, Hudis C, Burstein HJ, et al. American Society <strong>of</strong> Clinical Oncology<br />
technology assessment on the use <strong>of</strong> aromatase inhibitors as adjuvant therapy for<br />
postmenopausal women with hormone receptor-positive breast cancer: status report<br />
2004. J Clin Oncol 2005; 23(3):619–629.<br />
64. Piccart-Gebhart MJ, Procter M, Leyland-Jones B, et al. Trastuzumab after adjuvant<br />
chemotherapy in HER2-positive breast cancer. N Engl J Med 2005; 353(16):1659–1672.<br />
65. Romond EH, Perez EA, Bryant J, et al. Trastuzumab plus adjuvant chemotherapy for<br />
operable HER2-positive breast cancer. N Engl J Med 2005; 353(16):1673–1684.<br />
66. Citron ML, Berry DA, Cirrincione C, et al. Randomized trial <strong>of</strong> dose-dense versus<br />
conventionally scheduled and sequential versus concurrent combination chemotherapy<br />
as postoperative adjuvant treatment <strong>of</strong> node-positive primary breast cancer:<br />
first report <strong>of</strong> Intergroup Trial C9741/<strong>Cancer</strong> and Leukemia Group B Trial 9741.<br />
J Clin Oncol 2003; 21(8):1431–1439.<br />
67. Mamounas EP, Bryant J, Lembersky B, et al. Paclitaxel after doxorubicin plus<br />
cyclophosphamide as adjuvant chemotherapy for node-positive breast cancer: results<br />
from NSABP B-28. J Clin Oncol 2005; 23(16):3686–3696.<br />
68. Martin M, Pienkowski T, Mackey J, et al. Adjuvant docetaxel for node-positive<br />
breast cancer. N Engl J Med 2005; 352(22):2302–2313.<br />
69. Piccart-Gebhart MJ. Anthracyclines and the tailoring <strong>of</strong> treatment for early breast<br />
cancer. N Engl J Med 2006; 354(20):2177–2179.<br />
70. Pittman J, Huang E, Dressman H, et al. Integrated modeling <strong>of</strong> clinical and gene<br />
expression information for personalized prediction <strong>of</strong> disease outcomes. Proc Natl<br />
Acad Sci U S A 2004; 101(22):8431–8436.<br />
71. Adjuvant! For <strong>Breast</strong> <strong>Cancer</strong>, Genomic Version 7.0. http://www.adjuvantonline.com/<br />
breastgenomic.jsp 2006.
15<br />
Tissue is the Issue<br />
Adekunle Raji<br />
Eastern Cooperative Oncology Group Pathology Coordinating Office,<br />
Northwestern University, Evanston, Illinois, U.S.A.<br />
INTRODUCTION<br />
All eyes are on tissue. The biggest challenge facing biomedical discoveries today<br />
is the issue with tissue. The era <strong>of</strong> personalized, individually tailored medicine has<br />
begun; therefore, cells, tissue and its intra- and interenvironment, are the focus <strong>of</strong><br />
pharmacogenetics. The intracellular kinetic and intercellular dynamics are key<br />
factors in the understanding <strong>of</strong> cell behavior before, during, and after therapy.<br />
Undoubtedly the solid type <strong>of</strong> tissue carries the bulk <strong>of</strong> these issues. Other<br />
fluid forms <strong>of</strong> tissue, however, do have their own share <strong>of</strong> these issues, <strong>of</strong> course.<br />
Tissue will be defined here as biological material comprising a network <strong>of</strong><br />
characteristically diverse cells. In essence, blood, urine, surgical resections,<br />
biopsies, marrow, lavage, hair, nail, and other specimen taken from a person will<br />
be considered as tissue. Turn to any biomedical community member and most, if<br />
not all, will have some sort <strong>of</strong> opinion about tissue. Between the ultimate giver<br />
(patients) and ultimate user (laboratorians) lie the challenges. To the patient, care<br />
team, and several entities, the tissue is the issue, and to the laboratories, technologists,<br />
and the interpreters <strong>of</strong> results, the issue is really the tissue.<br />
In the past two years, several working groups were established not only to<br />
understand current issues as well as future anticipated issues but also to address<br />
them. These working groups are usually comprise various clinical and financial<br />
institutions and experts from multiple disciplines, such as medical scientists,<br />
basic scientists, surgeons, laboratories, pathologists, funding agencies, agent<br />
213
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manufacturers, patient advocates, statisticians, bioinformatics, research associates,<br />
research administrators, medical staff, and clinical administrators. These<br />
groups guide the interdisciplinary patient care team by addressing the procurement,<br />
collection, preservation, acquisition, processing, transportation, and<br />
storage <strong>of</strong> biological materials <strong>of</strong> high quality and usability that are adequate<br />
for analysis and interpretation. Good news is that specimens with clinical data<br />
are archived as far back as 40 years ago, the bad news is that they were<br />
collected non-uniformaly; today’s good news is that the collection processing<br />
andassociateddataareundergoingharmonization (uniformity). Contingency<br />
plans are also being developed for future anticipated issues. The key areas <strong>of</strong><br />
the tissue issue are obviously procurement, collection, acquisition, processing,<br />
preservation, storage, distribution, and bioinformatics that are successfully<br />
conducted within stringent policies, procedures, rules, and regulations.<br />
PROCUREMENT<br />
A strategy for collecting any biological specimens from patients is needed to<br />
ensure that the behavior <strong>of</strong> cells or tissue after it leaves the body <strong>of</strong> the patient is<br />
consistent with what it was while in the patient’s body. Regardless <strong>of</strong> the<br />
strategy, clear and simple instructions should be written and made available to<br />
the procurement team comprising a preoperation nurse, phlebotomist, surgery<br />
assistant, physician/surgeon, physician assistant, pathology assistant, research<br />
coordinator, patient care nurse, internal specimen transporters, and medical/<br />
research technologist. These instructions are typically included in the local<br />
manual <strong>of</strong> operational procedures and/or clinical protocols and should provide<br />
details <strong>of</strong> the type <strong>of</strong> collection devices such as vacutainers, syringes, punch size,<br />
preservatives, buffers, reagents and the containers needed. The manual must also<br />
include procedures for preparing reagents or buffers such as formalin, RNAlater,<br />
and the optimal cutting temperature (OCT) compound. It must contain information<br />
about the precollection requirements with regard to time, temperature,<br />
and pressure for certain types <strong>of</strong> specimens (Table 1)<br />
COLLECTION<br />
There are several choices <strong>of</strong> collection devices, to the extent that they have<br />
created a great deal <strong>of</strong> confusion in the medical field. It is not surprising to see<br />
several new clinical trial protocols now restricting collection to the use <strong>of</strong><br />
preassembled collection devices, with instructions to assist the procurement<br />
plan and ensure uniform collection, particularly in a multi-institutional study.<br />
While it is possible to obtain serum from redtop, royal blue top, serum separator<br />
top, and plain syringe, it is true that tissue can be fixed in paraformaldhyde,<br />
formalin, acid alcohol, zinc formalin, and many other reagents. Each <strong>of</strong> these<br />
fixatives presents its own advantages and disadvantages toward downstream<br />
applications such as immunohistochemistry (IHC), in situ hybridization (ISH),
Tissue is the Issue 215<br />
Table 1<br />
Roles<br />
Roles and Responsibility<br />
Responsibility<br />
<strong>Ph</strong>ysician/surgeon including<br />
but not limited to pathologist<br />
and intervention radiologist<br />
Nurse<br />
<strong>Ph</strong>lebotomist, nurse, medical<br />
technologist<br />
<strong>Ph</strong>ysician/surgery assistant<br />
Transporter<br />
Research coordinators, project<br />
manager, protocol specialist,<br />
data manager, nurse<br />
coordinator, clinical<br />
research associates<br />
Medical/research technologist<br />
Inform patient, order test, perform surgery/biopsy/<br />
imaging/morphology/assay procedures.<br />
Inform patient, determine timing <strong>of</strong> procedure/draw.<br />
Draw blood via port.<br />
Draw blood into devices. Perform aspiration.<br />
Guide physician and expedite preservation and delivery.<br />
Ensure timely delivery from point <strong>of</strong> collection to next<br />
critical step/location while preserving samples.<br />
Ensure all team members are familiar with their roles<br />
and have adequate information to assist in<br />
performing task. Liaison among team members as<br />
well as facilitate collaboration.<br />
Ensure availability <strong>of</strong> appropriate supplies for<br />
collection and preservation. Effectively and<br />
efficiently process specimens and analyze samples.<br />
comparative genomic hybridization (CGH), polymerase chain reaction (PCR),<br />
blotting (Western, Northern, Southern), enzyme-linked immunosorbent assay<br />
(ELISA), radio immunoassay (RIA), enzyme immunoassay (EIA), and chemiluminescence<br />
assay (CLA). It is also true that plasma can be obtained from all<br />
anticoagulated vacutainers as well as heparin-coated syringes. Protein and<br />
nucleic acids can be obtained from biological samples regardless <strong>of</strong> collection<br />
or preservation buffer. However, experience has shown a great deal <strong>of</strong> variation<br />
in the yield and quality <strong>of</strong> sample products, as well as the analytical results<br />
obtained from the samples. It is therefore important to select one device and one<br />
alternate backup for the collection, preservation, and fixation that will provide<br />
the best representation <strong>of</strong> the cellular content and its content behavior as it was<br />
just before leaving the patient’s body. It is important to select a device and<br />
preservative that are best for yield and quality, as well as most appropriate for<br />
the next step in processing or analysis. Table 2 lists the collection devices,<br />
preservatives, and fixatives that have been selected by large research groups<br />
because <strong>of</strong> their overall performance success.<br />
Fresh tissue. Nonfrozen and nonfixed tissue should be collected directly<br />
into a labeled sterile screw-cap plastic container, with one <strong>of</strong> the<br />
following buffers:<br />
1. <strong>Ph</strong>osphate buffered saline (PBS) for tissue culture and cell viability<br />
studies.<br />
2. Neutral buffered formalin (10%) for gradual fixation.
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Table 2 Common Blood Collection Tubes<br />
Closure/stopper color Anticoagulant or additive<br />
Number <strong>of</strong><br />
inversions to mix Examples for use<br />
Red/red in glass tube None None Serum for chemistry, proteomics, immunology, and<br />
banking<br />
Red/red in plastic tube Clot activator 5–10 Serum for chemistry, proteomics, immunology, and<br />
banking<br />
Gold/red marbled SST Clot activator and separation gel 5–10 Serum for chemistry, proteomics, immunology, and<br />
banking. Remote collection.<br />
Lavender/lavender<br />
(purple)<br />
Sodium EDTA (dried), or<br />
potassium EDTA (liquid)<br />
Light blue/light blue Liquid 3.2% sodium citrate, a or<br />
3.8% sodium citrate<br />
Cell preparation<br />
tube CPT<br />
Liquid 3.2% sodium citrate, a or<br />
3.8% sodium citrate. Ficol and<br />
gel separator<br />
Paxgene RNA/DNA Preanalytic/Qiagen nucleic acid<br />
stabilizer<br />
5–10 Whole blood for hematology, certain whole blood<br />
chemistries, e.g., hemoglobin A1c or blood typing.<br />
Proteomics, floating DNA, genomic DNA, cells.<br />
5–10 Plasma for coagulation, Platelet poor plasma. Growth<br />
factors, fragile proteins, viable cells.<br />
5–10 Plasma for coagulation, Platelet poor plasma. Growth<br />
5–10 Genomic expressions.<br />
factors, fragile proteins, viable cells. Remote<br />
collection <strong>of</strong> viable mononuclear cells.<br />
P100 Protein stabilizer 5–10 Small size protein measurement. Proteomic studies.<br />
Tan Sodium heparin (glass), or 5–10 Whole blood for lead, heavy metals, toxicology studies.<br />
potassium EDTA (plastic)
Tissue is the Issue 217<br />
Table 2 Common Blood Collection Tubes (Continued )<br />
Closure/stopper color Anticoagulant or additive<br />
Number <strong>of</strong><br />
inversions to mix Examples for use<br />
Green/green Lithium heparin, or 5–10 Plasma or whole blood for chemistry, PK, and PD.<br />
Sodium heparin 5–10 Plasma for chemistry<br />
Light green/green<br />
marbled<br />
Lithium heparin and separation gel 5–10 Plasma for chemistry<br />
Gray/gray Potassium oxalate/sodium fluoride,<br />
or sodium fluoride, or lithium<br />
iodoacetate, or lithium<br />
iodoacetate/lithium heparin<br />
5–10 Serum or plasma for glucose or alcohol requiring<br />
inhibition <strong>of</strong> glycolysis, C-peptides.<br />
Dark royal blue Sodium heparin, EDTA, or plain 5–10 Plasma or serum for trace elements, toxicology, and<br />
nutrients.<br />
Pale yellow/bright yellow Sodium polyanetholesulfonate 5–10 Blood cultures, microbiology, and infectious diseases<br />
markers.<br />
Yellow Acid citrate dextrose (ACD) 5–10 Blood bank, viable cells alternate to CPT and CellSave<br />
Orange/yellow and<br />
marbled<br />
Clot activator (thrombin) 5–10 Serum for chemistry, ELISA<br />
Pink/pink Potassium EDTA 5–10 Molecular/viral load testing, blood bank, hematology.<br />
a www.bd.com<br />
Abbreviations: SST, serum separator top; PK, pharmacokinetic; PD, pharmacodynamic.
218 Raji<br />
3. RPMI (Roswell Park Memorial Institute) media for tissue culture, cell<br />
viability studies, protein extraction, and multiphoton studies.<br />
4. RNAlater for RNA extraction.<br />
5. OCT bottom and top prior to freezing.<br />
PROCESSING<br />
Generally, processing methods that will ensure optimal quality and yield should be<br />
selected for the processing <strong>of</strong> specimens and samples into next product aliquot.<br />
Blood. It is important to perform all centrifugation in refrigerated and<br />
temperature-controlled chambers. This is to avoid unnecessary heat<br />
incubation <strong>of</strong> samples while spinning. In some instances, it will be<br />
necessarytospinthesampleataslowspeedfollowedbyhighspeedto<br />
eliminate platelets. Selection <strong>of</strong> complex processes to obtain delicate<br />
and fragile by-products is important. Aliquoting into the smallest<br />
useable quantity to avoid freeze-thaw-freeze cycle is also necessary.<br />
For example, serum and plasma can be apportioned into 200 mL, DNA<br />
andRNAcanbeapportionedinto75mg and1mg, respectively.<br />
Fresh frozen tissue. OCT-protected frozen tissue should be cryosectioned<br />
for hematoxylin and eosin staining to assess tumor area and percentage<br />
as part <strong>of</strong> the annotation. The information will guide case selection as<br />
well as steps in macro and micro dissection. Integrity <strong>of</strong> the sample must<br />
be maintained through sterile processes to avoid degradation and/or<br />
contamination. The tissue must be kept frozen until submersed into<br />
extraction buffer. There are many choices <strong>of</strong> methods for the extraction<br />
<strong>of</strong> cellular components such as RNA, DNA, and proteins.<br />
Fixed tissue. Tissue biopsies and/or resection must undergo complete and<br />
adequate fixation in a buffered environment. This is important for the<br />
morphologic review as well as the evaluation <strong>of</strong> reference and novel<br />
markers. Neutral buffered formalin has been widely used and is most<br />
appropriate for current and future translational processes. As previously<br />
mentioned, there are many choices <strong>of</strong> methods for the extraction and<br />
isolation <strong>of</strong> DNA; however, there are only few methods for the<br />
extraction <strong>of</strong> RNA and protein from fixed tissue. Care must be taken<br />
in making selections to avoid loss <strong>of</strong> valuable tissue resource.<br />
Sectioning should be performed with new blades and clean water.<br />
Nonprecipitating formalin should be avoided.<br />
Xylene rather than its substitutes should be used.<br />
Manual rather than automated processes should be used for small,<br />
starting samples.<br />
Washes between steps in any and all processes must be complete to<br />
avoid reagent carry over.
Tissue is the Issue 219<br />
CURRENT NOVEL PROCESSING OF BLOOD AND FIXED TISSUE<br />
Isolation <strong>of</strong> Mononuclear Cells<br />
Monocytes, a type <strong>of</strong> white blood cells, are found both in bone marrow and blood.<br />
The following procedure describes the isolation <strong>of</strong> monocytes in peripheral blood<br />
(peripheral blood mononuclear cells, PBMC).<br />
Steps<br />
1. Centrifuge CPT tubes containing whole blood for 15 minutes at 258C at<br />
2700 rpm.<br />
2. Pipet out the top layer (plasma) and transfer into two labeled tubes and<br />
freeze.<br />
3. Pipet out the lymphocyte layer (the cloudy layer) with a transfer pipette,<br />
placing the layer into a fresh 15-mL tube. Fill tube with room-temperature<br />
PBS.<br />
4. Centrifuge for 10 minutes at 1800 rpm.<br />
5. Discard supernatant and resuspend pellet in 1 mL PBS.<br />
6. Fill tube with PBS, inverting eight times to mix and wash.<br />
7. Centrifuge for 15 minutes at 2000 rpm.<br />
8. Discard supernatant.<br />
9. Proceed to cell sorting or resuspend and freeze pellet in 1 mL <strong>of</strong> RPMI or<br />
DMSO (dimethyl sulfoxide) for later sort.<br />
Note:<br />
For blood collected in any other anticoagulated tube, dilute 1:1 with<br />
RPMI, layer on Histopaque 1077, then proceed with step 1.<br />
Specialized Sorting <strong>of</strong> Circulating Tumor/Endothelial Cells<br />
1. Magnetic labeling (*45 min)<br />
a. Centrifuge sample for 10 minutes at 300g at room temperature.<br />
b. Carefully remove the supernatant completely.<br />
c. Resuspend cell pellet in appropriate amount <strong>of</strong> buffer as specified by<br />
the microbeads package insert. For fewer cells, use the same volume.<br />
d. Add microbeads per package insert.<br />
e. Incubate for 15 minutes at 68Cto128C. For less than 5 10 6 total cells,<br />
use the same volume.<br />
f. Wash cells by adding 10 to 20 times the labeling volume <strong>of</strong> buffer and<br />
centrifuge at 300g for 10 minutes.<br />
g. Remove supernatant completely (save the supernatant in appropriately<br />
labeled 1 mL cryovial) and resuspend cell pellet in appropriate amount<br />
<strong>of</strong> buffer (1 mL <strong>of</strong> buffer/10 8 total cells).<br />
h. Proceed to magnetic separation.
220 Raji<br />
2. Magnetic separation (*60 min)<br />
a. Turn on the manual and/or automated cell sorter.<br />
b. When the machine has finished initializing, perform a column exchange<br />
to remove blank columns and insert magnetic columns. (To save<br />
time, try to perform these functions while the sample and beads<br />
are incubating.)<br />
c. Wipe down probes with alcohol to disinfect.<br />
d. Place the sample in the uptake position, and place empty 15-mL conical<br />
tubes in the rack under the Neg1, Pos1, and Pos2 probes.<br />
e. Choose “Separation” on the main menu.<br />
f. Use the down arrows to select “possel_s” and press “start.”<br />
g. For both the positive and negative cells, separate elution container<br />
must be used (*15 min).<br />
h. Prepare and/or submit samples for next step in processing (DNA extraction,<br />
thin prep slides, etc.).<br />
Extraction <strong>of</strong> RNA from Peripheral Blood in PAXgene<br />
1. Centrifuge the PAXgene Blood RNA Tube for 10 minutes at 3000g to<br />
5000g using a swing-out rotor.<br />
2. Remove the supernatant by decanting or pipetting. Add 4 mL RNase-free<br />
water to the pellet, and close the tube using a fresh secondary Hemogard<br />
closure.<br />
3. Vortex until the pellet is visibly dissolved, and centrifuge for 10 minutes at<br />
3000g to 5000g using a swing-out rotor. Remove and discard the entire<br />
supernatant.<br />
4. Add 350 mL Buffer BR1, and vortex until the pellet is visibly dissolved.<br />
5. Pipet the sample into a 1.5-mL microcentrifuge tube. Add 300 mL Buffer<br />
BR2 and 40 mL proteinase K. Mix by vortexing for 5 seconds, and incubate<br />
for 10 minutes at 558C using a shaker/incubator at 400 to 1400 rpm.<br />
After incubation, set the temperature <strong>of</strong> the shaker/incubator to 658C (for<br />
step 20).<br />
6. Pipet the lysate directly into a PAXgene Shredder spin column (lilac)<br />
placed in a 2-mL processing tube, and centrifuge for 3 minutes at maximum<br />
speed (but not to exceed 20,000g).<br />
7. Carefully transfer the entire supernatant <strong>of</strong> the flow-through fraction to a<br />
fresh 1.5-mL microcentrifuge tube without disturbing the pellet in the<br />
processing tube.<br />
8. Add 350 mL ethanol (96–100%, purity grade p.a.). Mix by vortexing, and<br />
centrifuge briefly (1–2 seconds at 500–1000g) to remove drops from the<br />
inside <strong>of</strong> the tube lid.<br />
9. Pipet 700 mL sample into the PAXgene RNA spin column (red) placed in a<br />
2-mL processing tube and centrifuge for 1 minute at 8000 to 20,000g.
Tissue is the Issue 221<br />
Place the spin column in a new 2-mL processing tube, and discard the old<br />
processing tube containing flow-through.<br />
10. Pipet the remaining sample into the PAXgene RNA spin column, and<br />
centrifuge for 1 minute at 8000 to 20,000g. Place the spin column in a<br />
new 2-mL processing tube, and discard the old processing tube containing<br />
flow-through.<br />
11. Pipet 350 mL Buffer BR3 into the PAXgene RNA spin column. Centrifuge<br />
for 1 minute at 8000 to 20,000g. Place the spin column in a new 2-mL<br />
processing tube and discard the old processing tube containing flow-through.<br />
12. Add 10 mL DNase I stock solution to 70 mL Buffer RDD in a 1.5-mL<br />
microcentrifuge tube. Mix by gently “flicking” the tube and centrifuge<br />
briefly to collect residual liquid from the sides <strong>of</strong> the tube.<br />
13. Pipet the DNase I incubation mix (80 mL) directly onto the PAXgene RNA<br />
spin column membrane, and place on the bench top (20–308C.) for<br />
15 minutes.<br />
14. Pipet 350 mL Buffer BR3 into the PAXgene RNA spin column, and<br />
centrifuge for 1 minute at 8000g to 20,000g. Place the spin column in a<br />
new 2-mL processing tube and discard the old processing tube containing<br />
flow-through.<br />
15. Pipet 500 mL Buffer BR4 to the PAXgene RNA spin column, and<br />
centrifuge for 1 minute at 8000g to 20,000g. Place the spin column in a<br />
new 2-mL processing tube, and discard the old processing tube containing<br />
flow-through.<br />
16. Add another 500 mL Buffer BR4 to the PAXgene RNA spin column.<br />
Centrifuge for 3 minutes at 8000g to 20,000g.<br />
17. Discard the tube containing the flow-through, and place the PAXgene<br />
RNA spin column in a new 2-mL processing tube. Centrifuge for 1 minute<br />
at 8000g to 20,000g.<br />
18. Discard the tube containing the flow-through. Place the PAXgene RNA<br />
spin column in a 1.5-mL microcentrifuge tube, and pipet 20 mL Buffer<br />
RNAse-free water directly onto the PAXgene RNA spin column membrane.<br />
Centrifuge for 1 minute at 8000g to 20,000g to elute the RNA.<br />
19. Repeat the elution step by adding the eluted RNA to the same PAXgene<br />
RNA spin column membrane. Centrifuge for 1 minute at 8000g to 20,000g<br />
to elute the RNA.<br />
20. Incubate the eluate for 5 minutes at 658C. in the shaker/incubator without<br />
shaking. After incubation, chill immediately on ice.<br />
21. If the RNA samples will not be used immediately, store at 208Cor 708C.<br />
Laser Capture Microdissection<br />
Laser capture microdissection (LCM) allows for the microscopic removal <strong>of</strong><br />
specific cells from paraffin or frozen tissue sections mounted on glass slides.
222 Raji<br />
This procedure describes the isolation <strong>of</strong> a pure population <strong>of</strong> cells followed by<br />
its nucleic acid or protein extraction.<br />
1.1. Prepare paraffin section on glass slide.<br />
a. Collect 5 mm paraffin section onto uncoated glass slide, using microtome<br />
and water bath.<br />
b. Allow to air-dry overnight at room temperature or at 428C for up to<br />
8 hours.<br />
c. Deparaffinize and lightly stain slide(s) by immersing in the following:<br />
i. Xylene 2: 5 minutes each<br />
ii. 100% ethanol: 1 minute<br />
iii. 95% ethanol: 1 minute<br />
iv. 70% ethanol: 1 minute<br />
v. Distilled water: 1 minute<br />
vi. Harris hematoxylin: 1 dip (Fisher’s Protocol #245–678)<br />
vii. Tap water: 2 minutes<br />
viii. Distilled water: 1 minute<br />
ix. 95% ethanol: 1 minute<br />
x. Eosin Y: 1 dip (Fisher’s Protocol #314–631)<br />
xi. 95% ethanol: 1 minute<br />
xii. Allow to air-dry<br />
or<br />
1.2. Prepare frozen section on glass slide.<br />
a. Collect 8 mm frozen section on an uncoated glass slide, using cryostat.<br />
b. Store frozen until ready to postfix.<br />
c. Postfix and lightly stain.<br />
d. Remove a maximum <strong>of</strong> four slides from cryostat or freezer, place on<br />
clean paper towel, i.e., KimWipe, and allow to thaw for approximately<br />
30 seconds.<br />
e. Place the slides in 75% ethanol for 30 seconds.<br />
f. Transfer the slides to distilled water for 30 seconds.<br />
g. Place slides on KimWipe, apply 100 mL <strong>of</strong> histogene staining<br />
solution, and stain for approximately 20 seconds.<br />
h. Place the slides in distilled water for 30 seconds.<br />
i. Place the slides for 30 seconds in each <strong>of</strong> the following ethanols in<br />
increasing concentration: 75%, then 95%, and then 100%.<br />
j. Transfer the slides to xylene for 5 minutes.<br />
k. Place the slides on a KimWipe to dry in a ventilation hood for 5 minutes.<br />
2. Dissect area <strong>of</strong> tissue from slide (to be performed same day as steps 1.1<br />
or 1.2).<br />
a. Place slide on Pixcell II LCM [Arcturus (now part <strong>of</strong> Molecular<br />
Devices), Mountain View, California, U.S.] and locate area <strong>of</strong><br />
interest using 4 or 10 objective.
Tissue is the Issue 223<br />
b. Load CapSure HS caps.<br />
c. Turn on laser key, push laser enable button, and locate laser spot on<br />
screen.<br />
d. Focus laser beam to bright, well-defined spot.<br />
e. Test laser, observing melted plastic ring(s).<br />
See following table for power and duration.<br />
Laser spot size (mm) Power (mW) Duration<br />
CapSure HS Small (7.5) 65–75 650–750 ms<br />
Medium (15) 35–45 2.5–3.0 ms<br />
Large (30) 45–55 6.0–7.0 ms<br />
f. View captured cells on cap.<br />
g. Move cap to capping station.<br />
h. Transfer to ExtractSure device from CapSure HS cap.<br />
3. Extract DNA from dissected tissue.<br />
a. Pipet 155 mL <strong>of</strong> reconstitution buffer into one vial <strong>of</strong> proteinase K.<br />
Gently vortex the tube to mix the reagents and immediately place<br />
the tube on ice. Completely dissolve reagent, excessive mixing can<br />
denature proteinase K.<br />
b. Assemble the ExtractSure Sample Extraction Device onto the CapSure<br />
HS LCM Cap.<br />
c. Using clean forceps, remove the cap from the cap insertion tool and<br />
place into the alignment tray (sample facing up).<br />
d. Make sure that the cap snaps securely and lies flat on the bottom <strong>of</strong> the<br />
opening <strong>of</strong> the alignment tray.<br />
e. Position the ExtractSure Device over the cap using clean forceps. The<br />
fill-port should be facing up.<br />
f. Using forceps, push the ExtractSure Device down onto the cap until it<br />
snaps securely into place. Use forceps to ensure that the cap is firmly<br />
attached to the ExtractSure Device.<br />
g. Pipet 10 mL <strong>of</strong> proteinase K extraction solution into the microchamber<br />
formed by the assembled ExtractSure Device and CapSure HS LCM<br />
Cap.<br />
h. Using gloved hands, place a 0.5-mL thin-walled reaction tube over the<br />
ExtractSure device.<br />
i. Cover the assembly in the alignment tray with the incubation block and<br />
incubate it overnight at 658C.<br />
j. After incubation, remove the assembled CapSure HS LCM Cap and<br />
ExtractSure Device from the incubator, place the assembly into a<br />
microcentrifuge and centrifuge for 1 minute at 4000g.
224 Raji<br />
k. Separate the CapSure HS LCM Cap and ExtractSure Device. Close the<br />
microcentrifuge tube containing the extract and heat to 958C for<br />
10 minutes in a heating block to inactivate the proteinase K. Cool<br />
the sample to room temperature.<br />
Isolation <strong>of</strong> DNA, RNA, Protein Using TRIZOL Method<br />
Isolation <strong>of</strong> RNA by TRI Reagent<br />
Homogenization Homogenize fresh tissue samples (that have been stored in<br />
RNAlater) in TRI Reagent (1 mL/50–100 mg tissue) using a glass-Teflon or<br />
Polytron homogenizer. Sample volume should not exceed 10% <strong>of</strong> the volume <strong>of</strong><br />
TRI Reagent used for homogenization.<br />
<strong>Ph</strong>ase separation<br />
1. Store the homogenate for 5 minutes at room temperature to permit the<br />
complete dissociation <strong>of</strong> nucleoprotein complexes.<br />
2. Add 0.2 mL chlor<strong>of</strong>orm to the homogenate, cover the samples tightly, and<br />
shake vigorously for 15 seconds.<br />
3. Store the resulting mixture at room temperature for 2 to 15 minutes and<br />
centrifuge at 12,000g for 15 minutes at 48C.<br />
4. Following centrifugation, the mixture separates into a lower red phenolchlor<strong>of</strong>orm<br />
phase, interphase, and the colorless upper aqueous phase. RNA<br />
remains exclusively in the aqueous phase, whereas DNA and proteins are<br />
in the interphase and organic phase. The volume <strong>of</strong> the aqueous phase is<br />
about 60% <strong>of</strong> the volume <strong>of</strong> TRI Reagent used for homogenization.<br />
RNA precipitation<br />
1. Transfer the aqueous phase to a fresh tube.<br />
2. Precipitate RNA from the aqueous phase by mixing with 0.5 mL <strong>of</strong><br />
isopropanol.<br />
3. Store samples at room temperature for 5 to 10 minutes and centrifuge at<br />
12,000g for eight minutes at 48C to258C. RNA precipitate (<strong>of</strong>ten invisible<br />
before centrifugation) forms a gel-like or white pellet on the side and<br />
bottom <strong>of</strong> the tube.<br />
RNA wash<br />
1. Remove the supernatant and wash the RNA pellet (by vortexing) with 75%<br />
ethanol (1 mL).<br />
2. Centrifuge at 7500g for 5 minutes at 48C to258C.
Tissue is the Issue 225<br />
RNA solubilization<br />
1. Remove the ethanol wash and briefly air-dry the RNA pellet for 3 to 5 minutes.<br />
2. Dissolve RNA water by passing the solution a few times through a pipette<br />
tip and incubating for 10 to 15 minutes at 55 to 608C.<br />
Isolation <strong>of</strong> DNA by TRI Reagent<br />
The DNA is isolated from the interphase and phenol phase separated from the<br />
initial homogenate as described in the RNA isolation protocol.<br />
DNA precipitation<br />
1. Remove the remaining aqueous phase overlying the interphase. Precipitate<br />
DNA from the interphase and organic phase with ethanol.<br />
2. Add 0.3 mL <strong>of</strong> 100% ethanol and mix samples by inversion.<br />
3. Store the samples at room temperature for 2 to 3 minutes and sediment<br />
DNA by centrifugation at 2000g for 5 minutes at 48C to258C.<br />
DNA wash<br />
1. Wash the DNA pellet twice in a solution containing 0.1 M trisodium citrate<br />
in 10% ethanol (1 mL).<br />
2. Store the DNA pellet in the washing solution for 30 minutes at room temperature<br />
with periodic mixing and centrifuge at 2000g for 5 minutes at 48Cto258C.<br />
3. Suspend the DNA pellet in 75% ethanol (1.5–2 mL <strong>of</strong> 75% ethanol). Store<br />
for 10 to 20 minutes at room temperature with periodic mixing and centrifuge<br />
at 2000g for 5 minutes at 48C to258C. This ethanol wash removes<br />
pinkish color from the DNA pellet.<br />
DNA solubilization<br />
1. Remove the ethanol wash and briefly air-dry the DNA pellet by keeping<br />
tubes open for 3 to 5 minutes at room temperature.<br />
2. Dissolve the DNA pellet in 8 mM NaOH by slowly passing through a<br />
pipette. Add (0.3 mL) <strong>of</strong> 8 mM NaOH.<br />
3. Centrifuge at 12,000g for 10 minutes and transfer the resulting supernatant<br />
containing DNA to new tubes.<br />
Isolation <strong>of</strong> Proteins by TRI Reagent<br />
Protein precipitation<br />
1. Aliquot a portion <strong>of</strong> the phenol-ethanol supernatant (0.2–0.5 mL, 1 volume)<br />
into a micr<strong>of</strong>uge tube.
226 Raji<br />
2. Precipitate proteins by adding 3 volumes <strong>of</strong> acetone.<br />
3. Mix by inversion for 10 to 15 seconds to obtain a homogeneous solution.<br />
4. Store samples for 10 minutes at room temperature and sediment the protein<br />
precipitate at 12,000g for 10 minutes at 48C.<br />
Protein wash<br />
1. Decant the phenol-ethanol supernatant and disperse the protein pellet in<br />
0.5 mL <strong>of</strong> 0.3 M guanidine hydrochloride in 95% ethanol þ 2.5 %<br />
glycerol.<br />
2. Disperse the pellet using a pipette tip. After dispersing the pellet, add<br />
another 0.5 mL aliquot <strong>of</strong> the guanidine hydrochloride/ethanol/glycerol<br />
wash solution to the sample and store for 10 minutes at RT.<br />
3. Sediment the protein at 8000g for 5 minutes.<br />
4. Decant the wash solution and perform two more washes in 1 mL each <strong>of</strong><br />
the guanidine/ethanol/glycerol wash solution. Disperse the pellet by vortexing<br />
after each wash to efficiently remove residual phenol.<br />
5. Wash in 1 mL <strong>of</strong> ethanol containing 2.5 % glycerol.<br />
6. Sediment the protein at 8000g for 5 minutes.<br />
7. Decant the alcohol, invert the tube, and dry the pellet for 7 to 10 minutes at<br />
room temperature.<br />
Protein solubilization<br />
1. After briefly air-drying the protein pellet, add 1% SDS (0.2 mL) to the<br />
protein pellet.<br />
2. Gently disperse and solubilize the pellet for 15 to 20 minutes by flicking<br />
the tube or pipetting as required.<br />
Storage <strong>of</strong> RNA, DNA, and/or Protein<br />
Store the solubilized proteins and DNA at 208C or colder, store RNA at 708C<br />
or colder.<br />
Tissue Microarray Creation<br />
This procedure describes the use <strong>of</strong> the MTA-1 (Manual Tissue Arrayer) by<br />
Beecher Instruments, Inc. (Sun Prairie, Wisconsin, U.S.) to create tissue microarray<br />
(TMA) blocks. TMA is a technology which facilitates research by permitting<br />
the collection <strong>of</strong> biological specimens representing a great many individuals<br />
without requiring a great deal <strong>of</strong> storage space. TMA eases comparison <strong>of</strong> the<br />
histological characteristics <strong>of</strong> different patients’ pathologies by placing many<br />
different tissue samples together on the same microscope slide for observation.
Tissue is the Issue 227<br />
Also, TMA helps prevent the unnecessary exhaustion <strong>of</strong> irreplaceable pathological<br />
material, since it permits the same diagnostic and research objectives to be achieved<br />
with far less tissue than was required for such objectives before TMA<br />
existed.<br />
A core <strong>of</strong> paraffin is removed from a “recipient” paraffin block (one<br />
embedded without tissue) and the remaining empty space is filled with a core <strong>of</strong><br />
paraffin-embedded tissue from a “donor” block. This process can be repeated up<br />
to 230 times for one TMA block, resulting in a block containing mapped pieces<br />
from many different blocks, and when sectioned, produces a slide containing<br />
mapped pieces from many different blocks. A corresponding map records each<br />
core’s location in the grid and the information connected with it (patient ID<br />
number, study arm, etc.). The array sections can be used for all histological<br />
staining, including hematoxylin & eosin (H&E), IHC, and ISH.<br />
1. Prepare the donor blocks and corresponding H&E slides.<br />
a. Re-embed tissue, as needed, for more uniform wax consistency and<br />
optimal tissue orientation.<br />
b. Donor tissue should be at least 1.0 mm thick for best results.<br />
c. Prepare a fresh H&E slide from each re-embedded potential donor block.<br />
2. Pathologist characterizes each H&E slide for appropriateness <strong>of</strong> diagnosis<br />
and percentage <strong>of</strong> tumor.<br />
a. Examine each H&E slide microscopically and indicate, with marker,<br />
the area <strong>of</strong> interest (AOI). This area corresponds to the area on the<br />
block from which the core(s) will be taken.<br />
3. Prepare the recipient (TMA) block.<br />
a. Label a blank cassette, using the automated cassette labeler.<br />
b. Determine the size <strong>of</strong> the TMA block and use:<br />
M—the medium mold (30 mm 23 mm) or<br />
L—the large mold (35 mm 23 mm)<br />
c. Make a paraffin block, containing no tissue, using the labeled cassette.<br />
d. Assure that the paraffin block surface is flat and parallel to the<br />
underside <strong>of</strong> plastic cassette and that it contains no air bubbles.<br />
4. Design the array.<br />
a. Determine the diameter <strong>of</strong> the cores to be removed from the donor<br />
blocks. Available core sizes include 0.6, 1.0, 1.5, and 2.0 mm. Core<br />
size is <strong>of</strong>ten indicated by protocol, i.e., breast cancer studies require<br />
the smallest core size—0.6 mm—to conserve tissue while removing<br />
three cores from each donor block during the routine production<br />
<strong>of</strong> three TMA blocks from each group <strong>of</strong> subjects.<br />
b. Allow for enough space (3.0 mm) around the edge <strong>of</strong> the block to avoid<br />
cracking paraffin.
228 Raji<br />
c. Determine the number <strong>of</strong> columns and number <strong>of</strong> rows to use by noting<br />
the following table, which describes the maximum number <strong>of</strong> cores per<br />
TMA block, with respect to core size and mold size.<br />
Maximum Number <strong>of</strong> Cores per TMA Block<br />
Core size (mm)<br />
Spacing between<br />
cores (mm)<br />
Mold size<br />
Maximum no.<br />
<strong>of</strong> cores/spaces<br />
0.6 a yellow 1.5 LM 20 12 ¼ 240<br />
1.0 a green 2.0 LM 15 9 ¼ 135<br />
1.5 a black 2.5 LM 12 7 ¼ 84<br />
2.0 a white 3.0 LM 10 6 ¼ 60<br />
a Punches are color coded.<br />
5. Create a map, identifying each core’s position on the grid its donor block.<br />
a. Enter information into Excel Program (TMA Mapping Tool, Attachment<br />
1) located on Server/Pathcore/TMA. TMA may be mapped using<br />
alternate methods.<br />
b. Include cores from tissue and cell line control blocks, in duplicate.<br />
i. Tissue blocks, i.e., for breast protocols:<br />
Fibroadenoma<br />
Normal endometrium<br />
Normal salivary gland<br />
Normal testis<br />
ii.<br />
Cell line blocks, i.e., for breast protocols:<br />
MCF7<br />
SKBR3<br />
T47D<br />
c. Each TMA block will be asymmetric to assure map orientation, i.e.:<br />
2nd column blank,<br />
4th row blank, and<br />
bottom right space blank<br />
6. Construct the TMA.<br />
a. Install the pair <strong>of</strong> tissue punches, for desired core size, in MTA-1. Install<br />
the slightly larger <strong>of</strong> the pair (identified in blue) on the right (for the<br />
donor block). Install the slightly smaller <strong>of</strong> the pair (identified in red) on<br />
the left for the (for the recipient or TMA block).<br />
b. Insert the recipient (TMA) paraffin block into the MTA-1 block holder.<br />
Secure in place by tightening two screws in block holder with MTA-1<br />
allen wrench.
Tissue is the Issue 229<br />
c. Remove the first core from TMA block.<br />
i. Assure that recipient punch is swung into place.<br />
ii. Align punch over site <strong>of</strong> first core, usually top left corner, by<br />
turning micrometers.<br />
iii. Zero micrometers by pressing the X and Y “ZERO/ABS” buttons.<br />
iv. Gently lower punch into TMA block, using care not to punch too<br />
deeply. Maximum punch depth can be adjusted with large vertical<br />
screw on the left.<br />
v. Raise punch.<br />
vi. Remove paraffin core from punch, using punch’s stylet.<br />
d. Remove core from first donor block.<br />
i. Place the donor block bridge over the TMA block holder.<br />
ii. Swing donor punch into place.<br />
iii. Manually align donor block so that AOI is directly under the<br />
donor punch.<br />
iv. Lower and raise punch.<br />
v. Remove core <strong>of</strong> tissue from punch using punch stylet.<br />
e. Place donor core into TMA block.<br />
i. Remove donor block bridge from over the TMA block holder,<br />
noting location <strong>of</strong> delicate loose core <strong>of</strong> tissue.<br />
ii. Using forceps, align donor core over hole created for it in the<br />
TMA block.<br />
iii. Lower donor core into hole and assure flat level block surface by<br />
pressing evenly with clean glass slide.<br />
f. Adjust the micrometer(s) to move the precision guide to the next X/Y<br />
position. Follow the Spacing Between Cores (mm) Column in the<br />
maximum number <strong>of</strong> cores per TMA block table above to determine<br />
the appropriate increments.<br />
g. Repeat this cycle (steps 6c–6f) to construct the whole array.<br />
h. To punch holes on right half <strong>of</strong> TMA block, rotate it 1808 and replace<br />
in recipient block holder, because the x axis micrometer should not be<br />
extended beyond 9 mm.<br />
i. Smooth and level surface <strong>of</strong> TMA block.<br />
i. Remove the TMA block from the recipient block holder.<br />
ii. Place the TMA block in oven at 378C, maximum temperature, for<br />
30 minutes.<br />
iii. Gently press a glass slide on the top <strong>of</strong> the TMA block, applying<br />
even pressure to push all tissue cores on the block to same level.
230 Raji<br />
7. Section TMA block.<br />
i. Cut a section from the TMA block and stain the slide with H&E for<br />
QA/QC review by the pathologist.<br />
ii. Store TMA block and save TMA map for any future sectioning requests.<br />
Label side <strong>of</strong> block<br />
2nd column left blank.<br />
4th row left blank<br />
Bottom right corner left blank
16<br />
Acquisition and Preservation <strong>of</strong> Tissue<br />
for Microarray Analysis<br />
Brian R. Untch and John A. Olson<br />
Institute for Genome Sciences and Policy and the Department <strong>of</strong> Surgery,<br />
Duke University Medical Center, Durham, North Carolina, U.S.A.<br />
INTRODUCTION<br />
Increasingly, collection <strong>of</strong> normal and neoplastic human tissue is being incorporated<br />
in the design <strong>of</strong> clinical trials and in routine clinical settings under tissue<br />
banking programs. The goal behind these activities is either to facilitate identification<br />
and development <strong>of</strong> robust predictive or prognostic biomarkers or to<br />
enable basic investigation <strong>of</strong> tumor or host biology.<br />
As experience in tissue banking has broadened, the role <strong>of</strong> rigorous collection<br />
protocols, tissue handling, and annotation has become increasingly appreciated.<br />
To this end, several groups have published guiding principles and standard<br />
operating procedures (SOPs) designed to advance the fields <strong>of</strong> tissue banking,<br />
biomarker development, and tumor biology (1,2).<br />
This chapter is designed to summarize key points in the process <strong>of</strong> informed<br />
consent, tissue collection, tissue preservation and storage, as well as extraction <strong>of</strong><br />
macromolecules <strong>of</strong> interest.<br />
INFORMED CONSENT<br />
Tissue banking requires informed consent by patients donating their tissues. The<br />
informed consent document should include standard topics such as study design,<br />
study purpose, and patient risks/benefits. Privacy rights are paramount for<br />
231
232 Untch and Olson<br />
patients when their health information is stored in databases, perhaps indefinitely.<br />
The Health Insurance Portability and Accountability Act and the Common<br />
Rule are federal regulations that require protection <strong>of</strong> patient identification.<br />
Database information and patient identifiers (name, social security number,<br />
institutional medical record ID) should remain separate. An arbitrary code system<br />
should be created to uniquely label specimens and data. The informed<br />
consent document should specify which investigator will keep the anonymous<br />
identifiers and patient identifiers together. In the event that third-party sharing <strong>of</strong><br />
patient identifier data becomes necessary, written authorization must be obtained<br />
from the patient. Also specified in the informed consent document is the<br />
patients’ right to withdraw from the study or have their specimen removed from<br />
the tissue databank. Contact information for these requests is provided to the<br />
patient at the time <strong>of</strong> document signing.<br />
A recent topic <strong>of</strong> debate has been whether giving consent for tissue<br />
banking also allows for indiscriminate investigations related to and unrelated to<br />
the initial study design. Implicit to the potential <strong>of</strong> tissue banking is the use <strong>of</strong><br />
specimens for areas <strong>of</strong> research that are unanticipated or currently unknown.<br />
However, the use <strong>of</strong> specimens for research not specifically noted in the<br />
informed consent document may violate religious and personal beliefs. The<br />
inclusion <strong>of</strong> broad consent for any possible project has been proposed by some as<br />
a solution (3). Others argue that this does not constitute informed consent, as the<br />
possibilities for potential research are unpredictable (4,5). Our approach has been<br />
to specifically describe research to be performed on collected specimens. This<br />
prevents any misunderstanding between patient and researcher. This is not useful<br />
for large repositories that collect tissue for future investigations.<br />
TISSUE ACQUISITION<br />
When tissues are devascularized and removed from the body, cellular metabolism<br />
is altered as hypoxia and acidification occur and nutrient delivery ceases.<br />
At a minimum, macromolecules in ischemic tissues are subject to degradation.<br />
Further, cellular response to ischemia may lead to stress-specific changes in gene<br />
expression. This may ultimately lead to changes in gene and protein expression<br />
or degradation until the cellular machinery stops or tissue preservation occurs.<br />
To accurately and reproducibly analyze gene and protein expression events<br />
within cells, tissue must be preserved as quickly and uniformly as possible.<br />
For all protocols where banking is superimposed upon clinical care, above<br />
all else, banking strategies must not interfere with pathologic assessment <strong>of</strong><br />
either the tumor or specimen resection margins. When designing these protocols,<br />
involvement <strong>of</strong> the pathologist who is custodian <strong>of</strong> the tissue record is critical.<br />
This is not to say that banking cannot occur before specimen processing in<br />
pathology, but that overall specimen integrity must be maintained during the<br />
research specimen collection (e.g., core biopsy <strong>of</strong> lumpectomy specimens).<br />
When a pathologist is immediately available, specimen banking after pathologic
Acquisition and Preservation <strong>of</strong> Tissue for Microarray Analysis 233<br />
gross evaluation is preferred. However, if a pathologist is not immediately<br />
available, then tumor specimens must be collected before pathologic assessment<br />
but without specimen disruption. Regardless <strong>of</strong> when during the processing <strong>of</strong><br />
the pathologic specimen banking occurs, representative samples <strong>of</strong> all banked<br />
tissue should be reviewed by the pathologist to both ensure that critical diagnostic<br />
material is not lost and to document that research tissue samples are<br />
indeed tumor tissues.<br />
To further ensure that research tissue acquisition will not have an impact<br />
on diagnosis, frozen specimens should be held and not analyzed until definitive<br />
pathologic assessment <strong>of</strong> the entire remaining specimen has been completed. At<br />
the time <strong>of</strong> definitive diagnosis <strong>of</strong> the surgical specimen, research specimens can<br />
then be deposited in the tumor bank (Fig. 1). If analysis <strong>of</strong> the surgical specimen<br />
does not demonstrate the abnormal pathology, then research specimens may be<br />
Figure 1 Flow diagram demonstrating the sample handling <strong>of</strong> core biospy specimens in<br />
patients suspected <strong>of</strong> having breast cancer. Research and clinical core biopsies should be<br />
pathologically examined so that the patient’s diagnosis is not affected and that research<br />
specimens contain the tissue <strong>of</strong> interest.
234 Untch and Olson<br />
used for frozen-section analysis. Should the frozen section <strong>of</strong> research specimens<br />
reveal abnormal histology not present in the surgical specimen (e.g., neoplasia<br />
is present in the research specimens and not in the surgical specimen), all<br />
samples can be turned over to pathology for routine processing. While this may<br />
result in a short delay as surgical pathology is reevaluating frozen research<br />
specimens, this can be explained in a preoperative consent form.<br />
In all cases, frozen research specimens should be held frozen until two<br />
weeks after a definitive pathologic report is issued on the diagnostic specimens.<br />
This hold period is desirable, as once DNA and RNA are extracted, no further<br />
diagnostic information relevant to patient care will be available.<br />
For clinical trials and research protocols, specimen collection needs to be<br />
standardized so that tumor samples are treated in a similar manner to minimize<br />
technical variability in gene and protein expression data. Several cooperative<br />
groups have developed banking SOPs for collection <strong>of</strong> formalin-fixed specimens<br />
and blood samples; however, few have until recently focused on frozen-tissue<br />
collection. The American College <strong>of</strong> Surgeons Oncology Group (ACOSOG) has<br />
developed effective procedures for collecting tumor tissue from breast cancer<br />
patients in neoadjuvant studies with good success. The European Human Frozen<br />
Tumor Tissue Bank has also developed similar SPOs that are followed across<br />
institutions and countries in an effort to reduce variability (2). In the former case,<br />
specialized collection kits have been privately developed and are in use by<br />
ACOSOG investigators for multisite tissue and blood collection at the time <strong>of</strong><br />
diagnostic biopsy and again at surgical resection (Fig. 2). Technicians responsible<br />
for tumor collection prepare reagents and containers before excision.<br />
Figure 2 A stereotactic biopsy device to retrieve breast cancer samples from excised<br />
lumpectomy specimens. (A, arrow) Excised lumpectomy specimens are placed in the<br />
device in an oriented fashion, and a specimen radiogram is taken to identify the location <strong>of</strong><br />
the tumor. (B) Coordinates are taken from the radiogram and are used to guide core biopsies<br />
taken from the tumor within an example lumpectomy specimen held in fixed orientation in<br />
the device.
Acquisition and Preservation <strong>of</strong> Tissue for Microarray Analysis 235<br />
Samples are required to be snap frozen within five minutes <strong>of</strong> excision,<br />
regardless <strong>of</strong> the method <strong>of</strong> tissue fixation (described subsequently). Rapid and<br />
uniform handling <strong>of</strong> specimens from patient to preservative is essential.<br />
TECHNIQUES OF CRYOPRESERVATION<br />
RNA degradation by tissue RNases can be detrimental to RNA yield and quality,<br />
with implications on microarray and quantitative PCR experiments. Traditional<br />
practice has been to snap freeze samples in liquid nitrogen followed by permanent<br />
storage at a minimum <strong>of</strong> 808C. This prevents changes to the RNA, and<br />
tumor banks using this method have been highly successful in producing highquality<br />
extracted RNA for microarray analysis. Given the complexity <strong>of</strong> sample<br />
acquisition and multi-institutional collection, recent studies have tried to determine<br />
the importance <strong>of</strong> immediate ex vivo freezing. Micke et al. (6) used ex vivo<br />
tumors at room temperature to determine the amount <strong>of</strong> time until RNA degradation<br />
occurred. Distinct ribosomal peaks were identified up to 16 hours,<br />
indicating that this tissue can remain stable for extended periods <strong>of</strong> time. Other<br />
sample-handling issues, such as freeze-thawing cycles, have also been studied.<br />
Jochumsen et al. (7) established that gene expression analysis is not altered in up<br />
to three freeze-thaw cycles. Despite these reports, it has been our experience that<br />
RNA degradation can be extremely variable, even when SOPs are employed<br />
(Figs. 3,4). To ensure quality RNA, samples should be frozen as soon as possible<br />
after being devascularized.<br />
Figure 3 RNA degradation is variable between two breast tumor samples. Samples are<br />
subjected to the same conditions over 120 minutes. Sample 2 degrades faster than sample 1,<br />
demonstrating the stability variation.
236 Untch and Olson<br />
Figure 4 The effect <strong>of</strong> two-hour extended ischemia time on microarray data. The same<br />
sample had RNA extracted at time 0 and after two hours and hybridized to the U95<br />
Affymetrix platform. Raw expression values were log normalized and graphed. Data<br />
demonstrate that at lower expression values greater variability exists between prolonged<br />
ischemic and nonischemic tissue. There was also variability demonstrated in published<br />
gene signatures describing prognosis. Source: From Refs. 26, 27, 31.<br />
Preservation <strong>of</strong> tissue morphology can be difficult in samples that are snap<br />
frozen. These samples are also subject to desiccation or freezer burn. Samples<br />
can be immersed in optimal cutting temperature (OCT) compound to preserve<br />
tissue morphology and prevent sample damage. This method has been shown to<br />
generate reproducible genomic DNA extraction results (8). Our group has not<br />
observed differences in microarray data when using OCT (unpublished observation).<br />
However, Turbett et al. (9) reported inhibition <strong>of</strong> a polymerase chain<br />
reaction (PCR) assay when compared with non-OCT compound–preserved<br />
specimens. OCT compound can also make specimen thawing and dissection<br />
more difficult. When specimens contain a mix <strong>of</strong> tissue (as in the case <strong>of</strong><br />
undissected tumor specimens with margins), separating whole tumor for RNA<br />
extraction can be difficult, particularly when trying to avoid time-dependant<br />
RNA degradation. Nonetheless, few studies have investigated OCT compound<br />
and its potential to alter microarray results. A recommendation for or against its<br />
use cannot be made at this time.<br />
Because <strong>of</strong> the challenges <strong>of</strong> sample handling (pathology analysis) and the<br />
potential for RNA degradation, RNA stabilization buffers have been developed.<br />
Commercially available RNAlater (Ambion inc.) has been shown to maintain<br />
RNA quality before snap freezing. Several studies have shown consistent<br />
microarray results when storing samples in RNA stabilization buffers (10–12).<br />
One such study found that samples could be stored up to one hour at 378C,<br />
without affecting the RNA or subsequent microarray data (13). These buffers
Acquisition and Preservation <strong>of</strong> Tissue for Microarray Analysis 237<br />
will be important for tissue acquisition protocols, particularly with regard to<br />
multi-institutional trials that will require transfering tissue specimens to a central<br />
tissue bank. This approach allows the samples to be shipped refrigerated rather<br />
than frozen. The principal disadvantages are that proteins are degraded, and<br />
subsequent sectioning for histologic verification <strong>of</strong> tumor identity is problematic.<br />
PARAFFIN-EMBEDED TISSUE FOR MICROARRAY ANALYSIS<br />
The discovery <strong>of</strong> paraffin wax is attributed to the noted hydrocarbon chemist,<br />
Dr. Carl (Karl) Ludwig von Reichenbach (1788–1869). After being developed in<br />
1830, pathology laboratories discovered its potential as a method for storing<br />
and preserving specimens. Throughout the twentieth century, formalin-fixed,<br />
paraffin-embedded tissue became a reliable way to archive tissue. Given the<br />
large quantity <strong>of</strong> specimens available in paraffin, these tissue banks were identified<br />
as a possible tissue source for array-based techniques. Numerous challenges have<br />
been identified that complicate the macromolecular extraction process from these<br />
samples.<br />
Sample handling is an important step that can lead to variability observed<br />
in microarray results. Ischemia time can pr<strong>of</strong>oundly affect protein, DNA, and<br />
RNA extraction because <strong>of</strong> enzymatic degradation. As noted above, sample<br />
collection is ideally performed by snap-freezing tissue in liquid nitrogen<br />
immediately after being devascularized and removed from a patient. Sample<br />
handling <strong>of</strong> formalin-fixed, paraffin-embedded tissue is variable, and this makes<br />
application <strong>of</strong> the array data potentially unreliable.<br />
Many investigators have attempted to develop methods for extraction <strong>of</strong><br />
these samples with mixed results. Given the cross-linking effects <strong>of</strong> formalin, it<br />
was thought that protein extraction for proteomic analysis would be impossible.<br />
However, attempts have been made to isolate proteins from paraffin, but results<br />
have been mixed and more technical development is required (14).<br />
Extraction <strong>of</strong> nucleic acids can be accomplished using commercially<br />
available proteinase digestion buffers (15). This method also eliminates the risk<br />
<strong>of</strong> Rnase activity in the sample as all previously fixed proteins are destroyed.<br />
Formalin fixation does not appear to affect the yield <strong>of</strong> RNA, unless the tissue is<br />
fixed for an extended period <strong>of</strong> time. This can result in cross-linking between<br />
RNA and protein (16). As long as the methylol groups added to the bases during<br />
fixation can be removed, the quality <strong>of</strong> the RNA is not perturbed (17) and results<br />
have been comparable with frozen-tissue controls (18).<br />
The most difficult problem encountered with these specimens has been that<br />
the total DNA or RNA yield from these samples is inadequate and <strong>of</strong>ten<br />
degraded (19). Yield <strong>of</strong> DNA or RNA is dependent on duration <strong>of</strong> protein<br />
digestion, temperature, and pH (20). Most array platforms will require several<br />
micrograms <strong>of</strong> RNA for hybridization. Amplification <strong>of</strong> the RNA has been<br />
suggested as means to increase quantity, but these techniques can result in<br />
considerable variability between samples (21). Further, amplification <strong>of</strong> partially
238 Untch and Olson<br />
degraded samples can also result in unreliable data. Statistical adjustments for<br />
nucleic acid amplification in microarray experiments are being developed and<br />
may eventually prove to be useful.<br />
As extraction, amplification, and statistical techniques improve, formalinfixed,<br />
paraffin-embedded tissue may provide an additional resource for researchers.<br />
Nevertheless, the variability in sample handling history makes application <strong>of</strong> the<br />
array data from these samples less than reliable. At this point in time, use <strong>of</strong> these<br />
samples for application to clinical investigations is discouraged.<br />
FINE NEEDLE ASPIRATION, CORE NEEDLE BIOPSY,<br />
AND SURGICAL SPECIMENS IN MICROARRAY ANALYSIS<br />
Using micorarray analysis, numerous genomic signatures have been developed<br />
that attempt to predict tumor phenotypes, including lymph node metastasis,<br />
tumor growth patterns, postoperative margin positivity, chemosensitivity, and<br />
oncogenic pathway activation. Most <strong>of</strong> these analyses have been based on<br />
samples collected at the time <strong>of</strong> surgery. These large volume samples can yield<br />
adequate RNA for array hybridization and also afford the opportunity to assess<br />
multiple areas <strong>of</strong> the tumor and subcellular compartments via laser capture<br />
microdissection. However, tumor size and growth is known to affect gene<br />
expression, and successful application <strong>of</strong> the developed models to earlier stage,<br />
preoperative biopsy specimens may not be possible.<br />
Nonetheless, application <strong>of</strong> these predictive tests depends on obtaining<br />
adequate tissue for microarray analysis before a definitive surgical intervention.<br />
The preoperative diagnosis <strong>of</strong> breast cancer is typically achieved though two<br />
methods <strong>of</strong> tumor biopsy: core needle biopsy (CNB) and fine needle aspiration<br />
(FNA). For micoarray-based predictive testing, an optimal protocol would allow<br />
these two modalities to collect tissue for pathologic analysis and for molecular<br />
extraction.<br />
FNA is performed by inserting a small gauge needle, typically under image<br />
guidance, into a tumor or lesion. Tissue is removed through a negative-pressure<br />
syringe. Tumor architecture is typically destroyed during this process, but the<br />
resultant sample can be analyzed under the microscope for tumor cell characteristics.<br />
Likewise, this small amount <strong>of</strong> tissue can be harvested for RNA and<br />
microarray analysis. However, concern exists over adequate sample amount (22).<br />
Several studies have shown that FNA can produce tissue in amounts appropriate<br />
for pathology review and array applications (23,24).<br />
CNB is performed under image or visual guidance in a fashion similar to<br />
FNA. However, with CNB, a larger bore needle is used, and the resultant<br />
specimen has maintained tissue architecture, allowing for a more accurate<br />
pathologic analysis. This larger amount <strong>of</strong> tissue can also be used for array-based<br />
studies. Several studies have addressed adequacy <strong>of</strong> tissue acquisition in core
Acquisition and Preservation <strong>of</strong> Tissue for Microarray Analysis 239<br />
biopsy samples (13,24). Studies from our laboratory indicate that core biopsy<br />
specimens can be collected before surgery and can undergo successful RNA<br />
extraction (unpublished observation). The average yield from core biopsy<br />
specimens was 10.4 mg <strong>of</strong> RNA, enough for most array platforms. After<br />
extraction, RNA samples underwent hybridization to the HU95 Affymetrix<br />
microarray platform and then chemosenstivity and oncogenic pathway signatures<br />
were successfully applied. This is consistent with the work <strong>of</strong> Ellis et al. (13),<br />
who was able to extract RNA from core biopsy specimens and distinguish cancer<br />
and noncancer phenotypes.<br />
LASER CAPTURE MICRODISSECTION<br />
Recently, focus has shifted away from whole-tumor microarray analysis to<br />
analysis <strong>of</strong> individual cell types within a tumor. A tumor is composed <strong>of</strong> a<br />
heterogeneous group <strong>of</strong> cells, malignant cells, premalignant cells, normal<br />
parenchymal cells, and supportive cells. This cellular heterogeneity must be<br />
taken into account when applying array-based techniques (25). Numerous successful<br />
expression-pr<strong>of</strong>iling studies have been performed using whole-tumor<br />
macromolecular extraction (26–28). These analyses thus incorporate a variety <strong>of</strong><br />
different cell types. Laser capture microdissection allows one to isolate histologically<br />
distinct cell types and perform subsequent analysis. The technique<br />
employs an infrared laser in conjunction with light microscopy. Prepared histologic<br />
slides are covered with a transfer film. When cells <strong>of</strong> interest are identified,<br />
the laser is activated over the cells. This process causes the cells to attach<br />
to the transfer film. The remainder <strong>of</strong> the sample remains, and the isolated cells<br />
can undergo extraction procedures. Notably, this procedure can result in lower<br />
yields <strong>of</strong> cells requiring amplification <strong>of</strong> nucleic acids for use in microarray<br />
experiments (29,30). Laser capture microdissection has the potential to not only<br />
improve our understanding <strong>of</strong> cellular deregulation but also to help determine<br />
how other cells within the tumor contribute to the overall tumor phenotype.<br />
Immune cells, vascular structures, and normal parenchymal cells within a tumor<br />
may contribute significantly to the growth and metastatic potential. Expression<br />
analysis <strong>of</strong> these individual cell types may help elucidate their roles.<br />
SUMMARY<br />
Acquisition and preservation <strong>of</strong> tissue for microarray experiments requires<br />
understanding <strong>of</strong> informed consent, sample handling, macromolecular extraction,<br />
and sampling methods. Significant advances in these areas have been made<br />
in recent years to improve the quality <strong>of</strong> microarray data. Appreciating these<br />
aspects is paramount if the application <strong>of</strong> microarray techniques is to contribute<br />
meaningfully to the care <strong>of</strong> the cancer patient.
240 Untch and Olson<br />
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1. McShane LM, Altman DG, Sauerbrei W, et al. Reporting recommendations for tumor<br />
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2. Mager SR, Oomen MN, Morente MM, et al. Standard operating procedure for the<br />
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3. Hansson MG, Dillner J, Bartram CR, et al. Should donors be allowed to give broad<br />
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4. Maschke KJ. Alternative consent approaches for biobank research. Lancet Oncol<br />
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5. Rothstein MA. Expanding the ethical analysis <strong>of</strong> biobanks. J Law Med Ethics 2005;<br />
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7. Jochumsen KM, Tan Q, Dahlgaard J, et al. RNA quality and gene expression analysis<br />
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82(1):95–102.<br />
8. Sun Y, Hegamyer G, Colburn NH. A simple method using PCR for direct sequencing<br />
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9. Turbett GR, Sellner LN. The use <strong>of</strong> optimal cutting temperature compound can inhibit<br />
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10. Gotzer MA, Patti R, Geoerger G, et al. Biological stability <strong>of</strong> RNA isolated from<br />
RNAlater-treated brain tumor and neuroblastoma xenografts. Med Pediatr Oncol<br />
2000; 34(6):438–442.<br />
11. Florell SR, C<strong>of</strong>fin CM, Holden JA, et al. Preservation <strong>of</strong> RNA for functional<br />
genomic studies: a multidisciplinary tumor bank protocol. Mod Pathol 2001; 14(2):<br />
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12. Mutter GL, Zahrieh D, Liu C, et al. Comparison <strong>of</strong> frozen and RNALater solid tissue<br />
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5(1):88.<br />
13. Ellis M, Davis N, Coop A, et al. Development and validation <strong>of</strong> a method for using<br />
breast core needle biopsies for gene expression microarray analyses. Clin <strong>Cancer</strong> Res<br />
2002; 8(5):1155–1166.<br />
14. Becker KF, Schott C, Hipp S, et al. Quantitative protein analysis from formalin-fixed<br />
tissues: implications for translational clinical research and nanoscale molecular<br />
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15. Krafft AE, Duncan BW, Bijwaard KE, et al. Optimization <strong>of</strong> the isolation and<br />
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16. Moller K, Rinke J, Ross A, et al. The use <strong>of</strong> formaldehyde in RNA-protein crosslinking<br />
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76(1):175–187.<br />
17. Lewis F, Maughan NJ, Smith V, et al. Unlocking the archive: gene expression in<br />
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18. Specht K, Richter T, Muller U, et al. Quantitative gene expression analysis in<br />
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19. Jackson DP, Lewis FA, Taylor GR. Tissue extraction <strong>of</strong> DNA and RNA and analysis<br />
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21. Coombs NJ, Gough AC, Primrose JN. Optimisation <strong>of</strong> DNA and RNA extraction<br />
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25. Fuller AP, Palmer-Toy D, Erlander MG, et al. Laser capture microdissection and<br />
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26. van de Vijver MJ, He YD, van ’t Veer LJ, et al. A gene-expression signature as a<br />
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29. Caretti E, Devarajan K, Coudry R, et al. Comparison <strong>of</strong> RNA amplification methods<br />
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microdissection. J Cell Biochem 2008; 103(2):556–563.<br />
30. Schuetz CS, Bonin M, Clare SE, et al. Progression-specific genes identified by<br />
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primary solid tumors. Nat Genet 2003; 33(1):49–54.
17<br />
Tapping into the Formalin-Fixed<br />
Paraffin-Embedded (FFPE) Tissue<br />
Gold Mine for Individualization<br />
<strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong> Treatment<br />
Mark Abramovitz<br />
VM Institute <strong>of</strong> Research, Montreal, Que´bec, Canada<br />
Brian Leyland-Jones<br />
Winship <strong>Cancer</strong> Institute, Emory University School <strong>of</strong> Medicine,<br />
Atlanta, Georgia, U.S.A.<br />
INTRODUCTION<br />
It is now clearly recognized that breast cancer is a heterogeneous disease that<br />
requires personalized targeted therapy in order to have the greatest impact on<br />
survival. This necessitates the development <strong>of</strong> biomarkers that can be used to<br />
predict outcome and response to therapy. Unfortunately, very few biomarkers<br />
have made their way from the research bench to the clinic. The main reason for<br />
this is that in order to validate breast tumor markers, large sample sets from welldefined<br />
patient groups are required, although this could be overcome by tapping<br />
into large numbers <strong>of</strong> well-defined banked tumor specimens. The efforts that are<br />
being made, though, toward the development <strong>of</strong> biomarkers in order to improve<br />
the assessment <strong>of</strong> cancer prognosis and diagnosis are great, given the enormous<br />
potential that they embody.<br />
243
244 Abramovitz and Leyland-Jones<br />
Formalin-fixed, paraffin-embedded (FFPE) tissue samples represent the<br />
largest collection <strong>of</strong> well-annotated, clinical tumor samples that are readily<br />
available for conducting retrospective studies and, therefore, represent a potential<br />
gold mine from which important biomarkers are just waiting to be discovered,<br />
given that the right tools can be brought to bear upon this invaluable resource.<br />
However, until relatively recently, a major caveat regarding the use <strong>of</strong> these FFPE<br />
specimens for genomewide studies was that due to the formalin fixation process,<br />
the standard procedure for handling tumor tissue samples in pathology labs around<br />
the world, RNA degradation occurred, greatly limiting their potential utility. In<br />
fact, with regard to nucleic acid–based assays, FFPE specimens have been mainly<br />
subjected to reverse transcribed-polymerase chain reaction (RT-PCR) analysis.<br />
This chapter will describe the progress that has been made in greatly expanding the<br />
use <strong>of</strong> FFPE tumor specimens for several genomic-based applications that will<br />
greatly increase our ability to tap into this gold mine and extract the biomarker and<br />
genomic data nuggets that will improve cancer prognosis, diagnosis, and treatment.<br />
EXTRACTION OF RNA FROM FFPE TUMOR TISSUE<br />
SAMPLES FOR GENE EXPRESSION ANALYSIS<br />
FFPE tissue samples, a vast archive <strong>of</strong> pathologically well-characterized clinical<br />
samples from randomized trials, are an enormous potential resource that will allow<br />
translational scientists to more fully describe breast cancers at the molecular level<br />
and will ultimately have important etiologic and clinical implications. Even<br />
though the degradation <strong>of</strong> RNA that occurs because <strong>of</strong> the formalin fixation<br />
process results in an RNA species with an average size <strong>of</strong> approximately 200 nt<br />
(1), several groups have illustrated that it is feasible to extract and purify RNA<br />
from such fixed tissue and perform gene expression pr<strong>of</strong>iling (2–9). With the<br />
development <strong>of</strong> real-time quantitative (q) RT-PCR technology that has high<br />
detection sensitivity and high dynamic range, it is now possible to detect even rare<br />
messages in FFPE tissue and to examine the variation <strong>of</strong> expression over quite a<br />
large dynamic range. Amplicons are designed specifically on small segments <strong>of</strong><br />
DNA [
The Formalin-Fixed Paraffin-Embedded (FFPE) Tissue Gold Mine 245<br />
method with multianalyte capability for potential use in clinical research and<br />
diagnostic assays. Two multianalyte assays, one with 48 genes and the other with<br />
92 genes, were designed and tested in different experiments with preserved<br />
human breast cancer tissue. In the first case, all <strong>of</strong> the genes pr<strong>of</strong>iled in FFPE<br />
RNA yielded measurable values, and the overall pr<strong>of</strong>ile was validated by its<br />
similarity to that generated with well-preserved RNA from matched freshly<br />
frozen tissue. In the second experiment using a 92-gene assay, only one <strong>of</strong> the<br />
tested genes failed to yield a signal. Measured levels <strong>of</strong> estrogen receptor (ER),<br />
progesterone receptor (PR), and human epidermal growth factor receptor 2<br />
(HER2) mRNAs were concordant with the levels <strong>of</strong> the respective proteins as<br />
measured by immunohistochemistry (IHC) at an independent clinical reference<br />
laboratory. Approximately 90% concordance was obtained when RT-PCR<br />
expression results for ER and PR were dichotomized into positive and negative<br />
values and compared with ER- and PR-positive and ER- and PR-negative<br />
assignments based on IHC. It is noteworthy that similar levels <strong>of</strong> concordance<br />
were found in other comparative studies <strong>of</strong> IHC versus RT-PCR (15,16).<br />
DEVELOPMENT OF AN RT-PCR-BASED BREAST CANCER<br />
CLINICAL DIAGNOSTIC TEST: ONCOTYPE DX ASSAY<br />
A major development in this area has been the development <strong>of</strong> a multigene qRT-<br />
PCR assay to predict the likelihood <strong>of</strong> breast cancer recurrence in node-negative,<br />
ER-positive, tamoxifen-treated patients (17). The authors studied the likelihood<br />
<strong>of</strong> distant recurrence in breast cancer patients who had involved lymph nodes and<br />
ER-positive tumors that were poorly defined by clinical and histopathologic<br />
measures. They tested whether a prospectively-defined 21-gene RT-PCR assay<br />
<strong>of</strong> FFPE tumor tissue could predict the likelihood <strong>of</strong> distant recurrence in<br />
patients in the National Surgical Adjuvant <strong>Breast</strong> and Bowel Project (NSABP)<br />
B-14 clinical trial. Measured levels <strong>of</strong> gene expression for 16 cancer-related and<br />
5 reference genes were used, by a prospectively defined algorithm, to calculate a<br />
recurrence score (RS) and risk group (low, intermediate, or high) for each<br />
patient. Adequate RT-PCR pr<strong>of</strong>iles were obtained in 668 <strong>of</strong> 675 tumor blocks.<br />
The proportion <strong>of</strong> patients categorized as low, intermediate, or high risk by the<br />
RT-PCR assay was 51%, 23%, and 27%, respectively. The Kaplan–Meier estimates<br />
and 95% confidence intervals for the rates <strong>of</strong> distant recurrence at 10 years<br />
were 6.8% (4.0%, 9.6%), 14.3% (8.3%, 20.3%), and 30.5% (23.6%, 37.4%),<br />
respectively, for the low-, intermediate-, and high-risk groups; the rate for<br />
the low-risk group was significantly lower than the rate for the high-risk group<br />
(p < 0.001). In a multivariate Cox model, RS provides significant (p < 0.001)<br />
predictive power that goes beyond age and tumor size. The RS was also predictive<br />
<strong>of</strong> overall survival (p < 0.001) and could be used as a continuous function<br />
to predict distant recurrence for individual patients. Finally, the RS was prospectively<br />
validated to predict the likelihood <strong>of</strong> distant recurrence in tamoxifentreated<br />
breast cancer patients with node-negative, ER-positive tumors (18,19).
246 Abramovitz and Leyland-Jones<br />
This methodology has been sufficiently successful, so that the Food and<br />
Drug Administration (FDA) approved it in 2004 to become commercially available<br />
[Genomic Health, Inc. (GHI), Redwood City, California, U.S.]. Oncotype DX<br />
is a diagnostic assay that quantifies the likelihood <strong>of</strong> breast cancer recurrence in<br />
women with newly diagnosed stage I or II, node-negative, ER-positive breast<br />
cancer who will be treated with tamoxifen. More importantly, this test will pave<br />
the way for individualization <strong>of</strong> cancer therapy. While the St. Gallen classification<br />
distinguished 7.9% <strong>of</strong> patients as low risk, the GHI RS identified 50.6% <strong>of</strong><br />
patients as low risk. Similarly, St. Gallen overestimated the high-risk patients at<br />
58.8%, while the GHI test classified high-risk patients at only 27.1%. It is obvious<br />
that St. Gallen underestimated the low-risk patients and overestimated the highrisk<br />
ones. The advantage <strong>of</strong> the newly developed multigene RS is, therefore, that<br />
low-risk patients can be saved from potentially toxic, high-dose chemotherapy.<br />
RNA-BASED EXPRESSION PROFILING OF<br />
FFPE BREAST TUMOR SPECIMENS<br />
Expression Pr<strong>of</strong>iling: The DASL Assay<br />
Gene expression pr<strong>of</strong>iling has become an important aspect in prediction, prognosis,<br />
and cancer modeling. Gene signatures resulting from expression pr<strong>of</strong>iling<br />
studies have the potential to define cancer subtypes, predict the clinical outcome<br />
(recurrence <strong>of</strong> disease) and response to specific therapies, and analyze oncogenic<br />
pathways (20–23). Investigations <strong>of</strong> gene pathways and interactions indicated by<br />
gene signatures that are truly predictive <strong>of</strong> the clinical endpoints are necessary to<br />
understand the biology underlying this predictive value. More importantly, when<br />
combined with clinical and demographic factors, multiple forms <strong>of</strong> molecular<br />
(protein- and gene-based) data can provide information that has the potential to<br />
identify unique characteristics <strong>of</strong> individuals and so lead to individualized<br />
treatment strategies (24,25).<br />
Although most array-based assays utilize RNA prepared from frozen<br />
specimens, the newer cDNA-mediated annealing, selection, extension, and<br />
ligation (DASL) assay (Illumina, Inc., San Diego, California, U.S.) platform was<br />
specifically designed to pr<strong>of</strong>ile small transcripts typically found in FFPE tissue<br />
(26–31). The DASL platform is based on massively multiplexed RT-PCR<br />
applied in a microarray format that allows for the determination <strong>of</strong> expression <strong>of</strong><br />
up to 512 genes (502 genes in the standard cancer panel available from Illumina)<br />
using a minimal amount <strong>of</strong> total RNA (100 to *200 ng per assay) isolated<br />
from 96 FFPE tumor tissue samples in a high-throughput format (29,30). In the<br />
procedure, biotinylated random nonamers (biotin-d(N)9) and oligo d(T)18 are<br />
used for cDNA synthesis, and therefore probes are designed so that they can<br />
target any unique region <strong>of</strong> the gene without limiting the selection <strong>of</strong> the optimal<br />
probe to the 3 0 ends <strong>of</strong> transcripts. In addition, because <strong>of</strong> the small size <strong>of</strong> the<br />
targeted gene sequence (50 nucleotides), along with the use <strong>of</strong> random primers in
The Formalin-Fixed Paraffin-Embedded (FFPE) Tissue Gold Mine 247<br />
Figure 1 The DASL platform for pr<strong>of</strong>iling both RNA and miRNA. (A) The DASL assay.<br />
FFPE tumor tissue samples, 5- to 10-mm shaved sections or from microscope slides, are<br />
first subjected to deparaffinization and then RNA extraction. The extracted RNA is<br />
fragmented with an average size <strong>of</strong> approximately 200 nucleotides. Biotinylated cDNA is<br />
made from the fragmented RNA using random primers (9-mers). Complex oligonucleotides<br />
that contain a gene-specific sequence and a unique address sequence are used to<br />
selectively bind to the cDNA. Extension and then ligation follow, resulting in cDNA<br />
products that are subsequently amplified using fluorescently labeled common primers.<br />
Depicted are three gene-specific oligonucleotide probe sets hybridized to cDNAs from a<br />
single gene. The amplified products contain 1536 unique addresses (3 addresses per gene<br />
and therefore 512 genes) and are hybridized to the 50,000-bead array (each unique<br />
complementary address bead at *30-fold redundancy). The address sequence directs<br />
hybridization to their complements on the universal bead arrays. (B) The miRNA array.<br />
cDNA derived from total RNA is recognized as a specific miRNA by MSO hybridization.<br />
Primer extension imparts further target specificity. The product <strong>of</strong> this extension is used as<br />
a template for universal PCR. Abbreviations: DASL, cDNA-mediated annealing, selection,<br />
extension, and ligation; FFPE, formalin-fixed, paraffin-embedded; PCR, polymerase<br />
chain reaction.<br />
the cDNA synthesis, RNAs that are otherwise too degraded for conventional<br />
microarray analysis can be readily detected. Sequence-specific query oligonucleotides<br />
encompassing primer extension, ligation, and universal PCR in highlyplexed<br />
reactions (1536-plex), two-color labeling, and redundant (*30-fold<br />
redundancy <strong>of</strong> each bead type) feature representations are used to probe up to<br />
three different sites on each cDNA (Fig. 1A) and, therefore, lend the assay the<br />
necessary sensitivity and reproducibility for quantitative detection <strong>of</strong> differential<br />
expression using RNA from FFPE tissue (29,30). In an initial study, a 231-gene
248 Abramovitz and Leyland-Jones<br />
cancer panel was used in the DASL assay in order to pr<strong>of</strong>ile both breast and<br />
colon cancer FFPE tumor samples. Cluster analysis was able to separate breast<br />
from colon tissue types and, subsequently, divide each tissue sample set into<br />
cancer versus normal (32). More recently, the DASL assay has been used to<br />
identify RNA signatures in prostate cancer, including a 16-gene set that correlates<br />
with prostate cancer relapse (31,33).<br />
We have designed our own custom 512-gene breast cancer–related gene<br />
panel so that it incorporates previously identified signature genes from various<br />
breast cancer microarray studies that have been used in the classification <strong>of</strong><br />
breast tumors (34,35), in prognosis (MammaPrint) (36) and as predictors <strong>of</strong><br />
outcome to treatment (17,37–40), as well as a host <strong>of</strong> additional genes that have<br />
been implicated as playing a role in a number <strong>of</strong> breast cancer–related processes,<br />
including proliferation, angiogenesis, (41,42), metastasis (43,44), DNA repair,<br />
apoptosis (45), thrombosis (46), and custom-selected genes taken from the most<br />
recently published data on breast cancer (47–49). Additional cancer-related<br />
genes include oncogenes, cell cycle genes, telomerase-related genes, amplified<br />
genes, breast cancer stem cell genes, and senescence-related genes (50–52).<br />
MiRNA Pr<strong>of</strong>iling<br />
MiRNAs (miRNAs) are small noncoding RNA species that regulate the translation<br />
and stability <strong>of</strong> mRNA messages for hundreds <strong>of</strong> downstream target genes<br />
via partial complementarity to short sequences in the 3 0 -untranslated region<br />
(3 0 UTR) <strong>of</strong> mRNAs. The aberrant expression or mutation <strong>of</strong> miRNAs, a distinct<br />
class <strong>of</strong> RNAs that control gene expression via translation repression or degradation,<br />
has been noted in cancers and suggests a key role for miRNAs in<br />
tumorogenesis. MiRNAs can act both as tumor suppressors and as oncogenes,<br />
depending on the sets <strong>of</strong> downstream targets that they regulate (53). Studies have<br />
recently implicated nearly 30 <strong>of</strong> the over 450 known human miRNAs in breast<br />
cancer, as tumor suppressor genes or as oncogenes, with significant deregulation<br />
among only a handful (54). Others are correlated with specific tumor<br />
types as identified by protein expression. For example, the mir-30 cluster is<br />
downregulated in both ER-negative, PR-positive and ER-positive, PR-negative<br />
tumors (54). Differential expression has also been noted by tumor stage or proliferative<br />
indices. The tumor suppressor, p53, was recently shown to regulate<br />
expression <strong>of</strong> mir-34b and mir-34c (55). One key set <strong>of</strong> tumor suppressor miRNAs<br />
includes mir15a-16, which targets the translation <strong>of</strong> the suppressor <strong>of</strong> apoptosis,<br />
Bcl-2 (56). These observations suggest that specific miRNAs are associated with<br />
major breast cancer subgroups, including hormone-positive and hormone-negative<br />
subgroups and the basal or triple negative subtype, and that the deregulated<br />
miRNAs may differ between African-American and Caucasian women.<br />
These low-molecular weight miRNAs can be extracted at the same time as<br />
total RNA is being isolated from FFPE tissue samples. qRT-PCR can be used to<br />
screen tumor samples for miRNA expression. The TaqMan TM array human
The Formalin-Fixed Paraffin-Embedded (FFPE) Tissue Gold Mine 249<br />
miRNA panel from Applied Biosystems allows for the expression pr<strong>of</strong>iling <strong>of</strong><br />
365 miRNAs from one RNA sample. Illumina, Inc., has recently adapted its<br />
DASL assay for high-throughput multiplexed miRNA expression pr<strong>of</strong>iling<br />
(Fig. 1B). It has made a human miRNA panel that can be used to detect 470<br />
miRNAs described in the miRBase database and an additional 265 potential<br />
miRNAs that have been identified in an RNA-primed array-based Klenow<br />
extension (RAKE) analysis study (57,58). With these arrays and tools now<br />
available, our understanding <strong>of</strong> the role that miRNAs play in breast cancer will<br />
be greatly enhanced.<br />
Whole-Genome Expression Pr<strong>of</strong>iling<br />
In order to conduct expression pr<strong>of</strong>iling using microarrays, it has been necessary<br />
to obtain freshly frozen tissue as the starting material for RNA extraction. This<br />
presents limitations with regard to obtaining enough samples, enough tissue per<br />
sample, as well as the logistics <strong>of</strong> storing this type <strong>of</strong> material. Most recently,<br />
new technologies have come into play that enable the use <strong>of</strong> RNA extracted from<br />
FFPE to be used in genomewide gene expression pr<strong>of</strong>iling. The company<br />
NuGEN Technologies, Inc. (San Carlos, California, U.S.) has developed an<br />
amplification system that can take picogram amounts <strong>of</strong> total RNA to yield<br />
micrograms <strong>of</strong> amplified cDNA that can be fragmented and labeled for same-day<br />
hybridization to a variety <strong>of</strong> array formats, including Affymetrix, Agilent, and<br />
Illumina microarrays (59). This type <strong>of</strong> technology will undoubtedly generate<br />
significant microarray data from FFPE breast tumor samples in the near future<br />
further adding to the utility <strong>of</strong> FFPE specimens.<br />
DNA-BASED ARRAY PROFILING THAT EMPLOYS FFPE SAMPLES<br />
Array CGH and SNP Pr<strong>of</strong>iling<br />
DNA copy number alterations (CNAs), which are the result <strong>of</strong> genomic instability,<br />
can give rise to gene amplifications and gene deletions that play important<br />
roles in tumor progression, and it has become apparent that an increased number<br />
<strong>of</strong> CNAs correlate with a poor prognosis for the patient. Several genes that<br />
undergo CNAs have been identified that act as either oncogenes or tumor suppressors,<br />
which are crucial factors in the disease process. With regard to breast<br />
cancer, array comparative genomic hybridization (CGH) has been used principally<br />
to identify novel breast cancer-related genes, genes that can be used to classify<br />
different types <strong>of</strong> breast cancer, genes that yield prognostic or predictive information,<br />
or genes that may represent important therapeutic targets. Single nucleotide<br />
polymorphisms (SNPs) have been linked to individual variations in breast cancer<br />
susceptibility. Genetic variations in genes involved in ER and drug metabolism and<br />
genes involved in cell cycle control as well as variations depending on ethnic<br />
background have been found to contribute to breast cancer susceptibility.
250 Abramovitz and Leyland-Jones<br />
Array CGH has been used in the classification <strong>of</strong> breast tumors and has<br />
shown that subtypes can be discerned on the basis <strong>of</strong> genomic instability patterns.<br />
In one study, array CGH, made up <strong>of</strong> 2464 genomic clones, was applied to<br />
the analysis <strong>of</strong> CNAs in 62 sporadic invasive ductal carcinomas, and three breast<br />
tumor subtypes were delineated based on genomic DNA CNAs (60). In another<br />
study, 89 breast cancer tumors <strong>of</strong> the luminal A, luminal B, HER2, and basal-like<br />
subtypes were also found to yield distinct CNA pr<strong>of</strong>iles, suggesting that genomic<br />
instability mechanisms differ between subtypes (61). Most array CGH studies<br />
have made use <strong>of</strong> DNA prepared from fresh or frozen tumor samples; however,<br />
the ability to tap into archival FFPE clinical samples would greatly accelerate the<br />
rate at which disease-related genomic change, including translocation breakpoints,<br />
could be more easily identified. Recent studies have addressed this<br />
important issue and shown that DNA can be prepared from FFPE tissue samples<br />
and used successfully in array CGH (62–64). Van Beers and colleagues (2006)<br />
have also developed a multiplex PCR predictor that can be used to assess the<br />
quality <strong>of</strong> the DNA extracted from FFPE samples (65) prior to use in array CGH.<br />
Recently, Oosting and co-workers (2007) (66) have compared the Affymetrix<br />
GeneChip and Bac array CGH with the Illumina BeadArray platform, which is a<br />
high-density SNP microarray that allows for the measurement <strong>of</strong> both genomic<br />
copy number and loss <strong>of</strong> heterozygosity (LOH). The BeadArray platform (Fig. 2)<br />
is amenable for use with fragmented DNA from FFPE tissue because <strong>of</strong> the fact<br />
that only short, intact genomic segments <strong>of</strong> approximately 40 bp flank each SNP<br />
on the array. The authors found that when paired comparisons <strong>of</strong> DNA prepared<br />
from freshly frozen and FFPE tissue were conducted, the patterns <strong>of</strong> genomic<br />
aberrations were basically identical.<br />
The use <strong>of</strong> array CGH and SNP pr<strong>of</strong>iling in conjunction with the vast<br />
archive <strong>of</strong> FFPE clinical breast cancer samples will allow for the identification <strong>of</strong><br />
chromosomal aberrations as well as novel variants that contribute to breast<br />
cancer, and this will lead to uncovering novel breast cancer-related genes that<br />
will be key to understanding the molecular mechanisms underlying the disease.<br />
Methylation Pr<strong>of</strong>iling<br />
Epigenetic changes, which involve very subtle molecular modifications, can<br />
cause activation or suppression <strong>of</strong> genes that can lead to abnormalities resulting<br />
in tumorigenesis (67), and these epigenetic changes have been found to occur in<br />
many cancers, including breast, colon, prostate, and blood cancer. The epigenotype<br />
is affected by such factors as the environment, age, epigenotype <strong>of</strong> the<br />
parents, and the genotype at the loci that regulate DNA methylation and chromatin,<br />
and these factors can cause aberrant methylation <strong>of</strong> the cytosine residue <strong>of</strong><br />
DNA, resulting in gene regulation.<br />
Illumina has developed an array-based platform (Fig. 3) that can be used in<br />
conjunction with its standard <strong>Cancer</strong> Methylation Panel, which spans 1505 CpG<br />
loci selected from 807 genes with 71% <strong>of</strong> those selected genes containing at least
The Formalin-Fixed Paraffin-Embedded (FFPE) Tissue Gold Mine 251<br />
Figure 2 Whole-genome expression pr<strong>of</strong>iling on the BeadChip array. (A) Gene-specific<br />
probes are attached to beads, which are then assembled into the arrays. For simplicity,<br />
this diagram shows only one oligomer attached to the bead; actual beads have hundreds<br />
<strong>of</strong> thousands <strong>of</strong> copies <strong>of</strong> the same sequence attached. (B) Sentrix Human-6 and<br />
HumanRef-8 Expression BeadChips. More than 48,000 different bead types, each with<br />
a unique sequence, are represented on the Human-6 Expression BeadChip, greater<br />
than 24,000 on the HumanRef-8 Expression BeadChip. Each bead contains hundreds<br />
<strong>of</strong> thousands <strong>of</strong> copies <strong>of</strong> covalently attached oligonucleotide probes. Beads are<br />
assembled into greater than 1.6 million pits, each measuring 3 mm in diameter,<br />
generating an average 30-fold redundancy for each sequence represented on the array.<br />
2 CpG sites. The <strong>Cancer</strong> Methylation Panel includes genes selected from various<br />
functional classes, including oncogenes and tumor suppressor genes; genes<br />
involved in cell cycle control, DNA repair, differentiation, and apoptosis; and<br />
X-linked and imprinted genes (68). Although DNA prepared from FFPE tumor<br />
samples has not yet been specifically validated for use in the assay, it would be<br />
useful to be able to scan large numbers <strong>of</strong> FFPE samples for methylation patterns,<br />
which could provide important clues regarding tumor type.<br />
THE WAY FORWARD<br />
The goal is to obtain as complete a molecular picture <strong>of</strong> a breast tumor as possible,<br />
which can now be realistically undertaken using FFPE tissue specimens, truly<br />
untapped gold now ready to be brought to the surface and subjected to a thorough<br />
nucleic acid characterization. Pr<strong>of</strong>iling platforms can now be brought to<br />
bear on these invaluable samples in order to build a comprehensive picture <strong>of</strong> the<br />
tumor. In the scheme shown in Figure 4, FFPE samples, either sections or cores,<br />
are used to extract DNA, RNA, and miRNA. These nucleic acids can now be
252 Abramovitz and Leyland-Jones<br />
Figure 3 The DNA methylation assay. (A) In order to perform a DNA methylation assay,<br />
genomic DNA must first undergo bisulfite conversion. Cytosines are converted to uracils,<br />
but methyl-cytosine is unreactive. (B) In order to take advantage <strong>of</strong> this differential<br />
reactivity to bisulfite treatment, two pairs <strong>of</strong> complex oligonucleotide probe sets are<br />
designed for each CpG site. The first probe pair is made up <strong>of</strong> an ASO and a LSO to<br />
interrogate the methylated state <strong>of</strong> the CpG site and a corresponding ASO-LSO pair for the<br />
unmethylated state. The ASO is used to determine whether a site is methylated or not and<br />
also incorporates a universal PCR primer sequence, P1 or P2. P1 and P2 are fluorescently<br />
labeled, each with a different dye, and associated with the “T” (unmethylated) allele or<br />
the “C” (methylated) allele, respectively. The LSO is made up <strong>of</strong> three parts: a CpG<br />
(Continued)
The Formalin-Fixed Paraffin-Embedded (FFPE) Tissue Gold Mine 253<br />
Figure 4 Analysis <strong>of</strong> FFPE specimens on multiple array platforms. FFPE tissue samples,<br />
as sections on either slides, sections, or cores are available for extraction <strong>of</strong> nucleic acids.<br />
DNA, RNA, and miRNA can be isolated and applied to various array platforms in order to<br />
maximize the molecular information that can be obtained from a single tumor sample.<br />
DNA can be subjected to array CGH, SNP analysis, LOH pr<strong>of</strong>iling, and methlylation<br />
analysis. RNA can be used in conjunction with expression pr<strong>of</strong>iling platforms that include<br />
the DASL assay and whole-genome expression pr<strong>of</strong>iling on different microarray platforms.<br />
MiRNA can be used to conduct miRNA pr<strong>of</strong>iling. This ability to utilize FFPE<br />
tumor tissues for detailed molecular characterization will undoubtedly have a pr<strong>of</strong>ound<br />
impact on our understanding <strong>of</strong> tumor biology. Abbreviations: FFPE, formalin-fixed,<br />
paraffin-embedded; CGH, comparative genomic hybridization; SNP, single nucleotide<br />
polymorphisms; LOH, loss <strong>of</strong> heterozygosity; DASL, cDNA-mediated annealing, selection,<br />
extension, and ligation.<br />
<<br />
locus-specific sequence, an address sequence in the middle corresponding to a complementary<br />
address sequence on the bead array, and a universal PCR priming site (P3) at the<br />
3 0 -end. The pooled assay oligonucleotides are first annealed to bisulfite-converted<br />
genomic DNA. An allele-specific primer extension step is then carried out using ASOs<br />
that are extended only if their 3 0 -base is complementary to their cognate CpG site in the<br />
genomic DNA template. Allele-specific extension is followed by ligation <strong>of</strong> the extended<br />
ASOs to their corresponding LSOs to create PCR templates. Common primers P1, P2, and<br />
P3 are then used to amplify the ligated products, which are subsequently hybridized to a<br />
microarray bearing the complementary address sequences. Abbreviations: ASO, allelespecific<br />
oligonucleotide; LSO, locus-specific oligonucleotide; PCR, polymerase chain<br />
reaction.
254 Abramovitz and Leyland-Jones<br />
subjected to a panoply <strong>of</strong> analysis platforms that can be used to detect CNAs,<br />
analyze SNPs, and investigate methylation aberrations, regulated gene expression,<br />
and signaling pathway changes, as well as miRNA alterations. Data derived from<br />
tumor tissue analyses can be used to further our understanding <strong>of</strong> the different<br />
subtypes, which could lead to new insights into breast cancer etiology, prediction<br />
<strong>of</strong> disease outcome, response to therapy, and ultimately to better and personalized<br />
patient management.<br />
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18<br />
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their<br />
Niche: Lethal Seeds in Lethal Soil<br />
Danuta Balicki<br />
Department <strong>of</strong> Medicine and Research Centre, Centre Hospitalier de<br />
l’Universite´ de Montre´al; Department <strong>of</strong> Medicine, Universite´ de Montre´al;<br />
and <strong>Breast</strong> <strong>Cancer</strong> Prevention and Genetics and <strong>Breast</strong> <strong>Cancer</strong> Risk<br />
Assessment Clinic, Ville Marie Multidisciplinary <strong>Breast</strong> <strong>Cancer</strong> Centre,<br />
Montre´al, Que´bec, Canada<br />
Brian Leyland-Jones<br />
Winship <strong>Cancer</strong> Institute, Emory University School <strong>of</strong> Medicine,<br />
Atlanta, Georgia, U.S.A.<br />
Max S. Wicha<br />
Comprehensive <strong>Cancer</strong> Centre, Department <strong>of</strong> Internal Medicine, Division <strong>of</strong><br />
Hematology-Oncology, University <strong>of</strong> Michigan, Ann Arbor, Michigan, U.S.A.<br />
INTRODUCTION<br />
The quest for optimal breast cancer therapy has been thwarted by recurrent<br />
treatment failures. As a result, the necessity has emerged to concomitantly rethink<br />
the fundamental basis <strong>of</strong> breast cancer, and to ensure that it is adequately and<br />
optimally targeted by therapeutic agents, and combinations <strong>of</strong> these agents.<br />
Adding to the complexity <strong>of</strong> this disease is the heterogeneity <strong>of</strong> breast cancer, and<br />
the realization that breast cancer is not a single disease, but a family <strong>of</strong> diseases.<br />
The daunting challenge that we face with a newly diagnosed breast cancer patient<br />
is one-to-one communication that tailors an individualized treatment strategy<br />
259
260 Balicki et al.<br />
based on the corresponding breast cancer pathophysiology. At the same time, we<br />
are mandated to address the urgency <strong>of</strong> this therapy on the basis <strong>of</strong> the underlying<br />
clinical assessment, the risks <strong>of</strong> metastases and death, as well as the benefits and<br />
adverse effects associated with therapeutic regimens to ensure that an enlightened<br />
decision among available therapeutic options can be mutually reached with an<br />
individual patient. As our understanding <strong>of</strong> the pathophysiology <strong>of</strong> breast cancer is<br />
incomplete, it is critical to channel the information that we glean from observations<br />
that deviate from our current models into a unified paradigm shift where therapeutic<br />
targets are validated, and subsequently attacked and destroyed. Given the<br />
complexity <strong>of</strong> breast cancer, the highest level <strong>of</strong> therapeutic success is most<br />
likely to be achieved when the uniqueness <strong>of</strong> an individual patient’s breast<br />
cancer is matched with individualized, combined, and targeted therapy.<br />
CANCER STEM CELL HYPOTHESIS<br />
The cancer stem cell hypothesis is a major player in the current paradigm shift in<br />
oncology (1,2). <strong>Cancer</strong> is, in essence, a genetic disease where three groups <strong>of</strong> genes<br />
are responsible for tumor progression, namely oncogenes, tumor suppressor genes,<br />
and stability genes (3,4). <strong>Cancer</strong> is a multistage process involving the accumulation<br />
<strong>of</strong> genetic and epigenetic changes in genes involved in the regulation <strong>of</strong> cell<br />
growth, DNA repair, and metastasis. In the traditional stochastic model, the vast<br />
majority <strong>of</strong> cancer cells can proliferate extensively to form new tumors. In contrast,<br />
the cancer stem cell model suggests that only rare cancer stem cells have the<br />
intrinsic ability to proliferate extensively to form new tumors (Fig. 1) (1). Clinical<br />
observations indicate that breast cancer responses to therapy do not necessarily<br />
correlate with patient survival. Tumor-initiating cells with the concomitant stem<br />
cell properties <strong>of</strong> self-renewal and successive maturation <strong>of</strong> progenitor cells into<br />
highly differentiated cells are referred to as “cancer stem cells” (Fig. 2).<br />
The cancer stem cell hypothesis provides an explanation for our clinical<br />
observations, including those which deviate from the stochastic model, by suggesting<br />
that therapies that kill cancer cells but not cancer stem cells may lead to tumor<br />
regression, but that the disease will subsequently recur as the cancer stem cells act as<br />
lethal seeds to repopulate the tumor (5). Thus, tumor regression in itself may represent<br />
an incomplete clinical end point, as it reflects cell death limited to differentiated tumor<br />
cells, while sparing cancer stem cells (Fig. 3). The “dandelion hypothesis” has been<br />
used to illustrate this pattern <strong>of</strong> clinical activity as it is analogous to cutting <strong>of</strong>f a<br />
dandelion (or other weed) at ground level, which may initially appear to produce the<br />
desired effect, but only the elimination <strong>of</strong> the root will prevent the weed from<br />
regrowing (6). Therefore, effective therapies should target cancer stem cells, with<br />
limited effects on normal cells. In the presence <strong>of</strong> therapies that kill cancer stem<br />
cells, tumors are predicted to lose their ability to generate new cancer cells,<br />
subsequently degenerate, leading to complete tumor eradication.<br />
Stem cells are multipotent with high proliferative potential and with<br />
concomitant properties <strong>of</strong> self-renewal and differentiation into multilineage
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 261<br />
Figure 1 A comparison <strong>of</strong> the stochastic model where most cancer cells can proliferate<br />
extensively to give rise to new tumors with the cancer stem cell model where only a few<br />
cancer stem cells can give rise to new tumors. Abbreviation: CSC, cancer stem cell.<br />
cells. Recent findings suggest that stem cell properties are integral and fundamental<br />
to the formation and perpetuation <strong>of</strong> human cancers, including breast<br />
cancer (7). The quiescence, self-renewal, and differentiation <strong>of</strong> stem cells are<br />
regulated by both intrinsic and extrinsic mechanisms. Intrinsic mechanisms<br />
include those governing the epigenetic state <strong>of</strong> stem cells, for example, hematopoietic<br />
stem cells are controlled by chromatin remodelers such as polycomb<br />
group proteins (8,9). The extrinsic mechanisms include changes in stem cell<br />
fate that are dictated by the environment, i.e., the stem cell niche. As our tissues<br />
are organized into stem cell hierarchies ranging from stem cells with extensive<br />
proliferative and self-renewal capacity to mature cells with little or no capacity<br />
for cell division, it is likely that cancer cell populations might also be organized<br />
in stem cell hierarchies, ranging from a small number <strong>of</strong> cells that are responsible<br />
for fueling the uncontrolled growth <strong>of</strong> the tumor cells to a large array <strong>of</strong> differentiated<br />
daughter cells (10). While the vast majority <strong>of</strong> cells in a tumor are<br />
merely the nontumorigenic daughter cells <strong>of</strong> cancer stem cells, the highly proliferative<br />
capacities <strong>of</strong> stem cells may drive the continued expansion <strong>of</strong> malignant<br />
cells. As cellular proliferation decreases with differentiation, the long-lived<br />
and slowly dividing stem and progenitor cells are likely candidates for the<br />
accumulation <strong>of</strong> mutations associated with carcinogenesis (11,12).
262 Balicki et al.<br />
Figure 2 NSCs and CSCs have the capacity <strong>of</strong> self-renewal and differentiation into<br />
progenitor cells. NSCs divide asymmetrically giving rise to a stem cell and a differentiated<br />
progenitor cell. CSCs may lose asymmetric division, leading to selectively selfrenewal,<br />
thereby increasing the number <strong>of</strong> CSCs and the subsequent tumor burden.<br />
Abbreviations: NSCs, normal stem cells; CSCs, cancer stem cells.<br />
The multilineage differentiation <strong>of</strong> cancer stem cells may contribute to the<br />
heterogeneity <strong>of</strong> certain tumors, including breast cancer. The parallels between<br />
normal stem cells and cancer stem cells suggest that some cancer stem cells<br />
arise from mutated normal stem cells, and are responsible for the proliferative<br />
capacity <strong>of</strong> cancers (Fig. 2) (7). The observation that stem and cancer cells share<br />
certain signaling pathways that regulate their self-renewal suggests that normal<br />
stem cells can, in fact, give rise to cancer cells (8). Mutated progenitor cells<br />
which acquire self-renewal properties may also become cancer stem cells<br />
(Fig. 2) (7,13). Epigenetic gene silencing and associated promoter CpG island<br />
DNA hypermethylation may precede genetic changes in premalignant cells and<br />
foster the accumulation <strong>of</strong> additional genetic and epigenetic hits that contribute
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 263<br />
Figure 3 Therapies which kill cancer cells, but not CSCs lead to an initial tumor<br />
regression, but as the CSCs are not eliminated, the tumor will regenerate. In contrast,<br />
therapies that target CSCs also eliminate the genesis <strong>of</strong> new tumor cells and result in<br />
tumor regression. Abbreviation: CSCs, cancer stem cells.<br />
to the generation <strong>of</strong> cancer stem cells from stem or early progenitor cells (14).<br />
The accumulation <strong>of</strong> mutations in cancer stem cells may disrupt the tight control<br />
<strong>of</strong> normal stem cells, leading to the dysregulation <strong>of</strong> self-renewal, tumorigenesis,<br />
loss <strong>of</strong> asymmetric division, and aberrant differentiation (Fig. 2) (12,15). The<br />
fate <strong>of</strong> stem cells, including self-renewal and differentiation, may also be programmed<br />
by the stem cell niche, i.e., the cellular microenvironment providing<br />
necessary support and stimuli to sustain self-renewal and other environmental<br />
factors (11,13,16–19). In normal breast development, mammary stem cells differentiate<br />
into myoepithelial, alveolar epithelial, and ductal epithelial cells (20).<br />
<strong>Cancer</strong> stem cells may acquire additional features associated with tumor progression,<br />
metastases, and therapeutic failure, including genetic instability and<br />
drug resistance (11). Mechanisms <strong>of</strong> therapeutic resistance include cellular<br />
quiescence and the acquisition <strong>of</strong> protein expression responsible for the cellular<br />
efflux <strong>of</strong> chemotherapeutic agents. The elimination <strong>of</strong> the cancer stem cell<br />
compartment <strong>of</strong> a tumor may be necessary to achieve stable, long-lasting cancer<br />
remissions. Therapeutic strategies designed to eradicate the cancer stem
264 Balicki et al.<br />
compartment should selectively, or at least preferentially, target the self-renewal,<br />
survival, and proliferative pathways specific to cancer stem cells, or induce<br />
differentiation. Thus, advances in our knowledge <strong>of</strong> stem cell properties are<br />
pivotal to the specific and effective targeting <strong>of</strong> these cells in therapeutic<br />
strategies, including cancer therapies.<br />
CURRENT STATUS OF BREAST STEM CELL RESEARCH<br />
The human mammary gland is organized during development as a hierarchy <strong>of</strong><br />
stem and progenitor cells that are successively limited in their multilineage<br />
potential and proliferative capacities. Stem cells represent approximately 1 in<br />
250 epithelial cells <strong>of</strong> the normal human breast (17). The transplantation <strong>of</strong><br />
mammary cells into cleared murine fat pads is a functional assay used to identify<br />
mammary epithelial cells with stem cell properties based on their ability to<br />
generate tumors (21,22). The existence <strong>of</strong> a stem cell origin <strong>of</strong> murine mammary<br />
development was established using transplantation <strong>of</strong> cells at limiting dilutions.<br />
These experiments confirmed the clonal nature <strong>of</strong> the regenerated alveolar,<br />
ductal, lobular, and more complex structures (22–24). The unequivocal demonstration<br />
<strong>of</strong> the existence <strong>of</strong> a murine mammary stem cell occurred with the<br />
reconstitution <strong>of</strong> a complete and functional mammary gland from a single lineage<br />
negative (Lin )/cluster <strong>of</strong> differentiation (CD)24 þ /CD29 þ cell, marked<br />
with a b-galactosidase (LacZ) transgene (25).<br />
Three types <strong>of</strong> murine mammary epithelial cell progenitors have been<br />
identified thus far (22–24). The first is a bipotent progenitor with features <strong>of</strong><br />
both luminal and myoepithelial characteristics, the second has luminal features,<br />
and the third has myoepithelial features. Analogous human studies <strong>of</strong> stem and<br />
progenitor cells have been limited by the challenges associated with the development<br />
<strong>of</strong> suitable in vivo xenotransplantation assays. Alternative strategies<br />
have been undertaken to assay human breast stem cells and to optimize conditions<br />
that support the in vitro growth and differentiation <strong>of</strong> primitive human epithelial<br />
cells. These include the proliferation <strong>of</strong> nonadherent mammospheres in suspension<br />
from human breast cancer tissues (17,26) and murine mammary fat pads<br />
(27). These mammospheres are enriched in stem and progenitor cells, as illustrated<br />
by their ability to differentiate along all three mammary epithelial lineages<br />
and to clonally generate complex functional structures in reconstituted threedimensional<br />
culture systems (17). Additional studies to characterize stem and<br />
progenitor cells include the identification <strong>of</strong> side populations using flow cytometry,<br />
i.e., low Hoechst 33342 dye-retaining populations (28–30). Side populations<br />
are enriched 30-fold in mammospheres derived from human tissues (17).<br />
The use <strong>of</strong> mammosphere assays subsequently lead to the discovery that<br />
the Notch signaling pathway plays a pivotal role in normal human mammary<br />
development through its activity in both stem and progenitor cells, concurrently<br />
affecting self-renewal and lineage-specific differentiation. As a result, it was<br />
proposed that abnormal Notch signaling contributes to mammary carcinogenesis
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 265<br />
by dysregulating the self-renewal <strong>of</strong> normal mammary stem cells (26). Additional<br />
pathways thought to be involved in stem cell self-renewal include<br />
hedgehog (HH), Bmi-1, wingless (Wnt/b-catenin) and phosphatase and tensin<br />
homolog deleted on chromosome ten (PTEN) (11,20,31).<br />
Further studies in human breast stem/progenitor cells identified a subset <strong>of</strong><br />
human breast cancer cells using flow cytometry with the phenotype CD44 þ /<br />
CD24 /Lin that possess highly tumorigenic properties in a nonobese diabetic/<br />
severe combined immunodeficiency (NOD/SCID) xenograft model (21). As few<br />
as 200 cells with the epithelial-specific antigen (ESA) þ , CD44 þ /CD24 /Lin<br />
phenotype consistently generated tumors in mice, whereas even 20,000 cells<br />
with alternative phenotypes were ineffective. The isolation and serial passage <strong>of</strong><br />
the tumorigenic CD44 þ /CD24 /Lin cell population in NOD/SCID mice gave<br />
rise to additional tumorigenic CD44 þ /CD24 /Lin cells, as well as phenotypically<br />
diverse nontumorigenic cells, that composed the bulk <strong>of</strong> the tumors,<br />
thereby confirming the self-renewal and differentiating capacities <strong>of</strong> these cells.<br />
While the unequivocal demonstration <strong>of</strong> stem cell characteristics necessitates<br />
model systems capable <strong>of</strong> tumor generation from a single cell, a population <strong>of</strong><br />
CD44 þ /CD24 /Lin cells appears to exhibit properties <strong>of</strong> human breast stem/<br />
progenitor cells in NOD/SCID mice.<br />
GENETIC SIGNATURES AND POLYMORPHISMS WHICH<br />
MAY INFLUENCE BREAST CANCER DEVELOPMENT<br />
AND TREATMENT OUTCOMES<br />
The study <strong>of</strong> molecular breast cancer portraits identifies patterns <strong>of</strong> molecular<br />
breast cancer markers using gene expression microarrays, which subsequently<br />
are useful in the selection <strong>of</strong> therapy. These portraits also seek to overcome<br />
potential differences in drug sensitivity or target expression between tumorinitiating<br />
cells and the more frequent nontumorigenic cells (32). The rationale for<br />
the pursuit <strong>of</strong> new drug discovery based on molecular targets results from the<br />
significant increment <strong>of</strong> the effectiveness <strong>of</strong> these drugs as compared with<br />
conventional chemotherapy. A recent systematic analysis <strong>of</strong> the consensus <strong>of</strong><br />
human breast and colorectal cancers included the analysis <strong>of</strong> 13,023 genes in<br />
11 breast and 11 colorectal cancers, and revealed that individual tumors accumulate<br />
an average <strong>of</strong> approximately 90 mutated genes, but that only a subset <strong>of</strong><br />
these contribute to the neoplastic process (33). Using stringent criteria, 189 genes<br />
(average <strong>of</strong> 11 per tumor) were found to be mutated at significant frequencies.<br />
The vast majority <strong>of</strong> these genes were not known to be genetically altered in<br />
tumors and are predicted to affect a wide range <strong>of</strong> cellular functions, including<br />
transcription, adhesion, and invasion. The genetic landscape <strong>of</strong> breast and colorectal<br />
cancer defined in this study provides new targets for diagnostic and<br />
therapeutic intervention.<br />
Gene signatures <strong>of</strong> tumor invasiveness in tumorigenic CD44 þ CD24 /low<br />
breast cancer cell populations correlate with overall and metastasis-free survival,
266 Balicki et al.<br />
independently <strong>of</strong> established clinical and pathological variables (34).<br />
CD44 þ CD24 /low breast cancer cells were also shown to increase from a median<br />
<strong>of</strong> 4.8% to 14.8% after chemotherapy in paired breast cancer biopsies from<br />
35 patients obtained before and after 12 weeks <strong>of</strong> neoadjuvant chemotherapy.<br />
Microarray analyses <strong>of</strong> CD44 þ CD24 /low breast cancer cells using the Affymetrix<br />
U133 GeneChip demonstrated an increased expression <strong>of</strong> self-renewal pathways.<br />
An increase in mammosphere formation <strong>of</strong> residual tumors was also observed<br />
postchemotherapy. Taken together, these results suggest that therapy resistant<br />
cancer stem cells may exist in the neoadjuvant setting <strong>of</strong> breast cancer therapy, and<br />
that breast cancer patients may benefit from the therapeutic targeting designed to<br />
eradicate these cells (35).<br />
In addition to chemoresistance, progenitor cells may also be resistant to<br />
radiation. This radioresistance has been shown in an animal model to be<br />
mediated at least in part by Wnt signaling, which has been implicated in stem<br />
cell survival (36). Radioresistance was investigated by treating primary BALB/c<br />
mouse mammary epithelial cells with clinically relevant doses <strong>of</strong> radiation. An<br />
enrichment in normal progenitor cells [stem cell antigen (SCA) 1-positive and<br />
side population progenitors] was identified.<br />
Specifically, radiation selectively enriched for progenitors in mammary<br />
epithelial cells isolated from transgenic mice with activated Wnt/b-catenin<br />
signaling but not for background-matched controls. Irradiated stem cell antigen<br />
1-positive cells had a selective increase in active b-catenin and survivin<br />
expression compared with SCA 1-negative cells. Radiation also induced enrichment<br />
<strong>of</strong> side population progenitors cells <strong>of</strong> the MCF-7 human breast adenocarcinoma<br />
cell line. In comparison to differentiated cells, progenitor cells have<br />
different cell survival properties that may facilitate the development <strong>of</strong> targeted<br />
antiprogenitor cell therapies.<br />
Closely intertwined with response prediction are an individual patient’s<br />
genetic polymorphisms, which may increase breast cancer susceptibility and<br />
influence the efficacy or safety <strong>of</strong> breast cancer treatments. The accumulation <strong>of</strong><br />
genetic factors that underlie breast cancer susceptibility or therapeutic escape<br />
may trigger the transformation <strong>of</strong> a normal stem or progenitor cell into a cancer<br />
stem cell. The variation <strong>of</strong> treatment responses between individuals also lies in<br />
differences in DNA sequences that alter the expression or function <strong>of</strong> proteins<br />
that are drug targets. These proteins are encoded by genes identified in early<br />
studies that are linked to highly penetrant, single-gene traits. Germline polymorphisms<br />
which confer susceptibility to breast cancer are associated with the<br />
highly penetrant breast cancer genes BRCA1 and BRCA2, p53, PTEN, ataxia<br />
telangiectasia mutated (ATM), and candidate BRCA3 genes (37). In addition,<br />
BRCA1 has been suggested to function as a breast stem cell regulator (38). Thus,<br />
BRCA1 may contribute to a conceptual link between hereditary and sporadic<br />
breast cancer. The loss <strong>of</strong> BRCA1 function may produce a block in cell differentiation<br />
and an increase in the self-renewal <strong>of</strong> mammary stem cells, resulting<br />
in the expansion <strong>of</strong> the stem cell pool. Taken together with its known function in
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 267<br />
DNA repair, BRCA1 may generate genetically unstable stem cells which are targets<br />
for additional carcinogenic events and the aggressive triple negative “basal” stem<br />
cell–like phenotype (39).<br />
Low-penetrance genes that may influence breast cancer susceptibility<br />
alone or in combination with environmental factors include genes in DNA repair,<br />
metabolic pathways, and estrogen-related pathways including HRAS1 minisatellite,<br />
CHEK2, XRCC1, XRCC2, RAD51, XRCC3, and LIG4 (37). Metabolic<br />
enzymes such as N-acetyltransferases (NAT) and gluthathione-S-transferases<br />
(GST) are involved in the metabolic inactivation <strong>of</strong> carcinogens. Mutations in<br />
these enzymes can result in increased toxicity and exposure to mutagenic substances,<br />
thereby increasing mutations, which contribute to carcinogenesis.<br />
Superoxide dismutases (SODs) are metalloenzymes that protect cells from free<br />
radicals, and their mutations can lead to increased breast cancer susceptibility<br />
(37). Cytochrome p450s (CYP) metabolically activate carcinogens into chemically<br />
reactive electrophiles. CYP1B1 is the most common form in the breast,<br />
and its alanine and serine (ALA/SER) polymorphism significantly increases<br />
breast cancer susceptibility (37). Additional targets <strong>of</strong> polymorphisms that<br />
increase breast cancer susceptibility include COMT, CYP17, CYP19, PPAR-g,<br />
TGF-b, HSP70-1, HSP70-2, and HSP70-HOM (37). Germline polymorphisms<br />
may also influence an individual’s susceptibility to the effects <strong>of</strong> breast cancer<br />
treatments (37). For example, dihydropyrimidine dehydrogenase (DPD) and<br />
thymidylate synthase (TS) are pivotal to the metabolism <strong>of</strong> 5-FU, and specific<br />
polymorphisms <strong>of</strong> these enzymes lead to 5-FU toxicity. Paclitaxel is metabolized<br />
by cytochrome P450, and specific CYP polymorphisms lead to defective metabolism.<br />
Additional breast cancer treatments with known polymorphisms, which<br />
may effect drug efficacy or safety include tamoxifen (ER, CYP2D6, SULT1A1),<br />
aromatase inhibitors (CYP19, CYP1A2, CYP2C9, CYP3A), cyclophosphamide<br />
(GST), methotrexate (MTHFR), doxorubicin (GST), and epirubicin (UGT2B7)<br />
(40). Future advances hinge on the more difficult challenge <strong>of</strong> elucidating multigene<br />
determinants <strong>of</strong> breast cancer susceptibility and drug responses through an<br />
intersection <strong>of</strong> genomics and medicine, which has the potential to yield a new set<br />
<strong>of</strong> molecular diagnostic tools that can be used to individualize and optimize drug<br />
therapy (41). Additional refinements can be accomplished through coupling in<br />
vitro drug sensitivity data with microarray data, to develop gene expression signatures<br />
that predict sensitivity to individual chemotherapeutic drugs (42). Thus, as<br />
we continue to fine-tune our stratification, we also approach our ultimate goal <strong>of</strong><br />
defining individual molecular breast cancer portraits.<br />
STEM CELL SANCTUARIES AS A MECHANISM<br />
OF THERAPEUTIC RESISTANCE<br />
Targeting <strong>of</strong> stem cells is particularly complex since this cellular population is<br />
largely quiescent. Thus, therapeutic agents directed against cycling cells are<br />
predictably ineffective in this population, which is essentially shielded from
268 Balicki et al.<br />
these drugs. <strong>Cancer</strong>s that respond to therapy initially may acquire drug resistance<br />
during the course <strong>of</strong> treatment, while other cancers appear to be intrinsically<br />
resistant. The cancer stem cell hypothesis suggests that in both <strong>of</strong> these cases, the<br />
resting cancer stem cell, which is both the cancer-initiating cell and its source<br />
<strong>of</strong> replenishment under selective pressure, has innate drug resistance by virtue <strong>of</strong><br />
its quiescent stem cell phenotype. Acquired drug resistance in more differentiated<br />
cancer cells may occur through gene amplification or rearrangement,<br />
thereby contributing to an aggressive phenotype. Through the accumulation <strong>of</strong><br />
mutations that distinguish cancer stem cells from their normal counterparts, these<br />
cells may acquire additional features associated with tumor progression,<br />
metastases, and therapeutic failure, including genetic instability, radioresistance,<br />
and chemoresistance (11,36). In the case <strong>of</strong> gliomas, cancer stem cells contribute<br />
to radioresistance through the preferential activation <strong>of</strong> the DNA damage<br />
checkpoint response and an increase in DNA repair capacity (43). Radioresistance<br />
may also result from a selective increase <strong>of</strong> cell survival pathways in<br />
progenitor cells, including b-catenin and survivin (36).<br />
Stem cell niches regulate stem cell proliferation and cell fate decisions, and<br />
they also play a protective role by shielding stem cells from environmental<br />
insults. In this manner, vascular niches might protect brain cancer stem cells<br />
from chemo- and radiotherapies, and the opportunity for these cells to reform a<br />
tumor mass in spite <strong>of</strong> an initial clinical response (44). With the acquisition <strong>of</strong><br />
properties <strong>of</strong> tumor progression and metastases, cancer stem cells may travel to<br />
sanctuary sites, such as across the blood-brain barrier, where it may be difficult<br />
to eradicate them, even with targeted therapies.<br />
The drug resistance <strong>of</strong> normal tissue stem cells is constitutively mediated<br />
by multidrug resistance (MDR) transporters and detoxifying enzymes (45). DNA<br />
repair mechanisms, resistance to apoptosis, and telomerase activity also contribute<br />
to the stability <strong>of</strong> normal tissue stem cells. Among the mechanisms<br />
responsible for stem cell drug resistance, there is mounting speculation that ABC<br />
transporters repress the maturation and differentiation <strong>of</strong> stem cells (46,47).<br />
However, since breast cancer resistance protein 1 (BCRP1) knockout mice<br />
display normal hematopoiesis instead <strong>of</strong> accelerated hematopoietic maturation,<br />
it is clear that multiple factors determine the maturation and differentiation <strong>of</strong><br />
stem cells (48). In addition, protection from hypoxia appears to be another<br />
function <strong>of</strong> ABC transporters. As a result, it has been postulated that Bcrp<br />
expression protects hematopoietic stem cells from hypoxic environments by<br />
preventing porphyrin accumulation that causes mitochondrial death (49).<br />
Thus, leading edge-targeted breast cancer therapies must overcome all <strong>of</strong><br />
the obstacles associated with cancer stem cell biology, including both the<br />
intrinsic and extrinsic properties <strong>of</strong> resistance, which define these enigmatic cells<br />
and the cloisters that keep them safe. From the cancer stem cell hypothesis, it<br />
follows that systemic administration <strong>of</strong> an efficacious MDR reversal agent would<br />
render tumor and normal tissue stem cells equally susceptible to the chemotherapeutic<br />
agents, <strong>of</strong>fering no net gain in therapeutic index (45). Thus, targeting
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 269<br />
the properties that distinguish cancer stem cells from their normal stem cell<br />
counterparts is necessary.<br />
CELLULAR BREAST CANCER STEM CELL TARGETS<br />
The identification <strong>of</strong> cancer stem cell markers is necessary for the isolation<br />
and purification <strong>of</strong> cancer stem cells as they are potential targets <strong>of</strong> novel drug<br />
discovery and development. The functional targets that define stem cells are<br />
their abilities to self-renew and differentiate using serial passages in xenotransplantation<br />
models. The current breast cancer stem cell hypothesis suggests that<br />
the most primitive human breast stem cell does not express the estrogen receptor<br />
(ER) while more mature progenitors may or may not express ER depending on<br />
their location in the differentiation pathway (12). A recent study using reverse<br />
transcriptase polymerase chain reaction (RT-PCR) and immunohistochemical<br />
analyses established that mouse mammary stem cells were negative for both ERa<br />
and the progesterone receptor (50). An independent study further supported this<br />
result by showing that ER-positive cells retained very little stem cell activity (51).<br />
Throughout much <strong>of</strong> mammary development during embryogenesis, the developing<br />
gland consists <strong>of</strong> a simple duct system, with a primitive mammary bud, the<br />
growing terminal end <strong>of</strong> the duct, with no ERa expression until the 30th week <strong>of</strong><br />
gestation. These structures are thought to be made <strong>of</strong> primitive ductal and<br />
myoepithelial progenitors. Later in gestation, ERa-positive cells are present in<br />
the growing terminal ends <strong>of</strong> these ducts, followed by a surge <strong>of</strong> ERa-positive<br />
cells in this region after birth (12). In the developing mammary gland, insulin<br />
growth factor-1 (IGF-1) is required to form ducts and terminal end buds, indicating<br />
its critical role in stimulating the proliferation and differentiation <strong>of</strong><br />
primitive breast epithelium (12).<br />
ER-positive progenitors arise after 30 weeks <strong>of</strong> gestation. They respond to<br />
estrogen stimulation by proliferating and generating ductal epithelial cells and<br />
alveolar cells that form the adult mammary tree. Maintenance <strong>of</strong> the myoepithelial<br />
progenitor pool and the stem cell compartment may rely on paracrine<br />
signaling initiated by the ER-positive progenitor cells. <strong>Breast</strong> carcinogenesis<br />
results from mutations affecting stem and progenitor cells. Thus, the phenotype<br />
<strong>of</strong> different subtypes <strong>of</strong> breast cancer is determined both by the cell <strong>of</strong> origin and<br />
the particular mutations driving carcinogenesis. Thus, ER-negative tumors arise<br />
from stem cells or early ER-negative progenitor cells, while ER-positive tumors<br />
arise from either ER-negative or ER-positive progenitors (12).<br />
On the basis <strong>of</strong> these observations, a breast tumor model has been proposed<br />
consisting <strong>of</strong> three types <strong>of</strong> tumors. The first is an ER-negative tumor termed<br />
type 1, which arises from the most primitive ER-negative stem or early progenitor<br />
cells. Using classic histopathology and molecular pr<strong>of</strong>iling studies, these<br />
tumors are poorly differentiated and display a basal phenotype with expression<br />
markers <strong>of</strong> both luminal epithelial and myoepithelial cells. Clinically, these<br />
tumors are highly aggressive and are associated with a poor prognosis. Type 2
270 Balicki et al.<br />
tumors also arise from early ER-negative stem or early progenitor cells. However,<br />
this group includes stem cell mutations, which allow for the differentiation<br />
<strong>of</strong> a specific subset <strong>of</strong> tumor cells into ER-positive cells, which may range from a<br />
low percentage (6–10%) similar to that seen in normal tissue to a high percentage<br />
(70–100%). Thus, these tumors are heterogeneous, with intermediate differentiation<br />
and prognosis. Type 3 tumors arise from the transformation <strong>of</strong> ER-positive<br />
progenitors. They are predicted to exclusively express luminal markers and have<br />
the best prognosis as they contain differentiated cells and respond to antiestrogen<br />
therapy (12). Additional breast stem cell markers include CD44, CD24, aldehyde<br />
dehydrogenase (ALDH), Oct-4, Bmi-1, and Wnt/b-catenin.<br />
As discussed above, a small population <strong>of</strong> cancer cells characterized by<br />
CD44 expression but low or undetectable levels <strong>of</strong> CD24 (CD44 þ CD24 /low )<br />
have a high tumorigenic capacity when injected into immunodeficient mice (52).<br />
In contrast, the rest <strong>of</strong> the cancer cells, called nontumorigenic breast cancer cells,<br />
have little or no tumorigenic ability. Tumors in mice that originate from purified<br />
tumorigenic breast cancer cells contain a mixture <strong>of</strong> both tumorigenic and<br />
nontumorigenic breast cancer cells, which confirms that the CD44 þ CD24 /low<br />
population shares the capacity for self-renewal with normal stem cells. Gene<br />
expression pr<strong>of</strong>iling <strong>of</strong> tumorigenic breast cancer cells was utilized to develop<br />
prognostic tools to assess survival in patients with breast cancer and to predict<br />
clinical outcomes in breast cancer (34). An invasiveness gene signature <strong>of</strong> 186<br />
genes was developed to predict the likelihood <strong>of</strong> a tumor to metastasize. This<br />
invasiveness gene signature is associated with the risk <strong>of</strong> death and metastasis<br />
not only in breast cancer but also in lung cancer, prostate cancer, and medulloblastoma.<br />
These results suggest the critical relevance <strong>of</strong> the CD44 þ CD24 /low<br />
tumorigenic subclass <strong>of</strong> breast cancer cells as a pivotal contributor to the association<br />
<strong>of</strong> the invasiveness signature to clinical outcome. In addition, CD44, a<br />
cell-surface receptor that mediates cell–cell contacts, promotes the engraftment<br />
<strong>of</strong> leukemic stem cells in the bone marrow and may thus have an additional role<br />
as a critical component <strong>of</strong> the cancer stem cell niche (53).<br />
As a result <strong>of</strong> the growing interest <strong>of</strong> identifying new markers for identifying<br />
and isolating stem cells, assays based on ALDH activity have been proposed<br />
to be a promising alternative in both murine and human mammary stem<br />
cells. ALDH is a family <strong>of</strong> cytosolic enzyme is<strong>of</strong>orms responsible for oxidizing<br />
intracellular aldehydes, including vitamin A, to carboxylic acids. It is highly<br />
expressed in hematopoietic progenitors, in intestinal crypt cells, as well as in<br />
breast tumor cells (54–56). ALDH1 or (ALDH isoenzyme 1 or ALDH1A1) has<br />
been shown to be responsible for the resistance observed in hematopoietic stem<br />
cells and breast cancer tumor cells to the alkylating agent, cyclophosphamide<br />
(56–58). A novel system has been designed to detect ALDH using a visible light<br />
excitable fluorochrome named Aldefluor 1 (BODIPY-conjugated aminoacetaldehyde,<br />
BAAA) metabolized by both murine and human cells (59).<br />
Fluorescence-activated cell sorter (FACS) <strong>of</strong> the normal breast cell population<br />
revealed that 6% <strong>of</strong> this population is ALDH-positive (60). In breast cancer cell
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 271<br />
lines, ALDH-positive cells were more frequently observed in basal-like than in<br />
luminal cell lines. ALDH-positive cells form mammospheres at a high efficiency<br />
and are tumorigenic in NOD/SCID mice (61).<br />
A recent study showed that when acute myeloid leukemic (AML) patient<br />
cells were sorted on the basis <strong>of</strong> Aldefluor staining, three distinct possibilities<br />
were observed (62). High levels <strong>of</strong> aldehyde dehydrogenase (ALDH hi ) cells from<br />
a first group <strong>of</strong> patients were identified as normal nonleukemic stem cells, were<br />
present in low number, and were able to reconstitute a complete hematopoietic<br />
system when transplanted into NOD/SCID mice. However, a second group <strong>of</strong><br />
patients had ALDH hi cells displaying a higher side scattering pattern than normal<br />
stem cells and these cells were also significantly more abundant. These cells<br />
correspond to leukemic stem cells. Finally, a third group <strong>of</strong> patients showed no<br />
ALDH hi cells. This study demonstrates that FACS analyses based on the first<br />
ALDH isozyme (ALDH1 or ALDHA1) activity can be used to identify and<br />
isolate leukemic stem cells (58). In contrast to the toxicity associated with<br />
Hoechst 33342, ALDH is relatively mild and allows for cell sorting using viable<br />
cell populations, enriched in stem cells. The availability <strong>of</strong> antibodies derived<br />
against ALDH1 allows for the study <strong>of</strong> their expression in fixed tissue samples<br />
using immunohistochemistry (Fig. 4). Immunohistochemistry <strong>of</strong> 577 human<br />
breast carcinoma specimens from two different sources detected 24% and 30%<br />
ALDH-positive cells, respectively. In this study, ALDH expression was strongly<br />
correlated with tumor size, grade, ER status, human epidermal growth factor<br />
receptor 2 (HER2) overexpression, and poor prognosis (60).<br />
OCT-4 is an additional cancer stem cell marker. The OCT-4 oncogene<br />
encodes a nuclear protein that belongs to a family <strong>of</strong> transcription factors containing<br />
the POU DNA–binding domain that plays an important role in maintaining<br />
the pluripotent state <strong>of</strong> embryonic stem cells (63). It may prevent expression <strong>of</strong><br />
genes activated during differentiation, and its expression is downregulated during<br />
differentiation. OCT-4 is normally found in the pluripotent stem cells <strong>of</strong> pregastrulation<br />
embryos, including oocytes, early cleavage-stage embryos, and the inner<br />
cell mass <strong>of</strong> the blastocyst, and knockout <strong>of</strong> OCT-4 causes early lethality in mice<br />
because <strong>of</strong> the absence <strong>of</strong> an inner cell mass (63). Thus, these activities associated<br />
with OCT-4 suggest that it plays a pivotal role in mammalian development and in<br />
the self-renewal <strong>of</strong> embryonic stem cells. The expression <strong>of</strong> genes such as OCT-4<br />
is potentially correlated with tumorigenesis and may affect some aspects <strong>of</strong> tumor<br />
behavior, such as tumor recurrence or resistance to therapies. OCT-4 is expressed<br />
in bladder cancer, as well as in CD44 þ /CD24 breast cancer–initiating cells<br />
(64,65). It may serve as a multifunctional factor involved in major biological<br />
processes such as embryonic development, control <strong>of</strong> differentiation, and stem<br />
cell–based carcinogenesis (64).<br />
Bmi-1 is a cancer stem cell marker that belongs to the polycomb group<br />
family <strong>of</strong> proteins, and was first identified as a c-myc cooperating oncogene in<br />
murine lymphomagenesis. Bmi-1 is involved in the regulation <strong>of</strong> diverse biological<br />
processes involved in X chromosome inactivation, repression <strong>of</strong> cellular
272 Balicki et al.<br />
Figure 4 ALDH1 immunostaining. Immunohistochemistry using anti-ALDH1 on tissue<br />
microarray samples from two patients (A, B) with infiltrating ductal carcinoma <strong>of</strong> the breast,<br />
both magnified 200, with a scale bar <strong>of</strong> 100 mm. Abbreviation: ALDH1, aldehyde dehydrogenase<br />
isoenzyme 1.<br />
senescence, carcinogenesis, and stem cell renewal (66). Bmi-1 oncoprotein<br />
overexpression correlates with axillary lymph node metastases in invasive ductal<br />
breast cancer (67). In addition, the Bmi-1 oncogene induces telomerase activity<br />
and immortalizes human mammary epithelial cells (68). Bmi-1 regulates selfrenewal<br />
<strong>of</strong> normal and malignant human mammary stem cells, stimulates<br />
mammosphere formation and colony size in vitro, and increases ductal/alveolar<br />
development in humanized NOD/SCID mammary fatpads (20).<br />
The final stem cell marker in this chapter is wingless (Wnt)/b-catenin. The<br />
canonical Wnt/b-catenin signaling pathway modulates the delicate balance<br />
between stemness, differentiation, and tumorigenesis in several adult stem cell
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 273<br />
niches such as the hair follicles in the skin, the mammary gland, hematopoiesis,<br />
and the intestinal crypt (8,69). Differences in the levels <strong>of</strong> Wnt signaling activity<br />
reflect tumor heterogeneity and are likely to account for distinct cellular activities<br />
such as proliferation and epithelial-mesenchymal transitions, which prompt<br />
tumor growth and malignant behavior, respectively. This pivotal pathway is<br />
highly conserved in evolution and is known to regulate cell-fate decisions, cell<br />
proliferation, morphology, migration, apoptosis, differentiation, and stem cell<br />
self-renewal. Recent evidence which suggests that Wnt signaling plays a role in<br />
human breast cancer include the detection <strong>of</strong> elevated levels <strong>of</strong> nuclear and/or<br />
cytoplasmic b-catenin using immunohistochemistry, and the overexpression or<br />
downregulation <strong>of</strong> specific Wnt proteins. The Wnt pathway has also been<br />
implicated in normal stem cell self-renewal in vivo, and there is evidence that<br />
dysregulation <strong>of</strong> this pathway in the mammary gland and other organs may play<br />
a key role in carcinogenesis (70).<br />
The Wnt family <strong>of</strong> secreted proteins includes the well-characterized<br />
canonical Wnt signaling pathway, in which Wnt ligands signal through the<br />
stabilization <strong>of</strong> b-catenin. In this pathway, Wnt proteins bind to a family <strong>of</strong><br />
frizzled receptors in a complex with the low-density lipoprotein receptor–related<br />
proteins 5 and 6 (LRP5/6) coreceptors to activate Dishevelled (Dsh). Subsequently,<br />
Dsh inhibits the activity <strong>of</strong> the b-catenin destruction complex [adenomatous<br />
polyposis coli (APC), axin, and glycogen synthase kinase-3b (GSK-<br />
3b)], which phosphorylates b-catenin in the absence <strong>of</strong> the ligands. As a result,<br />
b-catenin is stabilized and translocated to the nucleus, where it recruits transactivators<br />
to high mobility group (HMG)–box DNA-binding proteins <strong>of</strong> the<br />
lymphoid-enhancer factor/T-cell factor (LEF/TCF) family. In the absence <strong>of</strong><br />
Wnt signaling, b-catenin remains in the cytoplasm, where it forms the b-catenin<br />
destruction complex. GSK-3b phosphorylates b-catenin, which targets the protein<br />
for ubiquitin-mediated degradation. When the Wnt pathway is activated,<br />
GSK-3b is inhibited, blocking b-catenin phosphorylation and its subsequent<br />
degradation (71). In addition, several b-catenin-independent Wnt signaling<br />
pathways, known as noncanonical, have been shown to be crucial for different<br />
aspects <strong>of</strong> vertebrate embryo development (71).<br />
MOLECULAR BREAST CANCER STEM CELL TARGETS<br />
Molecular signatures <strong>of</strong> stem cells are currently being identified. The mutations that<br />
accumulate in cancer stem cells could inappropriately activate Bmi-1, Wnt/b-catenin,<br />
Notch, sonic hedgehog (SHH), and PTEN pathways that are involved in the regulation<br />
<strong>of</strong> self-renewal <strong>of</strong> normal stem cells and could potentially lead to cancer<br />
(Figs. 5 and 6) (26,72–74). These molecular markers can be exploited to purify<br />
cancer stem cells, which can then be characterized in the most intimate detail,<br />
by the modern tools <strong>of</strong> gene expression pr<strong>of</strong>iling and informatics. The failure to<br />
shut <strong>of</strong>f the self-renewal pathways could confer self-renewal on cells and microenvironments<br />
capable <strong>of</strong> evading homeostatic regulation, leading to tumorigenesis.
274 Balicki et al.<br />
Figure 5 Stem cell self-renewal pathways include the HH, Notch, Bmi-1, Wnt, and<br />
PTEN pathways. Dysregulation <strong>of</strong> the self-renewal pathways <strong>of</strong> NSCs and progenitor<br />
cells may lead to CSCs and tumor formation. NSCs are ER negative and they differentiate<br />
into ER-positive progenitor cells, which further differentiate into myoepithelial, alveolar<br />
epithelial, and ductal epithelial cells. CSCs in their niche may lose asymmetric division,<br />
leading to the clonal expansion <strong>of</strong> cancerous cells and tumor formation. Abbreviations:<br />
NSCs, normal stem cells; CSCs, cancer stem cells; ER, estrogen receptor.<br />
The intimate interrelationship <strong>of</strong> multiple pathways critical to tumorigenesis and<br />
to normal stem cell self-renewal raises the specter that cancer therapies targeting<br />
such pathways might ablate resident stem cells (75). Drugs that selectively target<br />
the self-renewal pathways <strong>of</strong> tumors will be attractive candidates for cancer<br />
therapies if they can effectively spare the normal bystander stem cells that use<br />
self-renewal as part <strong>of</strong> their regular functional repertoire. Alternatively,
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 275<br />
Figure 6 A model <strong>of</strong> the interrelationship <strong>of</strong> stem cell self-renewal pathways including a<br />
conceptual link between sporadic and hereditary breast cancers. BRCA1 may inhibit stem<br />
cell self-renewal, while HER2 overexpression may increase the stem cell pool through<br />
regulation <strong>of</strong> self-renewal. In this model, there is bidirectional cross talk between the<br />
Notch and HH pathways with subsequent regulation <strong>of</strong> the polycomb gene Bmi-1 and the<br />
Wnt/b-catenin pathway. SHH, IHH, and DHH are HH ligands, while Gli1 and 2 are two<br />
HH transcription factors that are positive mediator <strong>of</strong> HH signaling. Cylcopamine inhibits<br />
the HH pathway. The DSL peptide activates Notch signaling, while the GSI blocks the<br />
intramembrane cleavage <strong>of</strong> Notch required for signaling. PTEN affects Wnt signaling<br />
through GSK-3b. Exisulind, bromoindirubin-3 0 -oxime, and imatinib inhibit the Wnt/bcatenin<br />
pathway. Abbreviations: BRCA, breast cancer; HER, human epidermal growth<br />
factor receptor 2; HH, hedgehog; SHH, sonic hedgehog; IHH, Indian hedgehog; DHH,<br />
desert hedgehog; PTEN, phosphatase and tensin homolog deleted on chromosome ten;<br />
GSI, gamma secretase inhibitor.
276 Balicki et al.<br />
differentiating therapies may also be effective if they can efficiently shift the<br />
balance <strong>of</strong> stem cell divisions toward differentiation rather than self-renewal and<br />
stem cell expansion. In addition to Bmi-1, and Wnt/b-catenin discussed above,<br />
Notch, SHH pathways, and PTEN will be reviewed below.<br />
The Notch signaling pathway plays an important role in regulating proliferation,<br />
survival, and differentiation <strong>of</strong> many cell types, including the regulation<br />
<strong>of</strong> self-renewal <strong>of</strong> adult stem cells and differentiation <strong>of</strong> progenitor cells<br />
along a particular lineage. These outcomes include increased survival or death,<br />
proliferation or growth arrest, and commitment to or blockage <strong>of</strong> differentiation.<br />
There are four mammalian Notch homologues, Notch 1 to 4, which interact with<br />
the DSL ligands: Delta, Delta-like, Jagged1, and Jagged2 in vertebrates (71).<br />
Notch 1 and Notch 4 homologues are involved in the normal development<br />
<strong>of</strong> the mammary gland, and mouse mammary tumors are associated with their<br />
mutated forms. Notch signaling is critical to normal human mammary development<br />
where it acts on both stem cells and progenitor cells, affecting selfrenewal<br />
and lineage-specific differentiation. Notch signaling may also contribute<br />
to mammary carcinogenesis by dysregulating the self-renewal pathways <strong>of</strong><br />
normal mammary stem cells. There is bidirectional cross talk between the Notch<br />
and HH pathways with subsequent regulation <strong>of</strong> the polycomb gene Bmi-1, as<br />
well as the Wnt signaling network (71). The DSL ligands activate Notch signaling,<br />
while the gamma secretase inhibitor (GSI) blocks the intramembrane<br />
cleavage <strong>of</strong> Notch required for signaling (71). The abnormal expression <strong>of</strong> Notch<br />
receptors in epithelial metaplastic lesions and neoplastic lesions suggests that<br />
Notch may act as a protooncogene (26). In spite <strong>of</strong> the simplicity <strong>of</strong> the Notch<br />
signaling pathway, it yields varied outcomes in different contexts (76). These<br />
different outcomes are mediated through a novel signaling pathway in which<br />
Notch receptors on the cell surface are processed to give rise to a nuclear<br />
transcriptional activation complex. Activated Notch modulates stem and progenitor<br />
cell renewal through the upregulation <strong>of</strong> the cyclin-dependent kinase<br />
inhibitors p21 cip1 and p27 kip1 (77). Notch signaling is also required for the<br />
survival <strong>of</strong> niche cells (78). Notch receptor activation promotes the survival <strong>of</strong><br />
neural stem cells by inducing the expression <strong>of</strong> the specific target genes hairy and<br />
enhancer <strong>of</strong> split 3 (Hes3) and SHH. The underlying mechanism occurs through<br />
the rapid activation <strong>of</strong> cytoplasmic signals, including the serine/threonine kinase<br />
AKT, the transcription factor STAT3, and mammalian targets <strong>of</strong> rapamycin<br />
(mTOR) molecular targets <strong>of</strong> rapamycin (79).<br />
The HH signaling pathway has important roles in embryonic development<br />
and tumorigenesis (80). In a number <strong>of</strong> tumors, including prostatic and pancreatic<br />
cancers, HH signaling is abnormally upregulated, and effective signaling<br />
inhibition can repress tumor growth and induce apoptosis (81–83). The core<br />
components <strong>of</strong> the HH pathway are evolutionarily conserved from insects to<br />
mammals. They include the secreted ligand HH, the membrane receptor Patched<br />
(PTCH), the membrane signal transductor Smoothened (SMO), and the transcription<br />
factor Ci/glioma (GLI). However, while in Drosophila, there is only
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 277<br />
one secreted ligand (HH), one receptor (Ptc), and one transcription factor (Ci),<br />
there are three secreted ligands, sonic HH (SHH )/desert HH (DHH)/Indian HH<br />
(IHH), two receptors (PTCH1/2), and three transcription factors (GLI1/2/3) in<br />
mammals (81). The HH signaling components PTCH1 and GLI1/2 are highly<br />
expressed in normal mammary stem and progenitor cells, and activated HH signaling<br />
is mediated by Bmi-1 (30). Addition <strong>of</strong> Hh ligands increases the expression<br />
<strong>of</strong> the transcription factors Gli1 and Gli2, which are positive mediators <strong>of</strong> Hh<br />
signaling (20). SHH can potentiate tumor cell proliferation. Components <strong>of</strong> the<br />
SHH–GLI pathway are expressed in adult human prostate cancer, <strong>of</strong>ten with<br />
enhanced levels in tumors versus normal prostatic epithelia. Cyclopamine and<br />
anti-SHH antibodies inhibit the proliferation <strong>of</strong> GLI1–PSA primary prostate tumor<br />
cultures (83). The SHH signaling pathway also regulates adult neuronal selfrenewal,<br />
and may play a role in brain tumor biology (4).<br />
The final pathway discussed herein is PTEN, which is a phosphatase that<br />
inhibits cellular proliferation by negative regulation <strong>of</strong> signaling through the<br />
phosphatidylinositol-3-OH kinase (PI3K) pathway. The activation <strong>of</strong> PI3K leads<br />
to phosphorylation and activation <strong>of</strong> the Akt protein, which in turn can potentially<br />
phosphorylate a multitude <strong>of</strong> proteins (84). The PTEN–Akt pathway regulates<br />
stem cell activation by helping control nuclear localization <strong>of</strong> the Wnt<br />
pathway effector b-catenin. PTEN inhibits, whereas Akt enhances, the nuclear<br />
localization <strong>of</strong> b-catenin. Akt is thought to coordinate with Wnt signaling to<br />
assist in the activation <strong>of</strong> b-catenin in intestinal stem cells, either through<br />
phosphorylation <strong>of</strong> GSK-3b or by phosphorylation <strong>of</strong> b-catenin itself. The<br />
phosphorylation by Akt inactivates GSK-3b, helping the Wnt signal to permit<br />
b-catenin to escape proteasome-mediated degradation and facilitating its nuclear<br />
translocation and activity in transcriptional regulation (85).<br />
In addition to other downstream effectors, the highly branched PI3K<br />
pathway activates the mammalian target <strong>of</strong> rapamycin (mTOR). Molecules such<br />
as p21 Cip1/Waf1 are found downstream to PTEN, and they influence stem cell<br />
proliferation, without necessarily implicating self-renewal. FOXO regulatory<br />
proteins may have the effects <strong>of</strong> PTEN on p21 Cip1/Waf1 by influencing the<br />
expression <strong>of</strong> a range <strong>of</strong> crucial genes for stem cell function, including cell<br />
division, and tolerance <strong>of</strong> oxidative stress. Thus, the perturbation <strong>of</strong> PTEN may<br />
thus result in nonrenewing cell divisions or death <strong>of</strong> stem cells (84). PTEN has<br />
also been reported to be a guardian <strong>of</strong> genome integrity and <strong>of</strong> the maintenance<br />
<strong>of</strong> chromosomal stability through the physical interaction with centromeres and<br />
control <strong>of</strong> DNA repair. PTEN acts on chromatin and regulates the expression <strong>of</strong><br />
Rad51, which reduces the incidence <strong>of</strong> spontaneous double strand breaks (86).<br />
PTEN is commonly deleted or otherwise inactivated in diverse cancers,<br />
including hematopoietic malignancies, glioblastomas, endometrial carcinomas,<br />
breast carcinomas, and prostate carcinomas (86). PTEN deletion or HER2<br />
overexpression may modulate stem cell function leading to increased invasion<br />
and tumorigenicity (87). PTEN deletions are implicated in the generation <strong>of</strong><br />
transplantable leukemia-initiating cells, and the depletion <strong>of</strong> normal hematopoietic
278 Balicki et al.<br />
stem cells (88). PTEN normally maintains hematopoietic stem cells in a quiescent<br />
state, and in the absence <strong>of</strong> PTEN, hematopoietic stem cells are driven into the cell<br />
cycle, eventually leading to depletion <strong>of</strong> hematopoietic stem cell reserves. Thus,<br />
PTEN activity identifies a mechanistic difference between the maintenance <strong>of</strong><br />
normal stem cells and cancer stem cells. These effects were mostly mediated by<br />
mTOR as they are inhibited by rapamycin, which depletes leukemia-initiating<br />
cells but also restores normal hematopoietic stem cell function (88). This mechanistic<br />
distinction shall enable the design <strong>of</strong> therapies that target the PTEN<br />
pathway to combat leukemia with the goal <strong>of</strong> killing leukemic stem cells, without<br />
damaging the normal stem cell pool (75). In addition to leukemia, this pathway<br />
may be an attractive therapeutic target for treating a broad spectrum <strong>of</strong> tumors<br />
carrying inactivated PTEN because mTOR activity can be suppressed by a number<br />
<strong>of</strong> compounds including rapamycin (75). Genes that promote stem cell quiescence<br />
might be particularly amenable to such targeted strategies because although normal<br />
stem cells thrive in a quiescent world, forcing cancer stem cells to adopt<br />
quiescence could lead to their eradication (75).<br />
TARGETS OF THE STEM CELL NICHE<br />
While cancer stem cells may indeed be lethal seeds in themselves, they may<br />
whither and die unless they are supported by environmental factors, which<br />
perpetuate their survival and self-renewal. The stem cell niche provides this<br />
microenvironment for cancer stem cells, and the combination <strong>of</strong> lethal seeds in<br />
lethal soil is likely to lead to the most aggressive forms <strong>of</strong> breast cancer, with the<br />
most fatal course. The stem cell niche determines cell fate decisions based on<br />
surrounding cells, extracellular matrix components, and secreted local and systemic<br />
factors (11). The 1889 proposal by Stephen Paget that metastasis depends<br />
on cross talk between selected cancer cells (the “seeds”) and specific organ<br />
microenvironments (the “soil”) still holds forth today, and may also apply to the<br />
interrelationship between cancer stem cells and their niche (89–91). <strong>Cancer</strong><br />
metastasis requires the seeding, homing, and successful colonization <strong>of</strong> specialized<br />
cancer stem cells at distant sites (92). In this context, it is not surprising<br />
that the inhibition <strong>of</strong> the homing factor CXCR4 has been shown to effectively<br />
prevent both primary tumor formation as well as metastasis in animal models<br />
(92). Stem cells <strong>of</strong> various tissues exist within protective niches that are composed<br />
<strong>of</strong> differentiated cells which provide direct cell contacts and secreted<br />
factors that maintain stem cells primarily in a quiescent state by providing cues<br />
for the inhibition <strong>of</strong> proliferation (8,44). The mutations that distinguish cancer<br />
stem cells from normal stem cells may enable them to escape niche control.<br />
Alternatively, the niche provides proliferation or differentiation-inducing signals<br />
at times when high numbers <strong>of</strong> progenitors are needed and can quickly give rise<br />
to all committed cell lineages (8). As a result, the uncontrolled proliferation <strong>of</strong><br />
stem cells and tumorigenesis may be a consequence <strong>of</strong> the dysregulation <strong>of</strong><br />
extrinsic factors within the niche. In this case, the stem cell niche might represent
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 279<br />
targets for cancer therapy (44). Given that postnatal estrogen exposure is related to<br />
breast cancer risk, this effect could also apply in utero, where the developing fetus<br />
is exposed to estrogen levels up to 10-fold higher than at any other time in the<br />
individual’s lifetime. Under these circumstances, estrogens or other hormones may<br />
create the fertile soil that is responsible for an increase in the responsiveness <strong>of</strong><br />
estrogen-responsive tissue to undergo oncogenesis later in life (93).<br />
Epigenetic modulation <strong>of</strong> gene expression is highly abnormal in cancers,<br />
which <strong>of</strong>ten show aberrant promoter CpG island hypermethylation and transcriptional<br />
silencing <strong>of</strong> tumor suppressor genes and prodifferentiation factors.<br />
The aberrant epigenetic landscape <strong>of</strong> the cancer cell is also characterized by a<br />
massive genomic hypomethylation, an altered histone code for critical genes, and<br />
a global loss <strong>of</strong> monoacetylated and trimethylated histone H4 (94). In embryonic<br />
stem cells, these genes are maintained in a state that is ready for transcription<br />
mediated by a bivalent promoter chromatin pattern consisting <strong>of</strong> the repressor,<br />
histone H3 methylated at Lys27 (H3K27) by polycomb group proteins, plus the<br />
activator, methylated H3K4. However, in contrast to embryonic stem cells,<br />
embryonic carcinoma cells add two key repressors, dimethylated H3K9 and<br />
trimethylated H3K9, which are both associated with DNA hypermethylation in<br />
adult cancers (14). Polycomb group proteins reversibly repress genes required<br />
for differentiation in embryonic stem cells. Stem cell polycomb group targets are<br />
up to 12-fold more likely to have cancer-specific promoter DNA hypermethylation<br />
than nontargets, supporting a stem cell origin <strong>of</strong> cancer where<br />
reversible gene repression is replaced by permanent silencing, locking the cell<br />
into a perpetual state <strong>of</strong> self-renewal, thereby predisposing to subsequent<br />
malignant transformation (95). Thus, cell chromatin patterns and transient<br />
silencing <strong>of</strong> these important regulatory genes in stem or progenitor cells may<br />
leave these genes vulnerable to aberrant DNA hypermethylation and heritable<br />
gene silencing during tumor initiation and progression.<br />
Vascular niches have been described in the case <strong>of</strong> neural stem cells, which<br />
were found to be concentrated in regions that are rich in blood vessels (18).<br />
These vessels shelter neural stem cells from apoptotic stimuli and maintain a<br />
proper balance between self-renewal and differentiation. The endothelial cells<br />
that line blood vessels are key components <strong>of</strong> the vascular niche as they secrete<br />
factors that promote stem cell survival and self-renewal. As the cotransplantation<br />
<strong>of</strong> tumor cells with endothelial cells leads to more rapid tumor formation, the<br />
vascular niche may contribute to tumor initiation and development (44). In an<br />
established tumor, cancer stem cells that find a vascular niche may continue to<br />
self-renew, while the remaining cells may differentiate to form the bulk <strong>of</strong> the<br />
tumor, but not to its long-term maintenance (18). Thus, the identification <strong>of</strong><br />
endothelial cell signals, which regulate the self-renewal <strong>of</strong> cancer stem cells,<br />
may also contribute to our understanding <strong>of</strong> the interrelationship <strong>of</strong> cancer stem<br />
cells and their niche. There is some evidence that there is a bidirectional relationship<br />
between cancer stem cells and their niche. For example, gliomas secrete<br />
elevated levels <strong>of</strong> vascular endothelial growth factor (VEGF), which increases
280 Balicki et al.<br />
endothelial cell migration and tube formation (43). In addition, VEGF promotes<br />
neural stem cell survival (96). Thus, the effectiveness <strong>of</strong> antiangiogenic agents in<br />
cancer therapy may relate to the prevention <strong>of</strong> new blood vessels that inhibit<br />
tumor growth. In addition, antiangiogenic therapy may disrupt a vascular niche,<br />
which is necessary for cancer stem cell renewal (18).<br />
In the case <strong>of</strong> hematopoietic stem cell niches, studies have focused on the<br />
endosteum, the inner surface <strong>of</strong> the bone that interfaces with the bone marrow.<br />
Regulation <strong>of</strong> hematopoietic stem cell function is governed by factors secreted<br />
by osteoblasts, osteoclasts, and stromal fibroblasts within the endosteum (8).<br />
Imaging data suggests that hematopoietic stem cells reside near the endosteum,<br />
and that this localization and maintenance is also usually adjacent to CXCL12-<br />
secreting reticular cells, irrespective <strong>of</strong> whether the hematopoietic stem cells<br />
are in vascular or endosteal locations (97,98). The stimulation via parathyroid<br />
hormone–related protein receptors (PPR) or an increase in number <strong>of</strong> osteoblastic<br />
cells in the periosteum with high levels <strong>of</strong> the Notch ligand Jagged1,<br />
enlarged the stem cell niche with a resultant increase in the number <strong>of</strong> hematopoietic<br />
stem cells with evidence <strong>of</strong> Notch1 activation in vivo (99). The<br />
activation <strong>of</strong> Notch1 has been shown to result in enhanced self-renewal <strong>of</strong><br />
hematopoietic stem cells possibly by inducing self-renewal genes including<br />
Hes1 (8). In addition, the bone morphogenetic protein signal has an essential<br />
role in inducing haematopoietic tissue during embryogenesis within the hematopoietic<br />
stem cell niche (100). Mesenchymal stem cells have been shown to<br />
improve engraftment <strong>of</strong> hematopoietic stem cells, and suppress graft-versushost<br />
disease after allogeneic hematopoietic stem cell transplantation. In addition,<br />
when tumor cells are injected into NOD/SCID mice in conjunction with<br />
mesenchymal stem cells, their growth is much faster as compared to the group<br />
receiving only tumor cells. To explain this observation, it was suggested that<br />
mesenchymal stem cells have the ability to form a cancer stem cell niche in<br />
which tumor cells can preserve their proliferative potential to sustain the<br />
malignant process (101).<br />
Activated, irradiated, mammary fibroblasts are critical to the microenvironment<br />
<strong>of</strong> mammary stem cells as they are required for the successful<br />
repopulation <strong>of</strong> cleared mammary fat pads in immunocompromised mice (102).<br />
These epithelial-stromal interactions are also important in malignant transformation<br />
and likely to be important with cancer stem cells (11). Fibroblasts secrete<br />
matrix metalloproteinases and protease inhibitors and mobilize and activate<br />
growth factors such as tumor growth factor-b (TGF-b), which has been shown to<br />
promote tumor progression and invasion (103). Carcinoma-associated fibroblasts<br />
promote angiogenesis, as well as tumor growth, by secreting stromal cell-derived<br />
factor-1 (SDF-1), which interacts with the chemokine receptor CXCR4 on tumor<br />
cells and endothelial cells (104). As radiographic breast density correlates with<br />
increased breast cancer risk, as well as increased fibroblasts and collagen<br />
deposition, this modified niche may promote the transformation <strong>of</strong> initiated stem<br />
cells into cancer stem cells (11,105).
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 281<br />
FUTURE CHALLENGES: INDIVIDUALIZED THERAPY BASED<br />
ON BREAST CANCER STEM CELLS AND THEIR NICHE<br />
In the last few years, there has been a significant evolution in breast cancer care, if<br />
not a revolution (106). Whereas, we previously focused on tumor size, nodal<br />
status, and certain prognostic features based on tumor pathology, the paradigm has<br />
now shifted to include the underlying molecular pathways that drive cancerigenesis,<br />
as well as the cellular targets <strong>of</strong> breast cancer, including cancer stem cells<br />
and their niche. The intricate circuitry <strong>of</strong> pathways that define the hallmarks <strong>of</strong><br />
cancer and the corresponding complex molecular interactions are being translated<br />
into therapeutic benefit (107,108). Individualized drivers are gradually being<br />
factored into patient assessments in order to astutely and accurately determine the<br />
clinical course <strong>of</strong> breast cancer patients on the basis <strong>of</strong> their unique and complex<br />
cellular and molecular breast cancer portraits.<br />
<strong>Breast</strong> cancer stem cell markers and the critical molecules in the pathways<br />
that drive their self-renewal are necessary to complete the molecular portrait <strong>of</strong><br />
breast cancer. Our failure to eradicate most cancers may be as fundamental as a<br />
misidentification <strong>of</strong> the target cancer stem cell (Fig. 3) (6). The fact that stem cells<br />
rarely divide and have unique cellular properties, coupled with the observation that<br />
they may have high levels <strong>of</strong> drug transporters to pump chemotherapy agents out<br />
<strong>of</strong> the cell, has led many to believe that traditional chemo- or radiation therapies<br />
are insufficient to clear these tumor-initiating cells from the body (92). Clinical<br />
observations indicate that breast cancer responses to therapy do not necessarily<br />
correlate with patient survival and are incomplete clinical endpoints. Instead, the<br />
effectiveness <strong>of</strong> these treatments should be evaluated by decreased cancer recurrence<br />
and metastases as measures <strong>of</strong> therapeutic efficacy (92). As the molecular<br />
pathways that govern normal and cancer stem cells overlap, the targeting <strong>of</strong> these<br />
pathways could have harmful effects on the homeostatic function <strong>of</strong> normal stem<br />
cells. Targeted therapies that specifically eliminate cancer stem cells could further<br />
revolutionize the ways by which we treat cancer.<br />
As the proliferation <strong>of</strong> cancer stem cells is important for the growth <strong>of</strong> the<br />
primary tumor, as well as progression to metastatic disease, inhibition <strong>of</strong> the selfrenewal<br />
capacity <strong>of</strong> these cells is an obvious target for their elimination. Therapies<br />
such as cyclopamine (targeting the HH signaling) and exisulind, bromoindirubin-3 0 -<br />
oxime, and imatinib (targeting the Wnt/b-catenin pathways) were designed to<br />
inhibit self-renewal pathways critical to cancer stem cells, and have achieved<br />
varying levels <strong>of</strong> success (92). Another strategy to eliminate self-renewal is to force<br />
cancer stem cells to differentiate (Fig. 6).<br />
As cancer stem cells are currently being identified in a broad spectrum <strong>of</strong><br />
cancers (44,109–112), significant advances in their fundamental properties are<br />
forthcoming, and will serve as a scaffold to expedite our understanding and identification<br />
<strong>of</strong> cancer stem cells within specific types <strong>of</strong> cancers, including breast<br />
cancer. The existence <strong>of</strong> overlapping cancer stem cell pathways within different<br />
cancer types will also stimulate the discovery and development <strong>of</strong> new drugs to
282 Balicki et al.<br />
target these pathways, as they are likely to be effective and marketable in more than<br />
merely a small subset <strong>of</strong> cancer patients.<br />
The addition <strong>of</strong> molecular markers in our patient assessments acknowledges<br />
the pivotal role that the corresponding molecular pathways play in fueling<br />
tumorigenesis. This fine-tuning <strong>of</strong> patient prognostic factors will ensure that the<br />
oncogenic drivers <strong>of</strong> each individual patient’s tumor pathophysiology are both<br />
identified and tailored into the definition and optimization <strong>of</strong> their specific<br />
therapeutic strategy, which will translate into their best possible clinical outcome<br />
and overall survival. As a result, it is critical that our therapeutic armamentarium<br />
target the specific fingerprint <strong>of</strong> cellular and molecular pathways defined for an<br />
individual patient. Therapeutic resistance via quiescence and the sequestration <strong>of</strong><br />
cancer stem cells into sanctuaries that are impermeable to most therapeutic<br />
agents must be taken into consideration in the design <strong>of</strong> individualized breast<br />
cancer therapies. Therapeutic resistance may also result from an inadequate<br />
depletion <strong>of</strong> key cellular and molecular elements within the cancer stem cell<br />
niche. The elucidation <strong>of</strong> the secreted factors that set up the premetastasis and<br />
metastatic niche could provide both diagnostic as well as therapeutic benefits.<br />
Drugs such as Bevacizumab (Avastin 1 ) that reduce intratumoral pressure<br />
associated with leaky tumoral vasculature and thereby increase the introduction<br />
<strong>of</strong> intravenous agents within the tumor bed may have the critical synergy necessary<br />
in combinations <strong>of</strong> targeted agents to permeate cancer stem cell sanctuaries<br />
and cancer stem cell niches (18,44,113,114). In addition, they may target<br />
both the vascular niche and the associated cancer stem cells (44). Thus, it is not<br />
surprising that clinical trials <strong>of</strong> Bevacizumab combined with the chemotherapeutic<br />
drug CPT-11 suggest that this combination is one <strong>of</strong> the most<br />
effective treatments <strong>of</strong> glioblastoma (115). Other agents that stimulate cycling <strong>of</strong><br />
quiescent cancer stem cells may also be beneficial in combined therapies, which<br />
include agents that eradicate cycling cells. In addition, individualized therapy<br />
must also have the long-term objectives <strong>of</strong> predicting and preparing to eradicate<br />
the molecular pathways that may circumvent those pathways that drove the<br />
initial breast cancer pathophysiology, and were targeted by first-line targeted<br />
therapies. This strategy also underlines the critical objective <strong>of</strong> eradicating every<br />
single cancer stem cell and its niche in newly diagnosed breast cancer patients,<br />
thereby striving to eliminate all possibilities that these lethal seeds in lethal soil<br />
may ignite tumor recurrence in the future.<br />
Therefore, the genesis <strong>of</strong> individualized breast cancer therapy including<br />
the eradication <strong>of</strong> cancer stem cells has become a necessity to achieve optimal<br />
breast cancer patient care. It is imperative to understand the biology <strong>of</strong> cancer<br />
stem cells and their niche to optimize our cancer treatment strategies to prevent<br />
the longevity <strong>of</strong> cancer stem cells from interfering with our own (18). The<br />
associated costs <strong>of</strong> its future reality precipitate a call to rethink our healthcare<br />
strategies with a focus on cancer prevention and therapeutic schedules which<br />
deliver targeted agents when they are both necessary and most effective in our<br />
quest to optimally treat an individual breast cancer patient with the ultimate goal
<strong>Breast</strong> <strong>Cancer</strong> Stem Cells and Their Niche: Lethal Seeds in Lethal Soil 283<br />
<strong>of</strong> a stable, long-term remission. In this setting, the identification and targeting <strong>of</strong><br />
breast cancer stem cells and their niche hold great promise in the development <strong>of</strong><br />
new therapeutic translational strategies using state-<strong>of</strong>-the-art individualized<br />
therapies for breast cancer patients.<br />
ACKNOWLEDGMENTS<br />
Rhyna Salinas (CHUM) is gratefully acknowledged for her expert assistance in<br />
the preparation <strong>of</strong> this chapter. The contributions made by Drs. Francine<br />
Jolicoeur, Isabel Garcia de la Fuente, and Louis Gaboury (CHUM) to Figure 4<br />
during their collaboration with D.B. are greatly appreciated. Drs. Connie Eaves,<br />
Gabriela Dontu, Suling Liu, Christophe Ginestier, Hasan Korkaya, and Julie<br />
Dutcher are thankfully acknowledged for insightful and inspirational discussions.<br />
Dr. Connie Eaves is also gratefully recognized for her invaluable role<br />
as D.B.’s first stem cell research mentor and role model, who initially planted the<br />
seed that she continues to nourish. D.B. is a scholar <strong>of</strong> Fonds de la recherche en<br />
santé du Québec (FRSQ) and principal investigator <strong>of</strong> the National <strong>Cancer</strong><br />
Institute <strong>of</strong> Canada’s Clinical Trials Group (NCIC CTG). This chapter was also<br />
made possible through a Canadian Institutes for Health Research (CIHR)<br />
operating grant to D.B..<br />
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19<br />
Molecular Imaging in Individualized<br />
<strong>Cancer</strong> Management<br />
David M. Schuster<br />
Division <strong>of</strong> Nuclear Medicine and Molecular Imaging, Department<br />
<strong>of</strong> Radiology, Emory University, Atlanta, Georgia, U.S.A.<br />
Diego R. Martin<br />
Department <strong>of</strong> Radiology, Emory University, Atlanta, Georgia, U.S.A.<br />
INTRODUCTION<br />
X-rays were discovered by Karl Roentgen in 1895, and with that discovery came<br />
the ability to see structures within the living human body without having to directly<br />
sample the tissue. Imaging, particularly cross-sectional techniques, has revolutionized<br />
the practice <strong>of</strong> medicine. These techniques employ ionizing radiation, such<br />
as computed tomography (CT) scanning, single-photon emission computed<br />
tomography (SPECT), and positron emission tomography (PET), as well as nonionizing<br />
energy, such as ultrasound and magnetic resonance imaging (MRI). Some<br />
<strong>of</strong> the most exciting and practical major advances are unfolding within the MRI and<br />
PET technologies, specifically with regard to molecular imaging.<br />
Molecular imaging is defined as the in vivo characterization and measurement<br />
<strong>of</strong> biological processes at cellular and molecular levels. By definition,<br />
molecular imaging is not restricted to any one tool; it may involve optical,<br />
ultrasound, gamma ray, positron, or magnetic imaging (1). Since the 1960s, bone<br />
scanning has played a major role in the management <strong>of</strong> breast cancer. However,<br />
in the past decade, the role <strong>of</strong> radionuclide molecular imaging has expanded<br />
291
292 Schuster and Martin<br />
significantly in the clinical management <strong>of</strong> breast cancer because <strong>of</strong> PET,<br />
mammoscintigraphy, and sentinel lymph node techniques. Molecular imaging is<br />
also instrumental in drug development, gene therapy, and basic science research<br />
<strong>of</strong> breast cancer. Mammography and ultrasound have been and continue to be<br />
very important tools in the diagnosis and management <strong>of</strong> breast cancer, but other<br />
modalities, such as MRI, have enhanced our ability to detect occult disease,<br />
especially in those with high genetic risk, and to follow breast carcinoma during<br />
and after therapy (2,3).<br />
In this chapter, we will examine MRI and PET with regard to molecular<br />
imaging as applied to individualized breast cancer management; this chapter is<br />
not intended to be a comprehensive review <strong>of</strong> molecular imaging methods. We<br />
have specifically chosen MRI and PET because we believe the integration <strong>of</strong><br />
both technologies will lead to further progress in our ability to interrogate disease<br />
processes in a manner analogous to the classical pathologist, but in a<br />
noninvasive fashion. This ability will continue to expand as technology progresses.<br />
But in essence, the future is already here.<br />
RADIONUCLIDE TECHNIQUES:<br />
CURRENT CONCEPTS AND APPLICATIONS<br />
The advantage <strong>of</strong> radionuclide techniques over other imaging approaches is their<br />
ability to label any chemical species with an isotope <strong>of</strong> choice and to detect that<br />
radiotracer with high sensitivity. When certain radionuclides decay, gamma rays<br />
(photons) are given <strong>of</strong>f, which are detected with special crystal detectors. The<br />
gamma emissions <strong>of</strong> SPECT or PET radiotracers are energetic enough to travel<br />
through tissues and thus enable imaging <strong>of</strong> the whole body. Carbon, nitrogen,<br />
hydrogen, and oxygen have positron-emitting isotopes and are ideally suited for<br />
designing probes to image natural biochemical and molecular process in vivo.<br />
Probes can either home in on targets on cell surfaces, such as antigens and<br />
receptors, or intracellular structures such as internalized receptors.<br />
Single-photon imaging is the basis <strong>of</strong> simple techniques such as planar<br />
bone scanning or more sophisticated cross-sectional approaches such as SPECT.<br />
High-energy dual-photon imaging is utilized for PET, permitting better resolution<br />
and sensitivity than single-photon techniques. Yet, each method has its<br />
place. For example, sentinel lymph node procedures, employing single-photon<br />
radiotracers, are rapidly becoming the standard <strong>of</strong> care in breast cancer lymph<br />
node staging (4). For all practical purposes modern PET scanning is now<br />
accomplished as part <strong>of</strong> a PET-CT device, which fuses the functional imaging <strong>of</strong><br />
PET with the anatomic detail <strong>of</strong> CT.<br />
In 1984, Beaney and coworkers reported (5) on the use <strong>of</strong> [ 15 O]H 2 O PET<br />
to study blood flow, the oxygen extraction ratio, and oxygen utilization in breast<br />
cancer. The earliest studies on the use <strong>of</strong> 18 F-fluorodeoxyglucose (FDG) in the<br />
diagnosis <strong>of</strong> breast cancer were conducted in 1989 (6,7). Figure 1 is an example<br />
<strong>of</strong> 18 F-FDG PET uptake in a primary breast mass and axillary lymph nodes.
Molecular Imaging in Individualized <strong>Cancer</strong> Management 293<br />
Figure 1 (A) Axial CT, (B) attenuation corrected PET, and (C) fused image in a 50-year-old<br />
female with a 4-cm primary right breast cancer (arrow) and malignant axillary lymph<br />
nodes (arrowhead ). Abbreviations: CT, computed tomography; PET, positron emission<br />
tomography.<br />
Fluorodeoxyglucose is actively transported into the cell, where it is irreversibly<br />
phosphorylated by hexokinase, trapping it within the cell, where it does<br />
not proceed further down the glycolytic pathway. There is a correlation between<br />
FDG uptake and glucose transporter-1 (Glut-1) expression, the mitotic activity<br />
index, percent necrosis (likely reflecting the proliferation rate), tumor cells/<br />
volume, hexokinase-1 expression, the number <strong>of</strong> lymphocytes (but not macrophages),<br />
microvessel density, and possibly the expression <strong>of</strong> the p53 gene (8–12).<br />
PET is currently <strong>of</strong> limited value in routine screening for breast cancer (13)<br />
and cannot take the place <strong>of</strong> fine sectioning and immunohistochemical lymph<br />
node evaluation, but it has an important role to play in a select group <strong>of</strong> problematic<br />
diagnostic situations and in staging patients with high-risk disease (14–17).<br />
PET can provide important information by detecting locoregional lymph node<br />
involvement as well as distant metastases. Because <strong>of</strong> its high positive predictive<br />
value, FDG PET may obviate a sentinel lymph node procedure and axillary<br />
dissection.<br />
The use <strong>of</strong> neoadjuvant chemotherapy has increased the rate <strong>of</strong> breast<br />
conservation surgery (18). Clinical response does not necessarily correlate with<br />
pathologic response. FDG PET has been shown to aid in monitoring response to<br />
chemotherapy and has prognostic benefit (19). Nonresponders and those<br />
developing progressive disease or distant metastases can be identified earlier,<br />
and this information may prove useful in changing therapies and avoiding side<br />
effects <strong>of</strong> chemotherapy that is not effective. Baseline PET combined with PET<br />
after the first course <strong>of</strong> chemotherapy is highly accurate in this regard (16). A<br />
meta-analysis <strong>of</strong> the FDG PET literature (20) reports an overall 81% sensitivity,<br />
96% specificity, and 92% accuracy for monitoring response to therapy. Elevated<br />
FDG uptake was found to be an independent predictor <strong>of</strong> worse prognosis and<br />
decreased relapse-free survival in breast cancer (10). Figure 2 illustrates a dramatic<br />
response <strong>of</strong> metastatic breast cancer to fulvestrant (Faslodex) therapy.<br />
FDG PET has been shown to locate unsuspected metastases with high<br />
sensitivity and specificity (21,22). Detection <strong>of</strong> early recurrence may have
294 Schuster and Martin<br />
Figure 2 (A) Anterior view from a maximal intensity projection <strong>of</strong> an FDG-PET scan<br />
showing extensive breast cancer pleural implants in the left chest and (B) after one dose <strong>of</strong><br />
fulvestrant (Faslodex) after which the implants resolved. Note normal cardiac (arrow) and<br />
renal (arrowhead) uptake is better seen in B than in A. The patient was status post<br />
unrelated remote right nephrectomy. Abbreviations: FDG,<br />
18 F-fluorodeoxyglucose;<br />
PET, positron emission tomography.<br />
important survival benefits, prompting the use <strong>of</strong> new therapies as well as curative<br />
or palliative surgery. FDG PET is considered <strong>of</strong> great efficacy in the evaluation <strong>of</strong><br />
patients with suspected recurrent breast cancer, especially to differentiate true<br />
recurrence from postsurgical and radiation sequelae. The ability <strong>of</strong> PET to detect<br />
suspected clinical recurrence and in patients with elevated tumor markers has been<br />
demonstrated in a number <strong>of</strong> studies (23–32).<br />
MRI: CURRENT CONCEPTS AND APPLICATIONS<br />
In 1973, Paul Lauterbur discovered that introduction <strong>of</strong> magnetic gradients<br />
superimposed over a magnetic field makes it possible to create two-dimensional<br />
images <strong>of</strong> structures, with the first experiments showing the ability to differentiate<br />
between tubes filled with regular or heavy water (33). No other imaging<br />
method can achieve this differentiation. This work was the foundation for a<br />
revolutionary tissue-imaging technique that generates images on the basis <strong>of</strong><br />
molecular differences between or within tissues.<br />
To understand the added value <strong>of</strong> MRI to clinical sciences, it is useful to<br />
compare it with CT. CT can produce images <strong>of</strong> high spatial resolution, which are<br />
interpreted on the basis <strong>of</strong> anatomy. A distinct feature <strong>of</strong> MRI is that anatomically<br />
correct images are produced, but these images are based on molecular<br />
events that produce much higher s<strong>of</strong>t tissue contrast between tissues and between
Molecular Imaging in Individualized <strong>Cancer</strong> Management 295<br />
Figure 3 Liver cross-sectional imaging showing multiple hypervascular breast metastases.<br />
(A) On contrast-enhanced CT, the liver lesions are not visualized. (B) On contrastenhanced<br />
MRI, the lesions are conspicuous with innumerable tumors scattered throughout<br />
the liver (6 are shown: B, black arrows). Abbreviations: CT, computed tomography; MRI,<br />
magnetic resonance imaging.<br />
abnormal and normal tissues. It has been shown for most clinically important<br />
applications, including the brain and the solid organs <strong>of</strong> the body, that s<strong>of</strong>t tissue<br />
contrast resolution is more important than spatial resolution. Even on contrastenhanced<br />
CT, the ability to generate s<strong>of</strong>t tissue contrast resolution is less than<br />
that <strong>of</strong> MRI, and this difference may contribute to the relative advantage that<br />
MRI provides for lesion detection (Fig. 3).<br />
Unlike CT, which relies on the degree <strong>of</strong> X-ray absorption to differentiate<br />
tissues, MRI contrast differences are based on molecular interactions between<br />
protons located in water molecules within and between cells, neighboring<br />
molecules that influence mobility, such as charged glycoproteins and membranes,<br />
and species that can alter the magnetic field at the molecular level, such<br />
as iron and lipid. There are essentially an infinite variety <strong>of</strong> formulations <strong>of</strong> MRI<br />
techniques that can exploit these interactions to generate different contrast patterns.<br />
Similar to the concept <strong>of</strong> a pathologist using different histochemical stains<br />
to visualize molecular species, the expert MR imager will utilize an optimized<br />
set <strong>of</strong> MRI sequences to visualize the characteristics that may be unique to<br />
specific pathologies. Though the initial applications <strong>of</strong> MRI had been for<br />
imaging diseases <strong>of</strong> the brain, MRI is now able to image faster and with concomitant<br />
improvement in image quality. This has been critical for improving the<br />
capacity <strong>of</strong> MRI for rapidly imaging the chest, abdomen, and pelvis, where<br />
motion from respiration or cardiac contractions may deteriorate images (34,35).<br />
<strong>Breast</strong> MRI (Fig. 4) is a recent additional imaging method for the evaluation<br />
<strong>of</strong> primary breast carcinoma and is useful for early detection and staging<br />
(36–40). MRI has been found to provide maximal sensitivity, as compared to<br />
traditional mammography and ultrasound, but with comparatively lower specificity.<br />
The fundamental components to the current state <strong>of</strong> the art <strong>of</strong> breast MRI<br />
is to use the combination <strong>of</strong> time-resolved gadolinium uptake analysis in combination<br />
with the evaluation <strong>of</strong> the contours <strong>of</strong> a mass using high-contrast and
296 Schuster and Martin<br />
Figure 4 (A) Right breast carcinoma shown on sagittal plane dynamically enhanced<br />
gadolinium MRI at the indicated time intervals. Subsequently, (B) a region <strong>of</strong> interest is<br />
applied within the tumor, and a time-intensity curve is generated. (C) A lymph node<br />
showing a similar enhancement pattern (arrow) is shown on a more lateral slice through<br />
the right axilla on a representative 3-min sagittal image. (D) The enhancing primary breast<br />
carcinoma and the metastatic lymph node in the right axilla can be seen on an axial MRI<br />
image <strong>of</strong> both breasts (arrows), which correlates with findings on (E) PET-CT (arrows),<br />
confirming the presence <strong>of</strong> metastasis in the node. Note that the position <strong>of</strong> the breast is<br />
different on the MRI compared with the PET-CT due to prone MRI versus supine PET<br />
positioning. Abbreviations: MRI, magnetic resonance imaging; PET, positron emission<br />
tomography; CT, computed tomography.<br />
high-resolution imaging. MRI techniques have benefited from improvements in<br />
surface coils specifically dedicated to imaging both breasts simultaneously.<br />
The most important sequence used for routine breast MRI is the threedimensional<br />
gradient echo (3D GRE) technique. Uniform and complete suppression<br />
<strong>of</strong> the breast lipid signal has been a key area <strong>of</strong> focus for improvement<br />
<strong>of</strong> these sequences. With time-resolved imaging after an MRI contrast injection<br />
(Fig. 4), malignant focal breast carcinomas typically show enhancement that
Molecular Imaging in Individualized <strong>Cancer</strong> Management 297<br />
remains constant over several minutes or may diminish. Benign tumors such as<br />
fibromas may show differential contrast uptake, with progressive increase in<br />
contrast accumulation within the benign mass. However, the specificity for these<br />
differentiating features is relatively moderate. A more recent approach has<br />
incorporated lessons learned previously from conventional X-ray mammography;<br />
the margins <strong>of</strong> the tumor may show patterns specific to malignancy. Data<br />
are developing to support that the analysis <strong>of</strong> tumor margin morphology will<br />
improve MRI specificity for the diagnosis <strong>of</strong> breast carcinoma and separate this<br />
condition from benign tumors. Here, further development <strong>of</strong> newer MRI strategies,<br />
such as perfusion studies or spectroscopy, or a possible combination with<br />
metabolic imaging provided by PET, may help improve this apparent current<br />
limitation.<br />
Lymphatic spread may result in local lymph node involvement. Findings<br />
on MRI may be relatively specific when the lymph nodes are found to be over<br />
1 cm in diameter and show contrast enhancement that is similar in time course<br />
and intensity to those seen in the primary breast mass. However, for smaller<br />
lymph nodes the specificity may be significantly lower. Combined imaging with<br />
FDG-PET and MRI may help aid the specificity <strong>of</strong> the MRI study and the<br />
identification <strong>of</strong> the most concerning lymph nodes (Fig. 4).<br />
Currently accepted applications for MRI include screening for high-risk<br />
women, evaluation <strong>of</strong> dense fibroglandular breast tissue, and surveillance after<br />
surgical alteration or breast implant placement (36–40). Generalized use <strong>of</strong> MRI<br />
for evaluation <strong>of</strong> all women in the screening age group has not been widely<br />
accepted.<br />
With regard to hematogenous tumor spread, the liver is the organ most<br />
commonly involved in metastatic diseases, usually from breast, colon, pancreas,<br />
kidney, and carcinoid cancers, or melanoma. However, most liver tumors are<br />
benign, and benign liver lesions are common, including cysts, bile duct hamartomas,<br />
hemangiomas, adenomas, and focal nodular hyperplasia. The specificity <strong>of</strong><br />
a noninvasive test is the litmus test for utility in tumor evaluation. We have shown<br />
that although MRI is slightly more sensitive than CT for tumor detection, the most<br />
important distinction is that MRI is highly specific, approaching 98%, while CT is<br />
significantly less specific, approaching 80% (41–44). The ability to image rapidly<br />
and include the evaluation <strong>of</strong> the entire body is undergoing development. The<br />
potential for assessing metastatic burden will be a primary potential role for this<br />
capability.<br />
FUTURE DEVELOPMENTS<br />
The resolution <strong>of</strong> PET scanners is currently limited in comparison with anatomic<br />
imaging, but continued technological improvements are expected<br />
(45,46). Sensitivity for initial diagnosis will likely improve as device resolution<br />
advances and as special focused field PET-mammography imaging systems are<br />
developed (47–49). Small field-<strong>of</strong>-view cameras not only <strong>of</strong>fer better sentinel
298 Schuster and Martin<br />
lymph node and primary tumor detection because <strong>of</strong> increased spatial resolution<br />
but can also be mounted on maneuverable arms and used in an intraoperative<br />
setting, helping the surgeon realize a tumor-free margin (50).<br />
MR spectroscopic analysis has the potential to elucidate pathophysiological<br />
processes and may become useful for the assessment <strong>of</strong> primary or<br />
metastatic neoplasms, or the assessment <strong>of</strong> tumor response to therapy. MRI<br />
strategies are providing new diagnostic methods <strong>of</strong> evaluating dynamic pathophysiological<br />
processes using time-resolved imaging. These methods include the<br />
use <strong>of</strong> an injected contrast agent that can be visualized as it perfuses and then<br />
redistributes within the tissues, much as a radiotracer is sometimes used for<br />
nuclear imaging techniques (51,52). The advantages <strong>of</strong> MRI for this application<br />
include the capacity for high temporal and spatial resolution and the ability to<br />
combine this component <strong>of</strong> an examination with other elements <strong>of</strong> MRI to<br />
provide comprehensive evaluation during a single study. This approach is being<br />
used to detect and characterize diseases, including neoplasms, inflammation and<br />
infection, and fibrotic processes (53–57).<br />
Apoptosis, or programmed cell death, is an elementary process <strong>of</strong> life that<br />
maintains homeostasis. Apoptosis is physiologic and is different from necrosis,<br />
which is pathologic, <strong>of</strong>ten eliciting an inflammatory reaction. Successful chemotherapy<br />
increases apoptosis and enhances the uptake <strong>of</strong> annexin in the tumor.<br />
As the tumor becomes necrotic, the uptake <strong>of</strong> annexin reduces. PET and SPECT<br />
imaging with radiolabeled annexin have been used to assess apoptosis (58–61).<br />
Molecular process that occur within breast cancer cells can be imaged by<br />
using a radiotracer based on amino acid metabolism, such as methionine, which<br />
can serve as an indirect measure <strong>of</strong> protein synthesis, or an amine, such as<br />
choline, which can reflect membrane biosynthesis.<br />
11 C-methionine and<br />
11 C-choline have been used in assessing breast cancer and metastases (62,63).<br />
DNA synthesis within breast cancer has been examined with thymidine analogues<br />
such as 3 0 -deoxy-3 0 -[ 18 F]fluorothymidine (FLT), which has been<br />
employed to image intratumoral proliferation (64,65). [ 15 O]H 2 O PET can also be<br />
useful for tumor perfusion (65). These radiotracers can serve to characterize<br />
tumor as well as measure therapeutic response.<br />
Estrogen and progestin receptors <strong>of</strong>fer another avenue to image breast<br />
cancer. Estrogen receptors (ERs) have been successfully targeted using 16a-[ 18 F]<br />
fluoro-17b-estradiol (FES), and early clinical studies have shown them to be as<br />
sensitive as FDG imaging and probably more specific (66). In one study <strong>of</strong><br />
patients with ER-positive breast cancer, the patients also underwent FES PET.<br />
PET was able to predict global response to hormonal therapy in human epidermal<br />
growth factor receptor 2 (HER2)-negative patients better than pathology<br />
(24% vs. 47%) (67,68). Radiolabeled chemotherapeutic agents such as [ 18 F]<br />
fluorotamoxifen may be useful to detect ER-positive metastases and can also<br />
help in following response to treatment (69). 18 F-labeled paclitaxel has also been<br />
shown in a mouse model with a breast carcinoma xenograft to predict tumor<br />
response to paclitaxel chemotherapy (70).
Molecular Imaging in Individualized <strong>Cancer</strong> Management 299<br />
Peptides that localize to receptors are small-sized molecules and clear<br />
rapidly via the kidney, making these peptides attractive imaging probes. Peptides,<br />
such as bombesin (BN), labeled with single-photon and dual-photon<br />
radionuclides are being studied both for imaging and for therapeutic purposes<br />
(71). Gastrin-releasing peptide (GRP) receptors, a subtype <strong>of</strong> BN receptors, are<br />
overexpressed in breast carcinoma. Octreoscan, a single-photon radiotracer<br />
somatostatin analogue, has also been used to study breast cancer (72).<br />
Several gene therapy approaches have been considered for the management<br />
<strong>of</strong> breast cancer, such as ablation <strong>of</strong> oncogenic products, restoration <strong>of</strong> ER<br />
expression, alteration <strong>of</strong> genes that are involved in apoptosis, and activation <strong>of</strong><br />
tumor suppressor genes. Reporter genes have been used to study promoter or<br />
regulatory elements involved in gene expression conventionally studied by tissue<br />
biopsy and immunohistochemistry. Now noninvasive in vivo methods are<br />
available in the form <strong>of</strong> radionuclide techniques or optical imaging. The ability<br />
to image deeper structures in larger animal models and in humans is the main<br />
advantage <strong>of</strong> radionuclide technique over optical imaging. For example, 8-[ 18 F]<br />
Fluoropenciclovir has been studied as a reporter probe for herpes simplex virus<br />
type 1 thymidine kinase (HSV1-tk) gene expression (73). Other possibilities still<br />
in very early development include specially designed, small radiolabelled antisense<br />
oligodeoxynucleotides that may help image gene expression (20). PET<br />
imaging with an appropriate PET reporter gene and probe combination may<br />
become a powerful tool for molecular imaging <strong>of</strong> human gene expression (74).<br />
HER2/neu overexpression in breast cancer is associated with poor response<br />
to chemotherapy and an unfavorable prognosis. <strong>Ph</strong>ase I clinical trials <strong>of</strong> E1A<br />
gene therapy targeting HER2/neu-overexpressed breast cancer has shown<br />
increased apoptosis (75). A novel PET radiotracer, (68)Ga-DOTA-F(ab 0 )(2)-<br />
herceptin, has been developed, which has the potential to follow tumor<br />
response to the heat shock protein 90 (Hsp90) inhibitor 17-allylamino-17-<br />
demethoxygeldanamycin (17AAG) (76). In an animal model, downregulation <strong>of</strong><br />
HER2 occurs independently <strong>of</strong> glycolytic response as measured by 18 F-FDG-<br />
PET. This method could be used to rapidly screen tumor response to a variety <strong>of</strong><br />
HER2 inhibitors and choose the correct agent within hours rather than wait for<br />
weeks or months to determine if there is a response. Multidrug resistance as<br />
manifested by MDR1 P-glycoprotein transport activity also has the potential to<br />
be studied with SPECT and PET radiotracers (77).<br />
Angiogenesis, the outgrowth <strong>of</strong> new capillaries, is an important component<br />
in tumor formation and spread. Imaging <strong>of</strong> angiogenesis for diagnosis and following<br />
therapy can be performed with PET and SPECT radiotracers targeting<br />
integrin, VEGF, and other biomarkers (78). For example, (64)Cu-DOTA-VEGF<br />
(121) PET has been successful in small animals to specifically image in vivo<br />
expression <strong>of</strong> VEGF (79).<br />
Tumor hypoxia may result in resistance to therapy. [ 18 F]Fluoromisonidazole-3-fluoro-1-(2<br />
0 -nitro-1 0 -imidazolyl)-2-propanol ([ 18 F]MISO) and Cu-diacetylbis(N4-methylthiosemicarbazone)<br />
(Cu-ATSM) radiotracers for PET and blood
300 Schuster and Martin<br />
oxygen level–dependent (BOLD) MRI are some <strong>of</strong> the techniques being utilized<br />
for in vivo imaging but require further study (80,81).<br />
Finally, nanoparticles technology may be utilized to achieve diagnostic<br />
imaging with both PET and MRI and enable combined modality radionuclide<br />
therapy with intracellular drug delivery (82). The feasibility <strong>of</strong> magnetic<br />
frequency–activated thermal necrosis with antibody-linked iron oxide nanoparticles<br />
conjugated with the single-photon radiotracer Indium-111 has been<br />
recently demonstrated in breast cancer xenografts (83).<br />
An understanding <strong>of</strong> pathology, together with the capabilities <strong>of</strong> MRI,<br />
facilitates the use <strong>of</strong> a particular MRI feature for the evaluation <strong>of</strong> a range <strong>of</strong><br />
diseases that affect different organ systems. The application <strong>of</strong> MRI timeresolved<br />
perfusion imaging for tumor ranges from primary diagnosis to the<br />
determination <strong>of</strong> the degree <strong>of</strong> tumor neoangiogenesis. The ability to quantify<br />
tumor angiogenesis may provide a useful surrogate marker <strong>of</strong> tumor responsiveness<br />
prior to initiating therapy or indicate tumor response even prior to any<br />
change in the overall volume <strong>of</strong> tumor tissue (Fig. 4). MRI may be useful for the<br />
specific evaluation <strong>of</strong> chemotherapeutic antiangiogenic agents such as anti-<br />
VEGF (84,85). The possibility <strong>of</strong> using MRI molecular imaging capabilities for<br />
directing therapy is being explored, including the detection <strong>of</strong> reporter gene<br />
transcription, the use <strong>of</strong> MRI tissue thermometry to guide therapeutic ultrasound<br />
for noninvasive tumor ablation, and the use <strong>of</strong> functional brain MRI to plan the<br />
surgical resection <strong>of</strong> tumors, including metastases to the brain (51,52,86–91).<br />
Most importantly, in a manner analogous to the fusion <strong>of</strong> PET and CT<br />
scanning, the fusion <strong>of</strong> PET and MRI scanning holds even greater promise in<br />
providing a one-stop shop for molecular function and exquisite anatomic detail<br />
but with a markedly reduced radiation dose. There have been a number <strong>of</strong> studies<br />
examining the utility <strong>of</strong> MRI and FDG PET. For example, Walter et al. studied<br />
42 lesions in 40 patients preoperatively (92). The sensitivity <strong>of</strong> FDG PET was<br />
63%, and the specificity was 91%; the sensitivity <strong>of</strong> MRI was 89%, and the<br />
specificity was 74%. Thus, both modalities can play complementary roles, with<br />
overall improved accuracy when used in conjunction. Other papers examining<br />
the complementary strengths and weaknesses <strong>of</strong> both modalities have emerged<br />
(93–95). A recent intriguing study in a small group <strong>of</strong> patients examines the<br />
utility <strong>of</strong> MRI with ultrasmall superparamagnetic iron oxide particles in conjunction<br />
with PET, achieving 100% sensitivity and specificity on a per-patient<br />
basis for local lymph node staging when both modalities are employed (96).<br />
Further technical development is anticipated to facilitate combining MRI<br />
and PET (97). We have already started accumulating evidence <strong>of</strong> the advantages<br />
in this pairing <strong>of</strong> technologies, as compared with using the combination <strong>of</strong><br />
CT and PET. Figure 5 is an example from our institution <strong>of</strong> how PET and MRI<br />
data can be combined utilizing special s<strong>of</strong>tware registration. While the above<br />
PET-MRI studies have been performed on separate devices, necessitating<br />
moving and scheduling the patients between two physically separate units, a<br />
combined PET-MRI scanner in a single gantry is now a reality (45,46,97,98).
Molecular Imaging in Individualized <strong>Cancer</strong> Management 301<br />
Figure 5 <strong>Breast</strong> cancer patient (from Fig. 4) with axial MRI, PET, and CT images through<br />
the pelvis. (A) Shows the MRI and PET images before and after deformable registration <strong>of</strong><br />
the PET to the MRI. (B) The original CT (upper left) andPET-CT(lower left), compared<br />
with the MRI (upper right) and deformable registered PET-MRI (lower right). Increased<br />
FDG activity in the perirectal space on PET-CT (arrow) is <strong>of</strong> uncertain origin; however, the<br />
activity from the PET scan clearly corresponds to a benign fibroid on the MRI, which is not<br />
visible on CT. Benign fibroids may occasionally take up FDG in premenopausal women.<br />
The fusion <strong>of</strong> PET and MRI increased diagnostic accuracy in this patient. Abbreviations:<br />
MRI, magnetic resonance imaging; PET, positron emission tomography; CT, computed<br />
tomography; FDG, 18 F-fluorodeoxyglucose.<br />
OUR VISION<br />
Molecular imaging will continue to have an evolving role in diagnosis, staging,<br />
personalization <strong>of</strong> therapy, monitoring response to therapy, and detecting<br />
recurrence <strong>of</strong> breast carcinoma, both local and widespread. A patient will arrive<br />
in the imaging suite and have the entire disease process interrogated on a specially<br />
modified PET-MRI system, which will utilize dynamic MRI techniques<br />
and MR spectroscopy as well as novel PET radiotracers. Complex processes<br />
such as glycolysis, tumor perfusion, cell proliferation, protein synthesis, genetic<br />
expression, angiogenesis, and various other pr<strong>of</strong>iling will be examined. There is<br />
potential for additional information and improvements over current invasive<br />
tissue sampling and histopathology, which, for example, may not be truly representative<br />
<strong>of</strong> the heterogeneity present within a primary tumor or metastases.<br />
This process will not simply serve as a baseline to monitor therapy but<br />
will help individualize therapy since all the parameters can be entered into a<br />
matrix and therapy chosen on the basis <strong>of</strong> data derived from a cumulative<br />
outcomes database, including surgical, chemotherapeutic, radiotherapy, and<br />
genetic therapy options. Drug-target matching and optimal dosing can be<br />
assessed utilizing the powerful molecular techniques described above. These<br />
techniques will also facilitate targeted biopsies, which will improve genomic<br />
and proteomic pr<strong>of</strong>iling and permit a more tailored radiotherapy. Designer<br />
therapeutic and imaging agents may also be synthesized on the basis <strong>of</strong> a<br />
patient’s particular pr<strong>of</strong>ile.
302 Schuster and Martin<br />
These same methods may then be used to quickly determine treatment<br />
response as measured by glycolysis, amino acid transport, tumor perfusion,<br />
proliferation, and apoptosis, among other metabolic indices, and also monitor for<br />
resistant or mutating clones and for recurrence. Chemotherapeutic and genetic<br />
therapies may then be altered or doses optimized on the basis <strong>of</strong> rapid molecular<br />
imaging feedback rather than maximizing therapy based only on toxicity effects.<br />
The possibility <strong>of</strong> therapy using nanoparticle delivery <strong>of</strong> killing doses <strong>of</strong><br />
radiotherapy combined with chemotherapy may also be possible. The same<br />
nanoparticles could also carry MR and PET imaging tracers.<br />
The concept <strong>of</strong> the “one magic bullet” for the generic enemy <strong>of</strong> cancer has<br />
fallen by the wayside. A host <strong>of</strong> ordinary tools optimized for a particular patient<br />
and his or her own cancer can be combined in a unique fashion with the help <strong>of</strong><br />
the “magic window” afforded by molecular imaging. Though defeating the<br />
cancer is a l<strong>of</strong>ty goal, these tools may also enable patients to live with cancer and<br />
manage the entire disease process holistically in cooperation with their medical<br />
caregivers. The true cost-benefit ratios <strong>of</strong> therapies can then be ascertained. We<br />
believe the technology already exists, but further research, social, and financial<br />
investment, as well as societal vision is required for this dream to flower into<br />
everyday reality.<br />
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20<br />
<strong>Cancer</strong> Nanotechnology for Molecular<br />
Pr<strong>of</strong>iling and Individualized Therapy<br />
May Dongmei Wang<br />
Departments <strong>of</strong> Biomedical Engineering and Electrical<br />
and Computer Engineering, Georgia Institute <strong>of</strong> Technology<br />
and Emory University, Atlanta, Georgia, U.S.A.<br />
Jonathan W. Simons<br />
The Prostate <strong>Cancer</strong> Foundation, Santa Monica, California, U.S.A.<br />
Shuming Nie<br />
Departments <strong>of</strong> Biomedical Engineering and Chemistry<br />
and the Winship <strong>Cancer</strong> Institute, Emory University and<br />
Georgia Institute <strong>of</strong> Technology, Atlanta, Georgia, U.S.A.<br />
INTRODUCTION<br />
<strong>Cancer</strong> nanotechnology is an interdisciplinary area <strong>of</strong> research in science, engineering,<br />
and medicine with broad applications in molecular pr<strong>of</strong>iling and individualized<br />
therapy (1–4). The basic rationale is that nanometer-sized particles such as<br />
biodegradable micelles, semiconductor quantum dots (QDs), and iron oxide nanocrystals<br />
have functional or structural properties that are not available from either<br />
molecular or bulk materials (5–15). When linked with biotargeting ligands such as<br />
monoclonal antibodies, peptides, or small molecules, these nanoparticles are used to<br />
target malignant tumors with high affinity and specificity. In the “mesoscopic” size<br />
range <strong>of</strong> 5 to 100 nm diameter, nanoparticles also have large surface areas and<br />
functional groups for conjugating to multiple diagnostic (e.g., optical, radioisotopic,<br />
309
310 Wang et al.<br />
or magnetic) and therapeutic (e.g., anticancer) agents. The emergence <strong>of</strong> nanotechnology<br />
has opened new opportunities for personalized oncology in which cancer<br />
detection, diagnosis, and therapy are tailored to each individual’s molecular pr<strong>of</strong>ile,<br />
and also for predictive oncology in which genetic or molecular information is used to<br />
predict tumor development, progression, and clinical outcome.<br />
Recent advances have led to the development <strong>of</strong> functional nanoparticles<br />
that are covalently linked to biological molecules such as peptides, proteins,<br />
nucleic acids, or small-molecule ligands (1–4). Medical applications have also<br />
appeared, such as the use <strong>of</strong> superparamagnetic iron oxide nanoparticles as a<br />
contrast agent for lymph node prostate cancer detection (16), and the use <strong>of</strong><br />
polymeric nanoparticles for targeted gene delivery to tumor vasculatures (17). In<br />
particular, the U.S. Food and Drug Administration (FDA) recently approved<br />
Abraxane TM , an albumin-paclitaxel (Taxol TM ) nanoparticle for the treatment <strong>of</strong><br />
metastatic breast cancer (18). A phase I clinical trial determined that the maximum<br />
tolerated dose (MTD) <strong>of</strong> single-agent albumin-bound paclitaxel every<br />
three weeks was 300 mg/m 2 in patients with solid tumors (breast cancer and<br />
melanoma). A second phase I trial demonstrated five patient responses among<br />
39 pretreated patients with advanced solid tumors, including one response in a<br />
patient with non–small cell lung cancer (NSCLC), three responses in patients<br />
with ovarian cancer, and one in breast cancer. The dose-limiting toxicity was<br />
myelosuppression, the MTD was 270 mg/m 2 , and premedication was not<br />
required. Subsequent use <strong>of</strong> Abraxane in both phase II and phase III trials proved<br />
that this new formulation was considerably superior to Taxol. The use <strong>of</strong><br />
nanoparticles for drug delivery and targeting is one <strong>of</strong> the most exciting and<br />
clinically important applications <strong>of</strong> cancer nanotechnology.<br />
CANCER BIOMARKERS AND NANOTECHNOLOGY<br />
<strong>Cancer</strong> markers are broadly defined as altered or mutant genes, RNA, proteins,<br />
lipids, carbohydrates, small metabolite molecules, and altered expression <strong>of</strong><br />
those that are correlated with a biological behavior or a clinical outcome (19–22).<br />
Most cancer biomarkers are discovered by molecular pr<strong>of</strong>iling studies on the<br />
basis <strong>of</strong> an association or correlation between a molecular signature and cancer<br />
behavior. In the cases <strong>of</strong> both breast and prostate cancers, a major progression<br />
step is the appearance <strong>of</strong> so-called “lethal phenotypes” (causing patient death)<br />
such as bone metastatic, hormone-independent, and radiation- and chemotherapyresistant<br />
phenotypes. It has been hypothesized that each <strong>of</strong> these aggressive<br />
behaviors or phenotypes could be understood and predicted by a defining set <strong>of</strong><br />
biomarkers (20). By critically defining the inter-relationships between these<br />
biomarkers, it could be possible to diagnose and determine the prognosis <strong>of</strong> a<br />
cancer on the basis <strong>of</strong> a patient’s molecular pr<strong>of</strong>ile, leading to personalized and<br />
predictive medicine.<br />
One example is the drug trastuzumab (Herceptin TM ; Genentech/Roche,<br />
California, U.S./Basel, Switzerland), a monoclonal antibody designed to target
<strong>Cancer</strong> Nanotechnology 311<br />
amplified and overexpressed ERBB2 (also known as HER2) tyrosine kinase<br />
receptor found in only approximately 25% to 30% <strong>of</strong> breast cancers. FDA<br />
approval <strong>of</strong> trastuzumab was predicated on the availability <strong>of</strong> a test to detect<br />
ERBB2 overexpression. Both an immunohistochemistry (IHC) assay for the<br />
expressed protein (HercepTest TM ; Dako, Glostrup, Denmark) and a nucleic acid–<br />
based fluorescence in situ hybridization (FISH) test (PathVysion; Abbott Laboratories,<br />
Illinois, U.S.) have been approved as in vitro diagnostics to guide<br />
trastuzumab treatment decisions. In another example, the clinical response <strong>of</strong><br />
lung cancer patients to the epidermal growth factor receptor (EGFR) tyrosine<br />
kinase inhibitor gefitinib (Iressa TM ; AstraZeneca, London, U.K.) is associated<br />
with a small number <strong>of</strong> genetic mutations (23,24). Thus, a molecular diagnostic<br />
test could be used to identify patients that are most likely to respond to this drug.<br />
Despite these advances, critical studies that can clearly link biomarkers<br />
with cancer behavior remain a significant challenge. One difficulty is that most<br />
cancer tumors (especially prostate and breast cancer) are highly heterogeneous,<br />
containing a mixture <strong>of</strong> benign, cancerous, and stromal cells. Current technologies<br />
for molecular pr<strong>of</strong>iling including reverse-transcriptase polymerase<br />
chain reaction (RT-PCR), gene chips, protein chips, two-dimensional gel electrophoresis,<br />
biomolecular mass spectrometry (e.g., MALDI-MS, i.e., matrix<br />
assisted laser desorption ionization mass spectrometry, ES-MS, i.e., electro spray<br />
mass spectroscopy, and SELDI-MS, i.e., surface enhanced laser desorption/<br />
ionization) are not designed to handle this type <strong>of</strong> heterogeneous samples<br />
(25,26). Furthermore, a limitation shared by all these technologies is that they<br />
require destructive preparation <strong>of</strong> cells or tissue specimens into a homogeneous<br />
solution, leading to a loss <strong>of</strong> valuable three-dimensional cellular and tissue<br />
morphological information associated with the original tumor. The development<br />
<strong>of</strong> nanotechnology, especially bioconjugated nanoparticles, provides an essential<br />
link by which biomarkers could be functionally correlated with cancer behavior<br />
(Fig. 1).<br />
NANOTYPING<br />
Semiconductor QDs are tiny light-emitting particles on the nanometer scale, and<br />
are emerging as a new class <strong>of</strong> fluorescent labels for biology and medicine<br />
(8–15). In comparison with organic dyes and fluorescent proteins, QDs have<br />
unique optical and electronic properties such as size-tunable light emission,<br />
superior signal brightness, resistance to photobleaching, and simultaneous excitation<br />
<strong>of</strong> multiple fluorescence colors (Fig. 2). These properties are most promising<br />
for improving the sensitivity and multiplexing capabilities <strong>of</strong> molecular histopathology<br />
and disease diagnosis. Recent advances have led to highly bright and<br />
stable QD probes that are well suited for pr<strong>of</strong>iling genetic and protein biomarkers<br />
in intact cells and clinical tissue specimens (2–4). In contrast to in vivo imaging<br />
applications where the potential toxicity <strong>of</strong> cadmium-containing QDs is a major<br />
concern, immunohistological staining is performed on in vitro or ex vivo clinical
312 Wang et al.<br />
Figure 1 Size-tunable optical properties <strong>of</strong> semiconductor QDs for multiplexed molecular<br />
pr<strong>of</strong>iling <strong>of</strong> cancer. (A) Fluorescence emission spectra <strong>of</strong> different sizes <strong>of</strong> CdSe QDs.<br />
(B) Relative sizes <strong>of</strong> the five sizes <strong>of</strong> CdSe QDs corresponding to the above spectrum.<br />
From left to right the diameters are: 2.1 nm, 2.5 nm, 2.9 nm, 4.7 nm, and 7.5 nm.<br />
Abbreviations: CdSe, cadium selenide; QDs, quantum dots.<br />
patient samples. As a result, the use <strong>of</strong> multicolor QD probes in IHC is likely one<br />
<strong>of</strong> the most important and clinically relevant applications in the near term (27).<br />
Indeed, significant opportunities exist at the interface between QD nanotechnology<br />
and signature biomarkers for cancer diagnosis and individualized<br />
therapy. In particular, QD nanoparticle probes have been used to quantify a panel<br />
<strong>of</strong> biomarkers on intact cancer cells and tissue specimens, allowing a correlation<br />
<strong>of</strong> traditional histopathology and molecular signatures for the same material<br />
(2–4,27). A single nanoparticle is large enough for conjugation to multiple<br />
ligands, leading to enhanced binding affinity and exquisite specificity through a<br />
“multivalency” effect (28). These features are especially important for the<br />
analysis <strong>of</strong> cancer biomarkers that are present at low concentrations or in small<br />
numbers <strong>of</strong> cells.<br />
Quantitative biomarker information can be obtained by using a spectrometer<br />
attached to the fluorescence microscope. It is however very important to<br />
use a common protein such as b actin or glyceraldehyde 3-phosphate dehydrogenase<br />
(GAPDH) as an “internal control.” That is, one <strong>of</strong> the QD–Ab (antibody)<br />
conjugates should be designed to measure the product <strong>of</strong> a housekeeping gene<br />
that is expressed at relatively constant levels in all cells. The use <strong>of</strong> an internal<br />
control holds great promise for overcoming a number <strong>of</strong> major problems in<br />
biomarker quantification, such as differences in the probe brightness, variations<br />
in probe binding efficiency, uneven light illumination, and detector responses.
<strong>Cancer</strong> Nanotechnology 313<br />
Figure 2 Nanotechnology linking biomarkers with cancer behavior and clinical outcome.<br />
The schematic diagram shows multiplexed detection and quantification <strong>of</strong> cancer<br />
biomarkers on intact cells or tissues with multicolor nanoparticle probes. (Left) The<br />
images show cancer cells labeled with quantum dots, and (right) the drawings suggest<br />
how surface and intracellular biomarkers could be quantified by wavelength-resolved<br />
spectroscopy or spectral imaging.<br />
The majority <strong>of</strong> available tumor specimens are archived, formalin-fixed paraffinembedded<br />
(FFPE) tissues that might be several decades old. As the clinical<br />
outcomes <strong>of</strong> these tissues are already known, these specimens are well suited for<br />
examining the relationship between molecular pr<strong>of</strong>ile and clinical outcome in<br />
retrospective studies.<br />
It is also critically important to validate the QD staining data with other<br />
available techniques. For this purpose, O’Regan and coworkers (2) have<br />
obtained QD molecular pr<strong>of</strong>iling data from standard human breast cell specimens<br />
and have compared the corresponding biomarker data with traditional IHC<br />
and FISH techniques. Briefly, slides from formaldehyde-fixed paraffin cell<br />
blocks were stained in accordance with standard pathological protocols for three<br />
breast cancer biomarkers—estrogen receptor (ER), progesterone receptor (PR),<br />
and HER2. This panel <strong>of</strong> protein biomarkers was selected because <strong>of</strong> its clinical<br />
significance in human breast cancer diagnosis and treatment (29–32). The traditional<br />
IHC results were analyzed by two independent observers and scored<br />
with a standard scale from 0 (no visible staining in the nucleus or membrane) to
314 Wang et al.<br />
3þ (strong and complete membrane or nuclear staining in > 10% <strong>of</strong> malignant<br />
stained cells). For a comparative analysis <strong>of</strong> QD pr<strong>of</strong>iling with traditional IHC, it<br />
is necessary to normalize the absolute fluorescence intensities <strong>of</strong> QD–Ab signals,<br />
so that relative percentage values are calculated from the maximum signal<br />
strength.<br />
The results reveal that a 3þ score for ER, PR, or HER2 by traditional IHC<br />
corresponds to 85% to 100% relative expression <strong>of</strong> the antigen by QD–Ab measurement,<br />
and that 1þ or 2þ scores by traditional IHC correspond to 11% to 48%<br />
expression as determined by QD quantification. It should be noted that classification<br />
<strong>of</strong> antigens expressed at low levels (1þ or 2þ) is subjective, requiring<br />
experience and <strong>of</strong>ten resulting in considerable interobserver variations. In contrast,<br />
quantitative QD measurements allow accurate determination <strong>of</strong> tumor antigens at<br />
low levels. For example, PR expression in MCF-7 cells and ER expression in BT-<br />
474 cells are both classified as 1þ by traditional IHC, but quantitative QD<br />
measurements indicate major differences in PR expression (16.8%) and ER<br />
expression (47.7%) in these two cell lines. This indicates that the quantitative<br />
nature <strong>of</strong> QD-based molecular pr<strong>of</strong>iling could simplify and standardize categorization<br />
<strong>of</strong> antigens that are expressed at low levels. This is <strong>of</strong> fundamental<br />
importance in the management <strong>of</strong> breast cancer, as the likely benefit <strong>of</strong> hormonal<br />
therapies and trastuzumab depends directly on not just the presence but also the<br />
quantity <strong>of</strong> hormone or HER2 receptors (33–35) (Fig. 3).<br />
Wang and coworkers (27) have developed an integrated image processing<br />
and bioinformatics s<strong>of</strong>tware tool called Q-IHC for quantitative analysis <strong>of</strong> biomarker<br />
expression and distribution in IHC images. In comparison with previous<br />
image processing s<strong>of</strong>tware for automated feature extraction and quantitative<br />
Figure 3 Targeting the HER 2/Neu antigen on breast cancer cells by using antibodyconjugated<br />
quantum dots. The image shows individual breast cancer cells with<br />
nuclear counterstaining.
<strong>Cancer</strong> Nanotechnology 315<br />
analysis (36,37), this s<strong>of</strong>tware system is capable <strong>of</strong> handling imaging data from<br />
both traditional and QD-based IHC. To measure the distribution <strong>of</strong> labeled<br />
antigens, multiple slides <strong>of</strong> IHC imaging data are acquired to capture selected<br />
tissue structures. After image acquisition, an image-processing module carries<br />
out automatic boundary identification, semiautomatic image segmentation, and<br />
color-based tissue classification on the basis <strong>of</strong> biomarker staining. Then, an<br />
image analysis module quantifies the various biomarker features into numerical<br />
values. These values become distinct features and are used for comparison with<br />
clinical diagnosis. After validation by a physician, the quantitative data and rules<br />
describing biomarker features are stored in a database. This semiautomatic<br />
image processing and quantification system is designed to provide molecular<br />
pr<strong>of</strong>iling data that are more objective, more consistent, and more reproducible<br />
than completely manual or automated quantification methods. The s<strong>of</strong>tware tools<br />
process image files from slide scanners in Matlab, which is a collection <strong>of</strong><br />
various engineering processing tools. In addition, a user-friendly graphical<br />
interface allows users to give input and feedback to improve the system quality.<br />
NANOTHERAPEUTICS<br />
Most current anticancer agents do not greatly differentiate between cancerous<br />
and normal cells, leading to systemic toxicity and adverse effects. Consequently,<br />
systemic applications <strong>of</strong> these drugs <strong>of</strong>ten cause severe side effects in other tissues<br />
(such as bone marrow suppression, cardiomyopathy, and neurotoxicity), which<br />
greatly limit the maximal allowable dose <strong>of</strong> the drug. In addition, rapid elimination<br />
and widespread distribution into nontargeted organs and tissues require administration<br />
<strong>of</strong> a drug in large quantities, which is not economical and <strong>of</strong>ten complicated<br />
because <strong>of</strong> nonspecific toxicity. Nanotechnology <strong>of</strong>fers a more targeted approach<br />
and could thus provide significant benefits to cancer patients.<br />
Rapid vascularization in fast-growing cancerous tissues is known to result<br />
in leaky, defective architecture and impaired lymphatic drainage. This structure<br />
allows an enhanced permeation and retention (EPR) effect (38–42), as a result <strong>of</strong><br />
which nanoparticles accumulate at the tumor site. For such passive targeting<br />
mechanism to work, the size and surface properties <strong>of</strong> drug delivery nanoparticles<br />
must be controlled to avoid uptake by the reticuloendothelial system<br />
(RES) (43). To maximize circulation time and targeting ability, the optimal size<br />
should be less than 100 nm in diameter and the surface should be hydrophilic to<br />
circumvent clearance by macrophages. A hydrophilic surface <strong>of</strong> the nanoparticles<br />
safeguards against plasma protein adsorption, and it can be achieved<br />
through hydrophilic polymer coatings such as polyethylene glycol (PEG),<br />
poloxamines, poloxamers, polysaccharides or through the use <strong>of</strong> branched or<br />
block amphiphilic copolymers (44–47). The covalent linkage <strong>of</strong> amphiphilic<br />
copolymers (polylactic acid, polycaprolactone, and polycyanonacrylate chemically<br />
coupled to PEG) is generally preferred, as it avoids aggregation and ligand<br />
desorption when in contact with blood components.
316 Wang et al.<br />
An alternative passive targeting strategy is to utilize the unique tumor<br />
environment in a scheme called tumor-activated prodrug therapy. The drug is<br />
conjugated to a tumor-specific molecule and remains inactive until it reaches the<br />
target (48). Overexpression <strong>of</strong> the matrix metalloproteinase (MMP), MMP-2, in<br />
melanoma has been shown in a number <strong>of</strong> preclinical as well as clinical<br />
investigations. Mansour et al. (49) reported a water-soluble maleimide derivative<br />
<strong>of</strong> doxorubicin (DOX) incorporating an MMP-2 specific peptide sequence (Gly-<br />
Pro-Leu-Gly-Ile-Ala-Gly-Gln) that rapidly and selectively binds to the cysteine-<br />
34 position <strong>of</strong> circulating albumin. The albumin-DOX conjugate is efficiently<br />
and specifically cleaved by MMP-2, releasing a DOX tetrapeptide (Ile-Ala-Gly-<br />
Gln-DOX) and subsequently DOX. pH and redox potential have been also<br />
explored as drug-release triggers at the tumor site (50). Another passive targeting<br />
method is the direct local delivery <strong>of</strong> anticancer agents to tumors. This approach<br />
has the obvious advantage <strong>of</strong> excluding the drug from the systemic circulation.<br />
However, administration can be highly invasive, as it involves injections or<br />
surgical procedures. For some tumors such as lung cancers that are difficult to<br />
access, the technique is nearly impossible to use.<br />
Active targeting is usually achieved by conjugating to the nanoparticle a<br />
targeting component that provides preferential accumulation <strong>of</strong> nanoparticles in<br />
the tumor-bearing organ, in the tumor itself, individual cancer cells, or intracellular<br />
organelles inside cancer cells. This approach is based on specific<br />
interactions such as lectin-carbohydrate, ligand-receptor, and antibody-antigen<br />
(51). Lectin-carbohydrate is one <strong>of</strong> the classic examples <strong>of</strong> targeted drug delivery<br />
(52). Lectins are proteins <strong>of</strong> nonimmunological origin, which are capable <strong>of</strong><br />
recognizing and binding to glycoproteins expressed on cell surfaces. Lectin<br />
interactions with certain carbohydrates are very specific. Carbohydrate moieties<br />
can be used to target drug delivery systems to lectins (direct lectin targeting), and<br />
lectins can be used as targeting moieties to target cell surface carbohydrates<br />
(reverse lectin targeting). However, drug delivery systems based on lectincarbohydrate<br />
have mainly been developed to target whole organs (53), which can<br />
pose harm to normal cells. Therefore, in most cases, the targeting moiety is<br />
directed toward specific receptors or antigens expressed on the plasma membrane<br />
or elsewhere at the tumor site.<br />
The cell surface receptor for folate is inaccessible from the circulation to<br />
healthy cells because <strong>of</strong> its location on the apical membrane <strong>of</strong> polarized epithelia,<br />
but it is overexpressed on the surface <strong>of</strong> various cancers like ovary, brain,<br />
kidney, breast, and lung malignancies (54,55). Surface plasmon resonance<br />
studies revealed that folate-conjugated PEGylated cyanoacrylate nanoparticles<br />
had a 10-fold higher affinity for the folate receptor than the free folate ones did<br />
(56). Folate receptors are <strong>of</strong>ten organized in clusters and bind preferably to the<br />
multivalent forms <strong>of</strong> the ligand. Furthermore, confocal microscopy demonstrated<br />
selective uptake and endocytosis <strong>of</strong> folate-conjugated nanoparticles by tumor<br />
cells bearing folate receptors. Interest in exploiting folate receptor targeting<br />
in cancer therapy and diagnosis has rapidly increased, as attested by many
<strong>Cancer</strong> Nanotechnology 317<br />
conjugated systems including proteins, liposomes, imaging agents, and neutron<br />
activation compounds (54,55).<br />
For enhanced tumor-specific targeting, the differences between cancerous<br />
cells and normal cells may be exploited. By virtue <strong>of</strong> their small size, nanoparticles<br />
entail a high surface area that not only paves a way for more efficient<br />
drug release but also a better strategy for functionalization. There is a growing<br />
body <strong>of</strong> knowledge on unique cancer markers due to recent advances in proteomics<br />
and genomics. They form the basis <strong>of</strong> complex interactions between<br />
bioconjugated nanoparticles and cancer cells. Carrier design and targeting<br />
strategies may vary according to the type, developmental stage, and location <strong>of</strong><br />
cancer (57). There is much synergy between imaging and nanotechnology in<br />
biomedical applications. Many <strong>of</strong> the principles used to target delivery <strong>of</strong> drugs<br />
to cancer may also be applied to target imaging and diagnostic agents to enhance<br />
detection sensitivity in medical imaging. With engineered multifunctional<br />
nanoparticles, the full in vivo potential <strong>of</strong> cancer nanotechnology in targeted<br />
drug delivery and imaging can be realized.<br />
BIONANOINFORMATICS<br />
For the long-term goal <strong>of</strong> personalized and predictive oncology, it is important to<br />
integrate biotechnology and nanotechnology with information technology<br />
(Fig. 4). A major step in this direction is the development <strong>of</strong> the <strong>Cancer</strong> Bioinformatics<br />
Grid (caBIG), the “World Wide Web for <strong>Cancer</strong> Research” spearheaded<br />
by the U.S. National <strong>Cancer</strong> Institute (NCI) (58). Through data sharing<br />
and standardization, this national initiative aims at improving the infrastructures<br />
<strong>of</strong> health care information technology, to facilitate the process from clinical data<br />
Figure 4 Schematic diagram showing the integration <strong>of</strong> biotechnology, nanotechnology,<br />
and information technology for personalized oncology.
318 Wang et al.<br />
collection to computational data mining, and to accelerate the use <strong>of</strong> biomolecular<br />
markers for personalized treatment. The caBIG organizational structure<br />
is divided into working groups, each focused on different aspects <strong>of</strong> the<br />
interoperability problem specific to cancer research. Two overarching work<br />
groups, “Architecture” and “Vocabularies and Common Data Elements” have<br />
begun to produce detailed specifications that will guide the design <strong>of</strong> interoperable<br />
bioinformatics tools. In practice, caBIG provides a common platform<br />
from which to launch even more ambitious integrated solutions. Bioinformatics<br />
laboratories can adopt the basic infrastructure, databases, and functionality<br />
provided by caBIG working groups to fill gaps in knowledge and to carry out<br />
information management responsibilities. In addition to these shared tools and<br />
architectures, caBIG is the perfect test bed for a suite <strong>of</strong> standard tools that can<br />
be understood, verified, and used by clinicians around the world. Another major<br />
effort is <strong>Cancer</strong> Bioinformatics Infrastructure Objects (caBIO), an application<br />
programming interface (API) that was first developed as Java objects and was<br />
later extended to support various programming platforms. These objects represent<br />
the most fundamental concepts in bioinformatics research such as genes,<br />
ontology, and sequences. Also under development are network programs such as<br />
caBIONet based on Micros<strong>of</strong>t’s .NET technology for data analysis and integration<br />
(59).<br />
OUTLOOK<br />
Looking into the future, there are a number <strong>of</strong> research themes or directions that<br />
are particularly promising but require concerted effort for success. The first<br />
direction is the design and development <strong>of</strong> nanoparticles with mon<strong>of</strong>unctions,<br />
dual functions, three functions, or multiple functions. For cancer and other<br />
medical applications, important functions include imaging (single or dualmodality),<br />
therapy (single drug or combination <strong>of</strong> two or more drugs), and<br />
targeting (one or more ligands). With each added function, nanoparticles could<br />
be designed to have novel properties and applications. For example, binary<br />
nanoparticles with two functions could be developed for molecular imaging,<br />
targeted therapy, or for simultaneous imaging and therapy (but without targeting).<br />
Bioconjugated QDs with both targeting and imaging functions will be used<br />
for targeted tumor imaging and molecular pr<strong>of</strong>iling applications. Conversely,<br />
ternary nanoparticles with three functions could be designed for simultaneous<br />
imaging and therapy with targeting, targeted dual-modality imaging, or for targeted<br />
dual-drug therapy. Quaternary nanoparticles with four functions can be<br />
conceptualized in future to have the abilities <strong>of</strong> tumor targeting, dual-drug<br />
therapy, and imaging. The second direction is nanoparticle molecular pr<strong>of</strong>iling<br />
(nanotyping) for clinical oncology; i.e., the use <strong>of</strong> bioconjugated nanoparticle<br />
probes to predict cancer behavior, clinical outcome, treatment response, and<br />
individualize therapy. This should start with retrospective studies <strong>of</strong> archived<br />
specimens because the patient outcome is already known for these specimens.
<strong>Cancer</strong> Nanotechnology 319<br />
The key hypotheses to be tested are that nanotyping a panel <strong>of</strong> tumor markers<br />
will allow more accurate correlations than single tumor markers, and that the<br />
combination <strong>of</strong> nanotyping tumor gene expression and host stroma are both<br />
important in defining the aggressive phenotypes <strong>of</strong> cancer as well as determining<br />
the response <strong>of</strong> early-stage disease to treatment (chemotherapy, radiation, or<br />
surgery). The third important direction is to study nanoparticle distribution,<br />
excretion, metabolism, and pharmacodynamics in in vivo animal models. These<br />
investigations will be very important in the development <strong>of</strong> nanoparticles for<br />
clinical applications in cancer imaging or therapy.<br />
In conclusion, nanotechnology is emerging as an enabling technology for<br />
personalized oncology in which cancer detection, diagnosis, and therapy are<br />
tailored to each individual’s tumor molecular pr<strong>of</strong>ile and also for predictive<br />
oncology in which genetic or molecular markers are used to predict disease<br />
development, progression, and clinical outcomes (60,61).<br />
ACKNOWLEDGEMENTS<br />
We are grateful to Dr. Leland Chung, Dr. Ruth O’Regan, and Dr. Dong Shin for<br />
insightful discussions. Our research in cancer nanotechnology was supported by<br />
National Institutes <strong>of</strong> Health (NIH) grants (R01 CA108468-01 and U54CA119338)<br />
and the Georgia <strong>Cancer</strong> Coalition Distinguished <strong>Cancer</strong> Scholars Program (to May<br />
Dongmei Wang and Shuming Nie).<br />
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Index<br />
ABCB1 polymorphisms, 19–20<br />
Abraxane TM , 310<br />
Acute myeloid leukemic (AML) cells, 271<br />
Adenosine triphosphate (ATP), 52<br />
Adjuvant chemotherapy, 100–101, 207<br />
Adjuvant! Online, 187, 189, 205, 207<br />
Adjuvant on Line (AOL), vs. RS assay,<br />
103–104<br />
AdnaGen system, 157<br />
Adriamycin. See Doxorubicin<br />
Advancing Clinico Genomic Trials<br />
(ACGT), 207<br />
16a-[ 18 F] fluoro-17b-estradiol (FES), 298<br />
Affymetrix GeneChip, 250, 266<br />
Affymetrix microarray, 239<br />
AIB1, 171<br />
Akt, 173, 277<br />
Aldefluor 1 , 270<br />
Aldehyde dehydrogenase (ALDH), 270–271<br />
ALDH. See Aldehyde dehydrogenase<br />
(ALDH)<br />
17-allylamino-17-demethoxygeldanamycin<br />
(17AAG), 299<br />
American College <strong>of</strong> Surgeons Oncology<br />
Group (ACOSOG), 234<br />
American Joint Committee on <strong>Cancer</strong><br />
(AJCC) classification system,<br />
159–160<br />
Aminoglutethimide, 174<br />
Amonafide, 17<br />
Amphiphilic copolymers, covalent linkage<br />
<strong>of</strong>, 315<br />
Amplicons, 244<br />
Anastrozole, 32, 174<br />
chemical structure <strong>of</strong>, 175<br />
vs. tamoxifen, 177<br />
Angiogenesis<br />
PET and SPECT imaging <strong>of</strong>, 299<br />
Animal model, 299<br />
Anthracyclines<br />
CBR and, 28<br />
oxidative stress and, 28–29<br />
Antiapoptosis genes, 201, 202<br />
Anticytokeratin antibodies, 153, 154<br />
Antiestrogens, 168–174<br />
Anti-VEGF, 300<br />
API. See Application programming<br />
interface (API)<br />
Apoptosis, 298<br />
Application programming interface<br />
(API), 318<br />
Aromatase inhibitors (AI), 32, 137<br />
analysis <strong>of</strong> gene expression with,<br />
177–178<br />
chemical structures <strong>of</strong>, 175<br />
mechanisms <strong>of</strong> resistance, 175–176<br />
steroidal and nonsteroidal, 174<br />
vs. tamoxifen, 176–177<br />
323
324 Index<br />
Array CGH, 250<br />
ATAC trial, 175–177<br />
AutoMACS TM system, 153–155<br />
Automatic magnetic cell sorting system.<br />
See AutoMACS TM system<br />
Avidin-biotin complex peroxidase<br />
technique, 154<br />
BAC. See Bacterial artificial<br />
chromosomes (BAC)<br />
Bacterial artificial chromosomes (BAC),<br />
64–65<br />
b-catenin, 273<br />
Bcl-2 tumor supressor, 248<br />
BCRP1 (breast cancer resistance<br />
protein 1), 268<br />
17b-estradiol (E2), 168, 201<br />
Bioinformatics laboratories, 318<br />
Biomarkers, 51. See also <strong>Cancer</strong><br />
biomarkers<br />
Bionanoinformatics, 317–318<br />
Biopsies, gene expression pr<strong>of</strong>iling in,<br />
91–92<br />
Biotinylated random nonamers, 246<br />
Blood collection tubes, 216–217<br />
Blood tisssue, 218<br />
Bmi-1, 271–272<br />
BMI-1 oncogenic pathway, 202<br />
BOLD (blood oxygen level-dependent)<br />
MRI, 300<br />
Bone marrow<br />
cancer stem cells in, 152<br />
cytokeratin positive tumor cells in, 206<br />
micrometastasis, 154<br />
tumor cells, 154<br />
Bone mineral density, 178<br />
Bone scanning, 291, 292<br />
BRCA1, 5, 50–51, 266, 267<br />
BRCA2, 5<br />
<strong>Breast</strong> cancer<br />
genetic susceptibility, 5<br />
luminal, 198<br />
molecular classification <strong>of</strong>, 3, 198–199<br />
patients information<br />
storage and protection <strong>of</strong>, 231–232<br />
treatment with genetic<br />
polymorphism, 267<br />
<strong>Breast</strong> cancer cells<br />
circulating. See Circulating tumor cells<br />
subgroups <strong>of</strong>, 202–204<br />
<strong>Breast</strong> <strong>Cancer</strong> Intergroup, 186, 188, 190<br />
<strong>Breast</strong> International Group (BIG) 1-98<br />
study, 175<br />
<strong>Breast</strong> MRI, 295–296<br />
<strong>Breast</strong> tumor model, 269<br />
caBIG. See <strong>Cancer</strong> Bioinformatics Grid<br />
(caBIG)<br />
caBIONet, 318<br />
<strong>Cancer</strong> Bioinformatics Infrastructure<br />
Objects (caBIO) API, 318<br />
<strong>Cancer</strong> biomarkers, 310–311<br />
<strong>Cancer</strong> Biomedical Informatics Grid<br />
(caBIG), 207<br />
development <strong>of</strong>, 317<br />
organizational structure <strong>of</strong>, 318<br />
<strong>Cancer</strong> cells vs. normal cells, 317<br />
<strong>Cancer</strong> metastasis, 278<br />
<strong>Cancer</strong> Methylation Panel, 250, 251<br />
<strong>Cancer</strong>ous tissues, vascularization in, 315<br />
<strong>Cancer</strong> stem cells. See Stem cells<br />
<strong>Cancer</strong> Trials Support Unit, 192<br />
CapSure HS LCM Cap, 223, 224<br />
Carbohydrate moieties, 316<br />
Carbonyl reductase (CBR), 28<br />
Carcinogenesis, 269<br />
CAT. See Catalase<br />
Catalase (CAT), 29<br />
11 C-choline, 298<br />
CD24 breast cancer cells, 202<br />
CD44+ breast cancer cells, 202<br />
CD44 + CD24 /loo breast cancer cells,<br />
265, 266, 270<br />
CD44 expression, 270<br />
cDNA synthesis, 246, 247<br />
CellPr<strong>of</strong>ile TM kit, 153<br />
CellSearch TM system, 153<br />
for prognostic and predictive value <strong>of</strong><br />
CTCs, 158<br />
CellSpotter TM Analyzer, 153<br />
c-erbB-2, 161<br />
CGH. See Comparative genomic<br />
hybridization (CGH)<br />
Chemotherapeutic agents, 263
Index 325<br />
Chemotherapy, 159<br />
adjuvant. See Adjuvant chemotherapy<br />
cytotoxic, 161<br />
evaluation <strong>of</strong> marker, 186<br />
neoadjuvant. See Neoadjuvant<br />
chemotherapy<br />
residual clinical risk and, 102–103<br />
Chromatin immunoprecipitation (ChIP), 53<br />
Chromogenic in situ hybridization<br />
(CISH), 139<br />
Circulating female hormone, 168<br />
Circulating tumor cells (CTCs)<br />
baseline and follow-up, 159<br />
CD326-positive, 154, 155<br />
clinical application <strong>of</strong>, 160–161<br />
detection, 153–157<br />
clinical benefits <strong>of</strong>, 161<br />
future uses <strong>of</strong>, 162<br />
immunostains <strong>of</strong>, 154<br />
magnetic labeling and separation <strong>of</strong>,<br />
219–220<br />
prognostic value <strong>of</strong>, 158<br />
for staging <strong>of</strong> MBC, 159–160<br />
CISH. See Chromogenic in situ<br />
hybridization (CISH)<br />
Classification <strong>of</strong> breast tumors, 250<br />
Clinical decision making, 190<br />
Clinical Laboratory Improvement<br />
Amendments (CLIA), 190<br />
Clinical trials<br />
for antiestrogens, 168<br />
designing, 190–192<br />
protocols, 214, 228<br />
for treatment with MAPK-specific<br />
inhibitors, 172<br />
Clinicogenomic models, 207<br />
Cloned probe arrays, in CGH, 65–66<br />
Cluster analysis, 248<br />
11 C-methionine, 298<br />
CNA, Copy number abnormalities (CNA)<br />
CNB. See Core needle biopsy (CNB)<br />
Coexpression modules, 202<br />
Combined imaging, with FDG-PET and<br />
MRI, 297<br />
Common Rule, 232<br />
Comparative genomic hybridization<br />
(CGH), 62, 249<br />
BAC, 64–65<br />
[Comparative genomic hybridization<br />
(CGH)]<br />
cloned probe arrays in, 65–66<br />
DNA in, 69–71<br />
metaphase chromosomes in, 65<br />
MIP, 66–69<br />
oligonucleotide-based arrays, 65–66<br />
Complete pathological response (pCR),<br />
101–102<br />
Computed tomography (CT) scanning, 294<br />
Contingency plans, 214<br />
Copy number abnormalities (CNA), 61, 249<br />
assessment by CGH. See Comparative<br />
genomic hybridization (CGH)<br />
gene deregulation by, 62–63<br />
Coregulators, 171<br />
Core needle biopsy (CNB), 83, 238–239<br />
Cox model, 245<br />
Cox regression, 159<br />
CpG dinucleotides, 45–46<br />
Cryopreservation techniques, 235–237<br />
CTCs. See Circulating tumor cells (CTCs)<br />
Cu-ATSM, 299<br />
Cu-DOTA-VEGF, 299<br />
CXCR4 chemokine receptor, 278, 280<br />
Cycling cells, 267, 282<br />
Cyclophosphamide<br />
CYP3A4 in, 27<br />
CYP2B6 in, 26–27<br />
and GST, 27–28<br />
CYP. See Cytochrome p450s (CYP)<br />
CYP3A4, in cyclophosphamide, 27<br />
CYP1B1, 267<br />
CYP2B6, in cyclophosphamide, 26–27<br />
CYP2C8<br />
in paclitaxel, 31<br />
CYP2D6<br />
genotyping, 170<br />
in TAM, 32<br />
Cytochrome p450s (CYP), 267<br />
Cytology, 51–52<br />
Cytotoxic agents, in neoadjuvant<br />
chemotherapy, 114–115<br />
Cytotoxic therapies, 109<br />
Dandelion hypothesis, 260<br />
DASL assay, 246
326 Index<br />
Database information, and patient<br />
identifiers, 232<br />
Daunorubicin, 28<br />
DCIS. See Ductal carcinoma in situ<br />
(DCIS)<br />
Deacetylation, <strong>of</strong> histones, 47<br />
Decision aids, 187<br />
Decision boards, 187<br />
DES. See Diethylstilbestrol (DES)<br />
DFS. See Disease-free survival<br />
3D GRE technique, 296–297<br />
Diethylstilbestrol (DES), vs.<br />
tamoxifen, 168<br />
Dihydropyrimidine dehydrogenase<br />
(DPD), 18–19<br />
Discovery-based research, 185<br />
Disease-free survival (DFS), 32–36, 109<br />
defined, 192<br />
GST in, 33<br />
MnSOD, 36<br />
MTHFR in, 33<br />
SULT1A1, 36<br />
UGT2B15, 36<br />
Disseminated tumor cells (DTC), 206<br />
Distant recurrence-free survival (DRFS)<br />
defined, 192<br />
DNA, in CGH, 69–71<br />
DNA hypermethylation, 279<br />
DNA isolation, 225–226<br />
DNA methylation, 45<br />
DNMT in, 46–47<br />
inhibitors, 53–54, 55<br />
DNA methyltransferases (DNMT), 46–47<br />
DNA microarray analysis, 107–108<br />
DNA synthesis, 298<br />
4 0 ,6-doamidino-2-phenylindole<br />
(DAPI), 153<br />
Docetaxel, 19–20<br />
Doxorubicin (DOX), 28, 316<br />
DPD. See Dihydropyrimidine<br />
dehydrogenase (DPD)<br />
Drosophila, 276<br />
Drugs<br />
chemotherapy, 159<br />
development<br />
chemotherapy, 159<br />
for CTC, 161<br />
predictive markers in, 141–143<br />
DSL ligands, 276<br />
Ductal carcinoma in situ (DCIS), 174<br />
E2. See 17b-estradiol (E2)<br />
Eastern Cooperative Oncology Group<br />
(ECOG), 192<br />
EasySep TM system, 155–157<br />
EGFR. See Epidermal growth factor<br />
receptor (EGFR)<br />
Embryogenesis, 269<br />
Embryonic stem cells, 271, 279<br />
Endocrine therapy, 167–168<br />
with antiestrogens, 168–174<br />
with aromatase inhibitors,<br />
174–178<br />
Endothelial cells, 279, 280<br />
magnetic labeling and separation <strong>of</strong>,<br />
219–220<br />
Endoxifen. See 4-hydroxy-N-desmethyl<br />
tamoxifen<br />
Enzymes, metabolic, 267<br />
EpCAM. See Epithelial cell adhesion<br />
molecule-1 (EpCAM)<br />
Epidermal growth factor receptor (EGFR),<br />
140–141, 171, 172<br />
Epigenetics<br />
DNA methylation. See DNA<br />
methylation<br />
histones. See Histones<br />
Epithelial cell adhesion molecule-1<br />
(EpCAM), 153<br />
Epithelial stem cells, 263<br />
ER. See Estrogen receptors (ER)<br />
ERa, 136–138, 168<br />
gene transcription, 169<br />
ERa-positive breast cancer, 168, 173<br />
ERa-positive cells, 269<br />
ERBB2, 62, 311<br />
ER-negative breast cancer, 168, 170<br />
ER-negative tumor cells, 269–270<br />
ER-positive breast cancer, 187, 246<br />
ER-positive tumors, 198, 245<br />
Estrogen receptors (ER), 51, 108, 298<br />
ERa, 136–138, 168<br />
gene transcription, 169<br />
gene expression <strong>of</strong>, 55–56<br />
mutations, 170
Index 327<br />
European Human Frozen Tumor Tissue<br />
Bank, 234<br />
European Union, 207<br />
Exemestane, 174<br />
chemical structure <strong>of</strong>, 175<br />
ExtractSure device, 223, 224<br />
FDG PET, 293–294<br />
FES. See 16a-[ 18 F] fluoro-17b-estradiol<br />
(FES)<br />
FES PET, 298<br />
18 F-fluorodeoxyglucose (FDG), 292<br />
[ 18 F] fluorotamoxifen, 298<br />
FFPE. See Formalin-fixed<br />
paraffin-embedded (FFPE)<br />
FFPE tissues, 313<br />
Fibroblasts, 280<br />
Fibromas, 297<br />
Ficoll-Paque TM , density gradient<br />
procedure, 156<br />
Fine needle aspiration (FNA), 83, 238<br />
Fixed tissue, 218<br />
18 F-labeled paclitaxel, 298<br />
Flow cytometry, 264<br />
Fluorescence-activated cell sorter<br />
(FACS), 270, 271<br />
Fluorescence microscope, 312<br />
Fluorescent in situ hybridization<br />
(FISH), 311<br />
for HER2/neu testing, 138–139<br />
Fluorodeoxyglucose, 293<br />
5-Fluorouracil (5FU)<br />
and DPD, 18–19<br />
DPD and, 30<br />
MTHFR and, 30, 31<br />
TS and, 31<br />
TS inhibition by, 19<br />
[ 18 F]MISO ([ 18 F]fluoromisonidazole-<br />
3-fluoro-1-(2 0 -nitro-1 0 -imidazolyl)-<br />
2-propanol), 299<br />
FNA. See Fine needle aspiration (FNA)<br />
Folate receptors, 316<br />
Food and Drug Administration<br />
(FDA), 153, 174, 246,<br />
311, 3111<br />
Formalin, 215, 218<br />
cross-linking effects <strong>of</strong>, 237<br />
Formalin-fixed paraffin-embedded<br />
(FFPE) tissue<br />
extraction <strong>of</strong> RNA from tissue samples<br />
<strong>of</strong>, 98–99, 244–245<br />
RNA-based expression pr<strong>of</strong>iling <strong>of</strong>, 246<br />
Forward genetics approach, 16<br />
FOXO regulatory proteins, 277<br />
Freeze-thawing cycles, 235<br />
Fulvestrant, 174<br />
Ga-DOTA-F(ab 0 )(2)-herceptin, 299<br />
Gamma rays, 292<br />
Gamma secretase inhibitor (GSI), 276<br />
Gefitinib, 172, 311<br />
Gene expression<br />
analysis, 177–178, 244–245<br />
pr<strong>of</strong>iling. See Gene expression pr<strong>of</strong>iling<br />
70-gene expression, 108, 124–126, 205.<br />
See also Gene expression signature<br />
in MINDACT trial, 130–132<br />
prognostic value <strong>of</strong>, 126–127<br />
in RASTER trial, 130<br />
in TRANSBIG study, 127–129<br />
Gene expression analysis, 177–178,<br />
244–245<br />
Gene expression Grade Index (GGI),<br />
200–201<br />
Gene expression pr<strong>of</strong>iling, 79–81, 97–98,<br />
198–199, 246, 270<br />
and chemotherapy, 100–101, 111–114<br />
in neoadjuvant setting, 101–102<br />
for classification <strong>of</strong> breast cancer,<br />
198–199<br />
and docetaxel, 112–113<br />
oncogenic pathway analysis by, 116–118<br />
pCR prediction, 101–102<br />
RNA expression, 98–100<br />
RS in. See Recurrence score (RS) assay<br />
RT-PCR for, 99<br />
Gene expression signatures, 265–267. See<br />
also Gene expression pr<strong>of</strong>iling<br />
for improving prognosis, 199–202<br />
integration with, 202–204<br />
Gene methylation<br />
BRCA1, 50–51<br />
estrogen receptors (ER), 51<br />
RASSF1A, 49–50
328 Index<br />
21-gene pr<strong>of</strong>ile. See Oncotype DX TM<br />
76-gene pr<strong>of</strong>ile, 129<br />
Genetic polymorphisms, 266–267<br />
Genetic susceptibility, 5<br />
Gene transcription, 19<br />
Genomic alterations<br />
prognostic value <strong>of</strong>, 158<br />
Genomic Health, 190, 191<br />
Genomic Health, Inc. (GHI), 246<br />
Genomic hypomethylation, 279<br />
Genomic predictors, 204–205<br />
Germline polymorphisms, 267<br />
Gestation, 269<br />
GHI. See Genomic Health, Inc. (GHI)<br />
Gliomas, 268<br />
Glutathione-S-transferases (GST), 16, 267<br />
and cyclophosphamide, 27–28<br />
in DFS, 33<br />
Glycophorin A, 156<br />
Graphical interface, user-friendly, 315<br />
GRP (gastrin-releasing peptide)<br />
receptors, 299<br />
GSK-3b, 273<br />
Harmonization, 214<br />
HAT. See Histone acetyltransferases<br />
(HAT)<br />
Health Insurance Portability and<br />
Accountability Act, 232<br />
Hedgehog (HH), 265<br />
ligands, 276–277<br />
signaling pathway, 276–277<br />
transcription factors, Gli1 and<br />
Gli2, 277<br />
H & E (hematoxylin & eosin), 227<br />
Hematopoietic cells, human, 156<br />
Heparin-coated syringes, 215<br />
HER2. See Human epidermal growth<br />
factor receptor 2 (HER2)<br />
HER2+, 198<br />
HER3, 171<br />
HER4, 171<br />
HER2/neu, 138–139, 171–172<br />
Heterochromatin, 47<br />
HH. See Hedgehog (HH)<br />
Histone acetyltransferases (HAT), 47<br />
Histone deacetylases (HDAC), 55<br />
Histones<br />
deacetylation, 47<br />
HAT, 47<br />
HDAC. See Histone deacetylases<br />
(HDAC)<br />
methylation <strong>of</strong>, 278<br />
modifiers, 48<br />
posttranslational modification <strong>of</strong>, 47<br />
ChIP for, 53<br />
H3K9 repressors, 278<br />
Homogenization, 224<br />
Hormonal therapy, 191<br />
adjuvant, 187<br />
Hormone receptors, 161<br />
Hormone replacement therapy<br />
(HRT), 173<br />
Human epidermal growth factor receptor<br />
2 (HER2). See also HER2/neu<br />
gene amplification in CTCs, 158<br />
Hybridization. See Comparative genomic<br />
hybridization (CGH)<br />
4-hydroxy-N-desmethyl tamoxifen,<br />
20, 170<br />
Hypothesis-based research, 185<br />
IHC. See Immunohistochemistry (IHC)<br />
Illumina, Inc., 249<br />
Illumina BeadArray, 250<br />
Illumina microarrays, 249<br />
Image file processing, 315<br />
Immunohistochemistry (IHC), 135–136,<br />
227, 311, 313<br />
for ERa, 136–138<br />
for HER2/neu, 138<br />
for PR, 136–138<br />
Immunohistological staining, 311–312<br />
Indium-111, 300<br />
In situ hybridization (ISH), 227<br />
International Stage IV Stratification<br />
Study, 160<br />
International Union Against <strong>Cancer</strong><br />
(UICC), 159<br />
Invasiveness gene signature (IGS), 202<br />
Iressa. See Gefitinib<br />
Isoniazid, 16–17
Index 329<br />
Java objects, 318<br />
Kaiser Permanente health care system,<br />
188<br />
Kaplan-Meier estimator, 245<br />
Krishnamurthy, Savitri, 154<br />
Laser capture microdissection (LCM),<br />
2–3, 221–224, 239<br />
Lauterbur, Paul, 294<br />
LCM. See Laser capture microdissection<br />
(LCM)<br />
Lectin-carbohydrate, 316<br />
Lectins, 316<br />
Lethal phenotypes, 310<br />
Letrozole, 32, 174<br />
chemical structure <strong>of</strong>, 175<br />
vs. tamoxifen, 176, 177<br />
Ligand-binding domain (LBD), 170<br />
Liver tumors, 297<br />
Lobular carcinoma in situ (LCIS), 174<br />
Loss <strong>of</strong> heterozygosity (LOH), 66, 250<br />
Luminal epithelial cells, genes <strong>of</strong>, 198<br />
Lymph nodes, 297<br />
microarrays for, 3–5<br />
recurrence <strong>of</strong> breast cancer in, 245<br />
Macromolecular extraction, 237, 239<br />
Magnetic labeling and separation, <strong>of</strong><br />
tumor tissue, 219–220<br />
MammaPrint. See 70-gene expression<br />
Mammary gland, 264<br />
Mammography, 292<br />
Manganese superoxide dismutase<br />
(MnSOD), 29<br />
in DFS, 36<br />
MAPK. See Mitogen-activated protein<br />
kinase (MAPK)<br />
MassARRAY, 52<br />
Matlab slide scanners, 315<br />
Matrix metalloproteinase (MMP), 316<br />
MBC. See Metastatic breast cancer<br />
(MBC)<br />
MCF-7 breast cancer cells, 170, 172<br />
Medicare, 190<br />
Mercaptopurine, 17<br />
Metaphase chromosomes, in CGH, 65<br />
Metastatic breast cancer (MBC)<br />
development <strong>of</strong>, 151<br />
diagnosis <strong>of</strong>, 158–159<br />
stage IV, 160<br />
HER2-positive, 177, 178<br />
staging, 159–160<br />
Methotrexate (MTX), 18, 30, 31<br />
Methylation<br />
detection <strong>of</strong>, 51–53<br />
<strong>of</strong> DNA. See DNA methylation<br />
<strong>of</strong> genes. See Gene methylation<br />
pr<strong>of</strong>iling, 250–251<br />
therapy, 53–55<br />
Methyltetrahydr<strong>of</strong>olate reductase<br />
(MTHFR), 18<br />
in DFS, 33<br />
and 5-Fluorouracil (5FU), 30, 31<br />
Microarray for Node-Negative Disease<br />
may Avoid Chemotherapy<br />
(MINDACT) trial, 130–132, 205<br />
Microarray Quality Control Project<br />
(MAQC), 82<br />
Microarrays<br />
for lymph node status, 3–5<br />
with OCT, 236<br />
with paraffin-embeded tissue, 236–237<br />
RNA expression, 2, 4<br />
with RNA stabilization buffers,<br />
237–238<br />
technical reproducibility <strong>of</strong>, 82–83<br />
using CNB, 238–239<br />
using FNA, 238<br />
using LCM, 239<br />
Micrometastatic carcinoma, 154<br />
Micrometastatic cells, 206<br />
microRNAs, 206<br />
Micros<strong>of</strong>t Excel, 228<br />
MIP. See Molecular inversion probe (MIP)<br />
miRNA pr<strong>of</strong>iling, 248–249<br />
Mitogen-activated protein kinase<br />
(MAPK), 172<br />
MMP. See Matrix metalloproteinase<br />
(MMP)<br />
MMP-2, 316
330 Index<br />
MnSOD. See Manganese superoxide<br />
dismutase (MnSOD)<br />
Molecular classification, <strong>of</strong> breast<br />
cancer, 3<br />
Molecular diagnostic test, 186, 188,<br />
190, 311<br />
Molecular epidemiologic studies, 32–36<br />
Molecular imaging, 291–292. See also<br />
Positron emission tomography<br />
(PET)<br />
Molecular inversion probe (MIP), 66–69<br />
PCR in, 67<br />
Molecular markers, 79, 160, 188–190<br />
defined, 185–186<br />
development process <strong>of</strong>, 81–82<br />
in stem cells, 265, 273<br />
Molecular pr<strong>of</strong>iling<br />
defined, 185<br />
developmental technologies for, 311<br />
Molecular signatures, 186<br />
Monoclonal antibodies, 153, 156<br />
Monocytes, 219<br />
MPO. See Myeloperoxidase (MPO)<br />
MRI, 294–297<br />
applications for, 297<br />
mRNAs, 245<br />
MTA-1 (Manual Tissue Arrayer), 226<br />
MTHFR. See Methyltetrahydr<strong>of</strong>olate<br />
reductase (MTHFR)<br />
mTOR (mammalian targets <strong>of</strong><br />
rapamycin), 276<br />
MTX. See Methotrexate<br />
Mutations<br />
estrogen receptors, 170<br />
Myeloperoxidase (MPO), 29<br />
N-acetyltransferases, 16, 267<br />
Nanoparticles. See also Nanotechnology<br />
bioconjugated, 311, 317<br />
design and development <strong>of</strong>, 318<br />
molecular pr<strong>of</strong>iling. See Nanotyping<br />
superparamagnetic iron oxide, 310<br />
Nanotechnology, 311<br />
for cancer detection, diagnosis, and<br />
therapy, 319<br />
QD, 312<br />
Nanotherapeutics, 315–317<br />
Nanotyping, 311–315, 318–319<br />
National <strong>Cancer</strong> Institute (NCI), 186,<br />
207, 317<br />
National Institutes <strong>of</strong> Health (NIH),<br />
199, 200<br />
cancer guidelines, 205<br />
National Surgical Adjuvant <strong>Breast</strong> and<br />
Bowel Project (NSABP), 99–100,<br />
188, 190, 192, 245<br />
N-CoR, 171<br />
Neoadjuvant chemotherapy, 5–7, 78,<br />
109–111<br />
cytotoxic agents in, 114–115<br />
predictive markers <strong>of</strong> response to,<br />
101–102<br />
response predictors <strong>of</strong>, 78–79<br />
use <strong>of</strong>, 293<br />
Netherlands <strong>Cancer</strong> Institute (NKI), 199<br />
.NET technology, 318<br />
Noncanonical pathways. See Wnt<br />
signaling<br />
Nonnucleoside inhibitors, 53, 54<br />
Non-small cell lung cancer (NSCLC), 310<br />
Nontumorigenic cells, 265<br />
North American <strong>Breast</strong> Intergroup, 192<br />
Notch receptors, 276<br />
Notch signaling pathway, 264, 276<br />
Nottingham Prognostic Index, 205<br />
NSABP. See National Surgical Adjuvant<br />
<strong>Breast</strong> and Bowel Project<br />
(NSABP)<br />
Nucleic acids<br />
amplification, 238<br />
collection, 215<br />
extraction, 237<br />
pr<strong>of</strong>ilation, 251<br />
Nucleoside inhibitors, 53, 54<br />
NuGEN Technologies, Inc., 249<br />
OCT. See Optimal cutting temperature<br />
(OCT)<br />
OCT-4, 271<br />
Octreoscan, 299<br />
[ 15 O]H 2 O PET, 292<br />
4OHT (4-hydroxytamoxifen), 171<br />
Oligonucleotide-based arrays,<br />
in CGH, 65–66
Index 331<br />
Oligonucleotides, 247<br />
Oncogenes, 62, 251<br />
Oncogenic pathways, deregulation <strong>of</strong>,<br />
116–118<br />
cytotoxic agents, 118<br />
Oncologists, 207<br />
Oncotype DX assay, 188, 190, 192, 194,<br />
200, 205, 246<br />
Optimal cutting temperature (OCT),<br />
218, 236<br />
p53<br />
signature genes, 201<br />
tumor suppressor, 248<br />
PACCT. See Program for the assessment<br />
<strong>of</strong> clinical cancer tests (PACCT)<br />
Paclitaxel, 19–20, 267<br />
CYP2C8 in, 31<br />
Paget, Stephen, 278<br />
Papanicolaou (Pap)-stained cytospin, 154<br />
Paraffin, 222, 227<br />
Paraffin-embedded tissue, 237–238<br />
Paraffin wax, discovery <strong>of</strong>, 237<br />
Partial signatures, 204<br />
Pathologic report, 234<br />
Pathologist, 232, 233<br />
Patient identifiers, 232<br />
PAXgene, 220–221<br />
p21 Cip1/Waf1 , 277<br />
PCR. See Polymerase chain reaction<br />
(PCR); Polymerase chain reaction<br />
(PCR), in MIP<br />
pCR. See Complete pathological response<br />
(pCR)<br />
Peptides, 299<br />
Peripheral blood<br />
cancer stem cells in, 152<br />
extraction <strong>of</strong> RNA from, 220–221<br />
isolation <strong>of</strong> monocytes in, 219<br />
separation from mononuclear cells, 156<br />
Peripheral blood mononuclear cells<br />
(PBMC), 219<br />
PET. See Positron emission tomography<br />
(PET)<br />
PET-MRI scanner, 300<br />
PET scanners, 297<br />
<strong>Ph</strong>armacodynamics, 13<br />
<strong>Ph</strong>armacoepidemiology, 14<br />
<strong><strong>Ph</strong>armacogenetics</strong>, 14, 206<br />
molecular epidemiologic studies, 32–36<br />
<strong>Ph</strong>armacogenomic predictors, 79–80<br />
published studies, 86–90<br />
validation studies <strong>of</strong>, 84–85<br />
<strong>Ph</strong>armacokinetics, 13<br />
<strong>Ph</strong>armacology, 13–14<br />
<strong>Ph</strong>osphatase and tensin homolog deleted<br />
on chromosome ten (PTEN),<br />
265, 277<br />
<strong>Ph</strong>osphate buffered saline (PBS), 215<br />
<strong>Ph</strong>osphatidylinositol-3-OH kinase (PI3K),<br />
173, 277<br />
Polycomb group (PcG) proteins, 46,<br />
261, 279<br />
Polymerase chain reaction (PCR), 236<br />
detection <strong>of</strong> tumor cells, 158<br />
in MIP, 67<br />
Positron emission tomography (PET),<br />
206, 292<br />
Posttranslational modification,<br />
<strong>of</strong> histones, 47<br />
PR. See Progesterone receptor (PR)<br />
Predictive markers<br />
defined, 136<br />
in drug development, 141–143<br />
ERa, 136–138<br />
HER2/neu, 138–139<br />
PR, 136–138<br />
Preoperative chemotherapy. See<br />
Neoadjuvant chemotherapy<br />
Preoperative systemic chemotherapy<br />
(PST). See Neoadjuvant<br />
chemotherapy<br />
Preregistration, 191<br />
Preservatives, 214, 215<br />
Primary study group, 192<br />
Privacy rights, 231<br />
Progenitor cells, mutated, 262, 266<br />
Progesterone receptor (PR), 136–138,<br />
176–177<br />
Prognosis, in breast cancer<br />
improving, 199–202<br />
Prognostic marker, 199<br />
Program for the assessment <strong>of</strong> clinical<br />
cancer tests (PACCT), 186<br />
Proliferation genes, 201, 202, 204
332 Index<br />
Prostate cancer, RNA signatures in, 248<br />
Proteinase digestion buffers, 237<br />
Protein biomarkers, 313. See also <strong>Cancer</strong><br />
biomarkers<br />
Protein collection, 215<br />
Protein isolation, 225–226<br />
Proteomics, 206<br />
PTEN. See <strong>Ph</strong>osphatase and tensin<br />
homolog deleted on chromosome<br />
ten (PTEN)<br />
PTEN pathway, 277–278<br />
Pyrosequencing, 52–53<br />
QD-Ab, 312<br />
QDs. See Quantum dots (QDs)<br />
Q-IHC tools, 314<br />
qRT-PCR (realtime quantitative-PCR)<br />
for screening tumor samples for<br />
miRNA expression, 248<br />
Quantification system, 315<br />
Quantum dots (QDs)<br />
multicolor, 312<br />
nanotechnology, 312<br />
semiconductor, 311<br />
Radiation, resistance to<br />
(Radioresistance), 266<br />
Radionuclide techniques, 292–294<br />
vs. optical imaging, 299<br />
RAKE (RNA-primed array-based Klenow<br />
extension) analysis study, 249<br />
Randomization, 192<br />
Rapamycin, molecular targets <strong>of</strong>. See<br />
MTOR (mammalian targets <strong>of</strong><br />
rapamycin)<br />
RASSF1A, 49–50<br />
RASTER trial, 130<br />
Rationally designed inhibitors, 53,<br />
54, 55<br />
Reactive oxygen species (ROS), 27<br />
and mitochondrial permeability, 29<br />
Recurrence score (RS), 100, 200, 245<br />
and adjuvant chemotherapy, 100–101<br />
clinical utility <strong>of</strong>, 103–104<br />
GHI, 246<br />
for node positive patients, 104<br />
[Recurrence score (RS)]<br />
and pCR, 101<br />
to predict recurrence <strong>of</strong> breast<br />
cancer, 245<br />
vs. AOL, 103–104<br />
REMARK guidelines, 186<br />
Reporter genes, 299<br />
Response predictors, <strong>of</strong> neoadjuvant<br />
chemotherapy, 5–7, 78–79<br />
molecular markers as, 79<br />
Reticuloendothelial system (RES), 315<br />
Reverse transcriptase-polymerase chain<br />
reaction (RT-PCR), 99, 244<br />
assay-based evaluation <strong>of</strong> ER<br />
expression, 189<br />
and gene signature, 200<br />
RNA<br />
amplification, 237–238<br />
degradation, 235, 247<br />
expression microarray, 2, 4<br />
hybridization, 238, 239<br />
isolation, 224–225<br />
RNAlater, 218, 236<br />
RNA stabilization buffers, 236–237<br />
RosetteSep TM method, 156<br />
Rotterdam group, 199<br />
RPMI (Roswell Park Memorial Institute)<br />
media, 218<br />
RS. See Recurrence score (RS) assay<br />
RT-PCR assay, 245<br />
Sample handling, 237<br />
Selective ER downregulators (SERDs),<br />
173, 174<br />
Selective ER modulators (SERMs), 173<br />
Semiautomatic image processing, 315<br />
Single nucleotide polymorphism (SNP),<br />
14–15, 249<br />
pr<strong>of</strong>iling, 250<br />
Single-photon imaging, 292<br />
SMRT, 171<br />
SNP. See Single nucleotide polymorphism<br />
(SNP)<br />
Sonic hedgehog (SHH), 276, 277<br />
SRC-1, 171<br />
St. Gallen classification, 246<br />
St. Gallen criteria, 199, 200
Index 333<br />
St. Gallen guidelines, 205<br />
Standard operating procedures (SOPs)<br />
for collection <strong>of</strong> tissue samples, 234<br />
for RNA degradation, 235<br />
STAR (Study <strong>of</strong> Tamoxifen and<br />
Raloxifene) trial, 173<br />
STAT3 transcription factor, 276<br />
Stem cell lines, derivation <strong>of</strong>, 266<br />
Stem cell niche, 261, 263, 268<br />
molecular targets <strong>of</strong>, 278–280<br />
Stem cell regulation, 280<br />
Stem cells<br />
cellular proliferation, 261<br />
clinical research on, 264–265<br />
differentiation <strong>of</strong>, 261, 262<br />
division <strong>of</strong>, 276<br />
drug resistance <strong>of</strong>, 268<br />
embryonic, 271, 279<br />
functional targets, 269–273<br />
gene expression pr<strong>of</strong>iling <strong>of</strong>, 270<br />
hematopoietic, 278, 280<br />
hypothesis, 260–264, 268<br />
intestinal, 277<br />
leukemic, 278<br />
mesenchymal, 280<br />
molecular markers, 265, 273<br />
mutations, 263, 270, 278<br />
nontumorigenic daughter cells <strong>of</strong>, 261<br />
Stem cell sanctuaries, 267–269<br />
Stem cell survival, neural, 279, 280<br />
Stochastic model, 260, 261<br />
SULT1A1<br />
in DFS, 36<br />
in TAM, 32<br />
Superoxide dismutases (SODs), 267<br />
Swiss Institute <strong>of</strong> Bioinformatics, 202<br />
TAM. See Tamoxifen (TAM)<br />
Tamoxifen (TAM), 20, 31–32, 56, 137<br />
adjuvant, 169<br />
CYP2D6 in, 32<br />
metabolizers <strong>of</strong>, 170<br />
SULT1A1 in, 32<br />
UGT2B15 in, 32<br />
vs. aromatase inhibitors, 176–177<br />
vs. DES, 168<br />
TaqMan TM array, 248–249<br />
Taxanes, 19–20<br />
CYP2C8 in, 31<br />
Thiopurine methyltransferase (TPMT),<br />
17–18<br />
Thymidylate synthase (TS), 19<br />
Tissue<br />
acquisition <strong>of</strong>, 232–235<br />
biopsies, 218<br />
collection, 214–218<br />
control blocks, 228<br />
defined, 213<br />
processing, 218<br />
procurement, 214<br />
Tissue banking, 231–232<br />
Tissue microarray (TMA), 226–227<br />
blocks, 228, 229, 230<br />
mapping, 227, 228<br />
TMA. See Tissue microarray (TMA)<br />
TRANSBIG study, 127–129, 199–200<br />
Transcriptional regulation, 277<br />
Trastuzumab, 138, 172, 310–311<br />
Trial Assigning Individualized Options for<br />
Treatment (TAILORx) trial, 178, 186<br />
designing <strong>of</strong>, 191<br />
recurrence score ranges in, 192–194<br />
TRI reagent<br />
for isolation <strong>of</strong> DNA, 225–226<br />
for isolation <strong>of</strong> RNA, 224–225<br />
for tissue microarray creation, 226–230<br />
TRIZOL method, 224–230<br />
TS. See Thymidylate synthase (TS)<br />
Tumor-activated prodrug therapy, 316<br />
Tumor growth factor-b (TGF-b), 280<br />
Tumor hypoxia, 299<br />
Tumorigenesis, 250, 273, 274<br />
Tumors<br />
detection <strong>of</strong>, 158<br />
invasion, 202<br />
regression, 260<br />
size and growth, 238, 239<br />
Tumor suppressor genes, 251<br />
Tumor tissue. See Tissue<br />
UGT2B15<br />
in DFS, 36<br />
in TAM, 32<br />
Ultrasound, 292
334 Index<br />
Vacutainers, use <strong>of</strong>, 215<br />
Vascular endothelial growth factor<br />
(VEGF), 279, 280<br />
Vascularization, 315<br />
VEGF. See Vascular endothelial growth<br />
factor (VEGF)<br />
White blood cells. See Monocytes<br />
Whole-genome expression pr<strong>of</strong>iling, 249<br />
Wnt/b-catenin, 272<br />
Wnt signaling, 266, 273<br />
World Wide Web for <strong>Cancer</strong> Research,<br />
317<br />
Wound-healing, gene phenotype, 200<br />
WWOX expression, 56<br />
Xenograft model, 265<br />
Xenotransplantation, 264, 269<br />
X-ray mammography, 297<br />
Xylene, 218
Oncology<br />
about the book…<br />
<strong><strong>Ph</strong>armacogenetics</strong> is becoming increasingly relevant in the diagnosis, treatment,<br />
and recovery <strong>of</strong> cancer patients. A major problem facing oncologists is the outstanding<br />
varied efficacy <strong>of</strong> treatment. Promising advances in pharmacogenetics<br />
have allowed the development <strong>of</strong> effective agents which will enable personalized<br />
cancer chemotherapy to become routine for the clinical practice.<br />
Written by experts in the field and combining information that is unable to be<br />
found in a single source volume, <strong>Ph</strong>armocogenetics <strong>of</strong> <strong>Breast</strong> <strong>Cancer</strong>:<br />
• combines a complete overview <strong>of</strong> pharmacogenetics and how it relates to<br />
breast oncology for diagnosis, treatment, and the recovery <strong>of</strong> patients using<br />
an individualized therapy model<br />
• is the first single source reference demonstrating the application <strong>of</strong><br />
pharmacogenetics in the care <strong>of</strong> breast cancer patients<br />
• enables physicians a coherent interpretation <strong>of</strong> the emerging science <strong>of</strong><br />
pharmacogenetics, aiding them to incorporate individual therapies in their<br />
own practice<br />
• gives practical guidance on various forms <strong>of</strong> specimen collection, tissue<br />
selection, and handling procedures<br />
about the editor...<br />
BRIAN LEYLAND-JONES is Associate Vice President and Director, Emory Winship<br />
<strong>Cancer</strong> Institute, Emory University, Atlanta, Georgia. Dr. Leyland-Jones’ main<br />
research interests are pharmacodynamics, pharmacokinetics, and pharmacogenetics<br />
in oncological clinical trials; translation <strong>of</strong> preclinical models into the clinic;<br />
biomarker endpoints in <strong>Ph</strong>ase I/II clinical trials; and screening and mechanistic<br />
studies <strong>of</strong> novel targeted and chemotherapeutic anti-cancer agents. He has<br />
authored more than 125 peer-reviewed articles and book contributions, 150<br />
abstracts, and 29 patents.<br />
Printed in the United States <strong>of</strong> America<br />
H8637