Chapter 2 - University of British Columbia

DEVELOPMENT AND APPLICATION OF AN INTEGRATIVE GENOMICS APPROACH TO
LUNG CANCER
by
RAJAGOPAL CHARI
B.Sc., University of British Columbia, 2001
B.Sc., University of British Columbia, 2004
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
in
THE FACULTY OF GRADUATE STUDIES
(Pathology and Laboratory Medicine)
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
June 2010
© Rajagopal Chari, 2010

Abstract
Lung cancer has the highest mortality rate amongst all diagnosed malignancies with
adenocarcinoma (AC) being the most commonly diagnosed subtype of this disease in North
America. The dismal survival statistics of lung cancer patients are largely due to the detection
of the disease at an advanced stage and to a lesser extent, the limited efficacy of current front
line treatments.
Genomic approaches, namely gene expression analysis, have provided tremendous insight into
lung cancer. While many gene expression changes have been identified, most changes are
likely reactive to changes which have a primary role in cancer development. Moreover, one
feature which can discern primary from reactive changes is the presence of concordant DNA
level alteration.
Many well known genes involved in cancer such as TP53 and CDKN2A have been shown to be
affected by multiple mechanisms of alteration such as somatic mutation in or loss of DNA
sequence. For a given gene, one tumor may be affected by one mechanism while another
tumor may be affected by a different mechanism. Although this level of multi-dimensional
analysis has been performed for specific genes, such analysis has not been done at the
genome-wide level.
This thesis highlights the development and application of a multi-dimensional genetic and
epigenetic approach to identify frequently aberrant genes and pathways in lung AC. I present,
first, the design and implementation of the system for integrative genomic multi-dimensional
analysis of cancer genomes, epigenomes and transcriptomes (SIGMA 2 ). Next, analyzing a
multi-dimensional dataset generated from ten lung AC specimens with non-malignant controls, I
identified novel genes and pathways that would have been missed if a non-integrative approach
were used. Finally, examining genes involved with EGFR signaling, I identified a gene, signal
receptor protein alpha (SIRPA), which had not been previously shown to be associated with
lung cancer.
Taken together, these findings demonstrate the power of a multi-dimensional approach to
identify important genes and pathways in lung cancer. Moreover, identifying key genes using a
multi-dimensional approach on a small sample set suggests the need of large datasets may be
circumvented by using a more comprehensive approach on a smaller set of samples.
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Table of Contents
Abstract ......................................................................................................................................... ii
Table of Contents ......................................................................................................................... iii
List of Tables ............................................................................................................................... vii
List of Figures ............................................................................................................................. viii
List of Abbreviations ...................................................................................................................... x
Acknowledgements ..................................................................................................................... xii
Dedication ................................................................................................................................... xiii
Co-Authorship Statement ........................................................................................................... xiv
Chapter 1: Introduction ................................................................................................................. 1
1.1 Lung cancer ......................................................................................................................... 2
1.2 Genomic profiling of lung cancer ......................................................................................... 3
1.2.1 Gene expression analysis ............................................................................................. 3
1.2.2 DNA copy number analysis .......................................................................................... 4
1.2.3 Loss of heterozygosity (LOH) and allelic imbalance ..................................................... 5
1.3 Somatic mutations in lung cancer ....................................................................................... 5
1.4 Epigenetic alterations in lung cancer ................................................................................... 6
1.4.1 DNA methylation ........................................................................................................... 6
1.5 Current level of integrative analysis .................................................................................... 7
1.6 Need for an integrative approach to study lung cancer ....................................................... 7
1.7 Bioinformatic tools for genomic analysis ............................................................................. 8
1.8 Thesis theme ....................................................................................................................... 9
1.9 Objectives and hypothesis .................................................................................................. 9
1.10 Specific aims and outline of thesis .................................................................................. 10
1.11 Description of high throughput data in this thesis ............................................................ 13
1.12 Other relevant contributions not included as chapters in this thesis ............................... 13
1.12.1 Development of tools for genomic analysis .............................................................. 14
1.12.2 Baseline gene expression in non-malignant lung tissue ........................................... 14
1.12.3 Differential gene expression analysis in lung cancer ................................................ 15
1.12.4 Integration of gene dosage and gene expression in lung cancer ............................. 16
1.13 References ...................................................................................................................... 18
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Chapter 2: SIGMA 2 : A system for the integrative genomic multi-dimensional analysis of cancer
genomes, epigenomes, and transcriptomes 1 .............................................................................. 24
2.1 Introduction ........................................................................................................................ 25
2.2 Implementation .................................................................................................................. 26
2.3 Results and discussion ...................................................................................................... 26
2.3.1 Look and feel of SIGMA 2 ............................................................................................ 26
2.3.2 Description of application scope and functionality ...................................................... 27
2.3.3 Approach to integration between array platforms and assays .................................... 27
2.3.4 Format requirements of input data .............................................................................. 27
2.3.5 Description of user interface ....................................................................................... 28
2.3.6 Analysis of data from a single assay type ................................................................... 29
2.3.7 Analysis of data from multiple assays in a given 'omics dimension ............................ 30
2.3.8 Combinatorial analysis of multiple 'omics dimensions - gene dosage and gene
expression ........................................................................................................................... 30
2.3.9 Group comparison analysis - single ‘omics dimension ............................................... 31
2.3.10 Group comparison analysis - integrating multiple 'omics dimensions ....................... 31
2.3.11 Multi-dimensional analysis of a breast cancer genome ............................................ 31
2.3.12 Exporting data and results ........................................................................................ 32
2.4 Conclusions ....................................................................................................................... 32
2.5 Availability and requirements ............................................................................................ 33
2.6 References ........................................................................................................................ 46
Chapter 3: An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant
genes and pathways in cancer 2 .................................................................................................. 48
3.1 Background ....................................................................................................................... 49
3.2 Methods ............................................................................................................................. 50
3.2.1 Data generation and acquisition ................................................................................. 50
3.2.2 Data processing and normalization ............................................................................ 51
3.2.3 Strategy for integrative analysis .................................................................................. 52
3.2.4 Multiple concerted disruption (MCD) analysis ............................................................ 53
3.2.5 Simulated data analysis .............................................................................................. 54
3.2.6 Pathway enrichment analysis ..................................................................................... 54
3.2.6 Survival and differential gene expression analysis in publicly available datasets....... 55
3.3 Results and discussion ...................................................................................................... 55
3.3.1 Analysis of individual genomic dimensions ................................................................. 55
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3.3.2 Multi-dimensional analysis (MDA) reveals a higher proportion of intra-sample
deregulated gene expression can be explained when more dimensions are analyzed ....... 56
3.3.3 MDA reveals genes are disrupted at higher frequencies when examining multiple
dimensions as compared to any single dimension alone .................................................... 56
3.3.4 MDA identifies significantly enriched cancer related pathways .................................. 58
3.3.5 MDA of the Neuregulin signaling pathway reveals a complex pattern of deregulation
............................................................................................................................................. 59
3.3.6 Genes exhibiting multiple concerted disruption (MCD) - biological and clinical
significance .......................................................................................................................... 60
3.3.7 Association of genes exhibiting MCD and triple negative breast cancers (TNBC) ..... 62
3.4 Conclusions ....................................................................................................................... 63
3.5 References ........................................................................................................................ 75
Chapter 4: Uniparental disomy is a prevalent genetic mechanism of oncogene disruption in lung
adenocarcinoma 3 ........................................................................................................................ 79
4.1 Introduction ........................................................................................................................ 80
4.2 Methods ............................................................................................................................. 81
4.2.1 Genome wide profiling of clinical lung adenocarcinoma specimens ........................... 81
4.2.2 Determination of regions of uniparental disomy (UPD) in clinical lung tumors ........... 81
4.2.3 Determining frequent regions of UPD, gain and loss .................................................. 82
4.2.4 Determination of UPD in cancer cell lines .................................................................. 82
4.2.5 Expression analysis of genes in focal regions of UPD ............................................... 82
4.3 Results .............................................................................................................................. 83
4.3.1 Detection of UPD using allele specific copy number analysis .................................... 83
4.3.2 UPD is prevalent and non-random in the lung cancer genome with comparable
frequencies to gain and loss ................................................................................................ 83
4.3.3 Overlap of major oncogenes and tumor suppressor genes in regions of gain, loss, and
UPD ..................................................................................................................................... 84
4.3.4 UPD is prevalent at oncogenes across multiple cancer types .................................... 84
4.3.5 Identification of novel candidate oncogenes using focal regions of UPD ................... 85
4.4 Discussion ......................................................................................................................... 85
4.5 Conclusion ......................................................................................................................... 87
4.6 References ...................................................................................................................... 108
Chapter 5: Integrating the multiple dimensions of genomic and epigenomic landscapes of
cancer 4 ...................................................................................................................................... 111
5.1 Introduction ...................................................................................................................... 112
v

5.2 Genomic alterations ........................................................................................................ 113
5.2.1 Chromosomal aberrations ........................................................................................ 113
5.2.2 Gene dosage, allelic imbalance, mutational status ................................................... 113
5.2.3 Genomic landscape: Gains, losses and uniparental disomy .................................... 116
5.3 Epigenomic alterations .................................................................................................... 117
5.3.1 The cancer methylome ............................................................................................. 117
5.3.2 Integration of cancer genomic and epigenomic events ............................................ 119
5.4 Relating genetic and epigenetic events to changes in the transcriptome through
integrative analysis ................................................................................................................ 120
5.4.1 Multiple mechanisms of gene disruption ................................................................... 121
5.4.2 Multiple mechanisms of disrupting non-coding RNA levels ...................................... 121
5.4.3 Multi-dimensional integration of genome, epigenome, and transcriptome ............... 122
5.4.4 Disruption of multiple components in biological pathways ........................................ 124
5.4.5 Identification of a novel gene involved with EGFR signaling deregulated in
adenocarcinoma ................................................................................................................ 125
5.4.6 Prevalence of SIRPA deregulation and association with clinical characteristics ...... 126
5.5 Tracking clonal expansion in spatial dimensions ............................................................ 127
5.6 Evaluating the biological significance of integrative genomics findings .......................... 127
5.5 References ...................................................................................................................... 144
Chapter 6: Conclusions ............................................................................................................. 162
6.1 Summary ......................................................................................................................... 163
6.1.1 Development of the integrative genetic and epigenetic approach ............................ 163
6.1.2 Identification of a prevalent genetic alteration in lung adenocarcinoma ................... 164
6.1.3 Application of the integrative approach to lung adenocarcinoma specimens ........... 165
6.2 Conclusions ..................................................................................................................... 166
6.3 Future directions .............................................................................................................. 168
6.4 References ...................................................................................................................... 171
APPENDIX I: List of publications .............................................................................................. 174
APPENDIX II: Description of cell lines ...................................................................................... 183
APPENDIX III: Sources of data ................................................................................................. 184
APPENDIX IV: MCD strategy and Kaplan-Meier analysis of TUSC3 ....................................... 185
APPENDIX V: Kaplan-Meier and Oncomine expression analysis of frequent MCD genes ...... 186
APPENDIX VI: Summary of Kaplan-Meier survival analysis .................................................... 188
APPENDIX VII: Copy of UBC Research Ethics Board certificate of approval........................... 189
vi

List of Tables
Table 2.1. Features required for integrative analysis .................................................................. 44
Table 2.2. Summary of Input, analysis, output for each dimension ............................................ 45
Table 4.1. Regions of the genome exhibiting frequent UPD ....................................................... 99
Table 4.2. List of major oncogenes and tumor suppressor genes assessed ............................ 101
Table 4.3. Overlap of oncogenes in frequent regions of genomic alteration ............................. 102
Table 4.4. Overlap of tumor suppressor genes in frequent regions of genomic alteration ....... 103
Table 4.5. Cell lines and oncogene loci with homozygous mutation ......................................... 104
Table 4.6. Summary of homozygous mutation analysis in cancer cell lines ............................. 105
Table 4.7. RefSeq genes in focal regions of UPD .................................................................... 106
Table 4.8. Genes overexpressed in focal regions of UPD ........................................................ 107
Table 5.1. List of software for integrative analysis .................................................................... 141
Table 5.2. List of genomic resources and databases ............................................................... 142
Table 5.3. Genes interacting with SIRPA as identified by network analysis ............................. 143
vii

List of Figures
Figure 1.1. Multiple mechanisms of alteration leading to same downstream consequences ..... 17
Figure 2.1. Main structural components of SIGMA2. .................................................................. 34
Figure 2.2. Data structure hierarchy. .......................................................................................... 35
Figure 2.3. Algorithm for integrating between different array platforms ...................................... 36
Figure 2.4. SIGMA2 interface. .................................................................................................... 37
Figure 2.5. Consensus calling and heterogeneous array analysis. ............................................ 38
Figure 2.6. Integrative genetic analysis of HCC2218 .................................................................. 40
Figure 2.7. Two-group two dimensional comparison of 37 NSCLC and 16 SCLC cancer cell
lines. ............................................................................................................................................ 41
Figure 2.8. Multi-dimensional perspective of chromosome 17 of the HCC2218 breast cancer cell
line. ............................................................................................................................................. 42
Figure 3.1. Genomic profiles of breast cancer cell lines. ............................................................ 65
Figure 3.2. Quantitative and qualitative benefits of integrative analyses. ................................... 66
Figure 3.3. Determination and application of a disruption frequency threshold. ......................... 68
Figure 3.4. Impact of multi-dimensional analysis on low frequency events. ............................... 69
Figure 3.5. Pathway analysis of the 1162 genes identified by multi-dimensional analysis. ........ 70
Figure 3.6. Complex deregulation of the Neuregulin/ERBB2 signaling pathway. ....................... 71
Figure 3.7. Deregulation of PTEN occurs differently between samples. ..................................... 72
Figure 3.8. Multiple concerted disruption (MCD) analysis and its application to triple negative
breast cancer. ............................................................................................................................. 73
Figure 4.1. Detection of UPD using allele specific copy number. ............................................... 88
Figure 4.2. Comparison of frequent regions of gain, loss and UPD in the lung adenocarcinoma
genome ....................................................................................................................................... 90
Figure 4.3. Venn diagram illustrating the amount of the genome covered by gain, loss, and UPD
.................................................................................................................................................... 92
Figure 4.4. Genomic profile of an individual lung adenocarcinoma sample ............................... 93
Figure 4.5. Examination of UPD events at the KRAS and RB1 loci ............................................ 95
Figure 4.6. Relationship of homozygous mutation at oncogenes and genomic alteration .......... 96
Figure 4.7. Identification of E2F3 in a focal region of UPD ......................................................... 97
Figure 5.1. Advances in cancer genomic landscape post Y2K. ................................................ 129
Figure 5.2. SNP array analysis to identify areas of altered copy number and allelic composition
in a clinical lung cancer specimen. ........................................................................................... 130
viii

Figure 5.3. Overlay of chromosomal regions of gain, loss and UPD (copy number neutral LOH)
inherent to the T47D breast cancer cell line. ............................................................................ 131
Figure 5.4. Integration of copy number, allelic status, DNA methylation, and gene expression for
a single lung adenocarcinoma sample. ..................................................................................... 132
Figure 5.5. Integration of copy number, allelic status, DNA methylation, and gene expression for
a single lung adenocarcinoma sample. ..................................................................................... 134
Figure 5.6. Identification of multiple disrupted components in a biological pathway. ................ 136
Figure 5.7. Multi-dimensional analysis of the epidermal growth factor receptor signaling
pathway. .................................................................................................................................... 137
Figure 5.8. Prevalence of SIRPA underexpression and its relationship with PTPN6 and smoking
status. ....................................................................................................................................... 138
Figure 5.9. Kaplan-Meier analysis of SIRPA in four independent microarray datasets. ........... 139
Figure 5.10. Automated detection of selected clonal populations of cells within a cancer biopsy
tissue section. ........................................................................................................................... 140
ix

List of Abbreviations
Abbreviation Definition
AC Adenocarcinoma
ASCN Allele specific copy number
BRAF v-raf murine sarcoma viral oncogene homolog B1
CDKN2A Cyclin-dependent kinase inhibitor 2A
CGH Comparative Genomic Hybridization
CNV Copy number variation
DNA Deoxyribonucleic Acid
EGFR Epidermal Growth Factor Receptor
FISH Fluorescence in-situ hybridization
GWAS Genome wide association studies
KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene
LOH Loss of Heterozygosity
MASI Mutant allele specific imbalance
MCD Multiple Concerted Disruption
MDA Multi-Dimensional Analysis
MUC1 Mucin 1
NSCLC Non-small cell lung cancer
PCR Polymerase Chain Reaction
qPCR Quantitative PCR
x

RB1 Retinoblastoma 1
RNA Ribonucleic Acid
RRM2 Ribonucleotide Reductase Subunit M2
SIGMA System for integrative genomic microarray analysis
SIGMA2 System for integrative genomic multi-dimensional analysis
SIRPA Signal Regulatory Protein Alpha
SKY Spectral karyotyping
SNP Single nucleotide polymorphism
TUSC3 Tumor suppressor candidate 3
UPD Uniparental Disomy
xi

Acknowledgements
I would like to acknowledge the contributions of many of my colleagues in the Wan Lam Lab
who contributed to this work, especially the co-authors of each of the manuscript chapters
presented herein. Detailed acknowledgements from the published version of Chapter 2 is listed
below:
Chapter 2: We thank William W. Lockwood and Timon P.H. Buys for useful discussion and
critical reading of manuscript, Ashleen Shadeo for providing data for breast cancer samples,
and Anna Chu, Byron Cline, Devon Macey, Andrew Thomson, Lan Wei, Reginald Sacdalan,
Tiffany Chao, and Laura Aslan for help with software development.
I would also like to acknowledge generous scholarship support from the Canadian Institutes of
Health Research and Michael Smith Foundation for Health Research.
The research presented in this thesis was funded by the following granting agencies: Genome
Canada/ Genome British Columbia, Canadian Cancer Society Research Institute (CCS20485),
Canadian Institute of Health Research (MOP 86731, MOP 77903), National Institutes of Health
(R01 DE15965-01), National Cancer Institute Early Detection Research Network (5U01
CA84971-10), Canary Foundation, and Canadian Breast Cancer Research Alliance.
xii

Dedication
To my family.
xiii

Co-Authorship Statement
Chapters 2 to 5 were co-authored as manuscripts for publication. The following author lists
apply for each chapter:
Chapter 2: Chari R, Coe BP, Wedseltoft C, Benetti M, Wilson IM, Vucic EA, MacAulay C, Ng
RT, Lam WL. (2008) SIGMA2: a system for the integrative genomic multi-dimensional analysis
of cancer genomes, epigenomes, and transcriptomes. BMC Bioinformatics, 9(1):422, 1-12.
Contribution: I am the first author of this manuscript. I designed and developed the software
and wrote the manuscript. The co-authors of this manuscript were either undergraduate
students who I mentored on this project or were fellow graduate students who tested the
software and provided important user feedback.
Chapter 3: Chari R, Coe BP, Vucic EA, Lockwood WW, Lam WL. (2010) An integrative multidimensional
genetic and epigenetic strategy to identify aberrant genes and pathways in cancer.
BMC Systems Biology, 4(1):67, 1-14.
Contribution: I am the first author of this manuscript. I acquired most of the data through
generating genomic profiles and downloaded the rest of the data from public resources. I
conceived the analysis for the manuscript and wrote the manuscript.
Chapter 4: Chari R, Lockwood WW, Soh J, Coe BP, Tam K, MacAulay C, Minna JD, Lam S,
Gazdar AF, Lam WL. (2010) Uniparental disomy is a prevalent mechanism of genetic alteration
in lung adenocarcinoma.
Contribution: I am the first author of this manuscript. I generated all of the data and performed
all of the analyses for this manuscript and my co-authors provided useful information through
comments and other supporting data.
xiv

Chapter 5: Chari R, Thu KL, Wilson IM, Lockwood WW, Lonergan KM, Coe BP, Malloff CA,
Gazdar AF, Lam S, Garnis C, MacAulay CE, Alvarez CE, Lam WL. (2010) Integrating the
multiple dimensions of genomic and epigenomic landscapes of cancer. Cancer and Metastasis
Reviews, 29(1):73-93.
Contribution: I am the first author of this manuscript. I orchestrated the study, performed all
analyses and wrote the manuscript with the help of my supervisor. Other co-authors provided
useful information, data, or comments.
xv

Chapter 1: Introduction
1

1.1 Lung cancer
Lung cancer has the highest mortality rate amongst all diagnosed malignancies [1]. In 2009, it
is estimated that 24,000 individuals will be diagnosed with lung cancer with approximately
21,000 individuals succumbing to this disease (Canadian Cancer Statistics 2009,
www.cancer.ca). Lung cancer is classified into two main types: non-small cell lung cancer
(NSCLC) and small cell lung cancer (SCLC) and within NSCLC, the two major histological
subtypes are adenocarcinoma (AC) and squamous cell carcinoma (SqCC) with large cell
carcinoma (LCC) being the third most common histological subtype . AC accounts for the
highest percentage of all lung cancer cases, representing almost half of all NSCLCs diagnosed.
The primary etiological factor associated with lung cancer is tobacco smoke exposure. While
the majority of lung cancer patients have a heavy smoke exposure history, there is an
increasing percentage of lung cancer patients (25%) where primary smoke exposure is not the
associated cause of the disease [2]. Moreover, when examining the association of smoke
history and histological subtypes diagnosed, while all subtypes have an association with smoke
exposure, SCLC and SqCC show the most strongest associations [3]. In addition, amongst
never smokers, the majority of cases are of the adenocarcinoma subtype [2].
Examining across the spectrum of all NSCLC patients, independent of stage, only 15% of all
lung cancer patients will achieve five-year survival with the median survival time of lung cancer
patients less than one year. Stratification by stage reveals that those individuals diagnosed
early (stage IA) have a superior rate of five year survival as compared to those diagnosed late
(stage IV) (50% vs. 2%) [4]. Given the overall survival independent of stage is closer to stage
IV than stage IA, it is clear that the paltry survival statistics are largely due to the late diagnosis
of this disease and to a lesser extent, the nominal response rate observed by conventional
chemotherapies [5].
2

While overall therapeutic strategies have provided limited benefit to prolonging patient survival,
there has been moderate success in the application of targeted therapeutics. Specifically,
pharmacological agents against the epidermal growth factor receptor (EGFR) tyrosine kinase
have shown selective efficacy in a subset of lung AC patients [6-12]. Hence, in addition to
improving early detection strategies, another main focus of lung cancer research is the
identification of novel therapeutic targets. One such approach that can be used to identify
targets is through the application of genomic tools to clinical lung cancer specimens.
1.2 Genomic profiling of lung cancer
1.2.1 Gene expression analysis
One of the first applications of high throughput genome technologies was to the assessment of
messenger RNA (mRNA) levels [13, 14]. While the first, landmark cancer-related studies were
done in breast and hematological malignancies [15-17], substantial findings were made in the
analysis of lung cancer. Specifically, lung cancer gene expression studies have identified
genes differentially expressed in tumors, genes associated with angiogenic potential, genes
associated with chemoresistance, expression signatures defining subclasses of lung cancer,
expression signatures associated with patient prognosis, and expression signatures from
normal bronchial epithelium samples to detect lung cancer [18-34]. In addition, much work has
also been done to understand baseline gene expression in non-malignant lung tissue as well its
changes with respect to heavy smoke exposure [35-38]. These studies are as important as
studies involving lung cancer samples as they provide an important reference level of gene
expression to decipher the dysregulated gene expression in tumors.
However, from a given analysis of differential expression in tumors, there are typically
hundreds, if not thousands, of genes which may show aberrant gene expression in tumors when
compared to non-malignant tissue. Moreover, it is likely that a proportion of the genes which
are aberrantly expressed are not integral or causal to tumor development as many gene
3

expression changes are reactive to changes in expression of other genes. In addition, using
gene expression alone, one cannot discern which changes are causal and which changes are
reactive. One approach to assign causality with gene expression changes is to identify
alterations at the DNA level such as somatic mutation, changes in gene dosage (DNA copy
number), or epigenetic changes such aberrant DNA methylation or histone modification which
can explain the observed differential expression.
1.2.2 DNA copy number analysis
Alterations in gene dosage, whereby segments of DNA in the genome are either replicated or
lost, have shown to be important in lung cancer [39-41]. Typically, these gains and losses of
DNA are detected through the comparison of a genome from a tumor sample with a genome
that is normal or non-malignant. It is thought that these increases or decreases in amounts of
specific gene sequence could allow for increased or decreased expression of that gene.
Technological advances have allowed for the high throughput assessment of DNA copy number
changes in the cancer genome namely through microarray comparative genomic hybridization
(CGH) [42, 43]. Briefly, this technology capitalizes on differential fluorescence labelling where
DNA from the tumor sample and DNA from the normal sample, each labelled with different
fluorescent dyes, are hybridized together on the same chip and differences in fluorescent
intensities are measured. Moreover, array CGH profiling of both lung cancer cell lines and
tumors have identified areas of the genome which are frequently gained or lost [44-52].
Specifically, these areas of copy number alteration have targeted known oncogenes such as
MYC, EGFR, MDM2, TERT, and tumor suppressor genes such as CDKN2A, TP53 and RB1.
However, these alterations typically do not occur in 100% of lung tumors (e.g. the EGFR locus
is gained/amplified in 10-20% of cases). In addition, the amplification and deletion events
typically encompass multiple genes and as such, more often than not, only a subset of those
genes will have a downstream consequence at the gene expression level. Hence, integration of
4

gene dosage with gene expression analysis would be useful to discern the target gene(s) of a
given copy number alteration.
1.2.3 Loss of heterozygosity (LOH) and allelic imbalance
Loss of heterozygosity (LOH) is a common genetic event in cancer [53]. In the normal cell,
each somatic chromosome has two copies, with one copy (or allele) originiating from each
parent. Subsequently, in the tumor, a specific segment from one of the copies of the
chromosome is lost, resulting in loss of heterozygosity.
Frequent regions of LOH have also been identified in the lung cancer genome [54-58]. While
initial studies involved the use of microsatellite markers placed throughout the genome and
thus, the resolution of these changes were limited, the application of SNP arrays were able to
refine these areas into specific chromosome arms [45, 46]. In addition to advances in SNP
array technology, analysis approaches were also developed that increased the detection
sensitivity of regions of LOH / allelic imbalance [59-63]. Although most areas with altered gene
dosage will also be detected as LOH (in case of copy number loss) and allelic imbalance (in
case of copy number gain), there are also areas in the genome which exhibit LOH but no
change in copy number, termed copy neutral LOH or uniparental disomy. However, the role of
UPD in lung cancer is not well understood.
1.3 Somatic mutations in lung cancer
Somatic mutations have also shown to be important in cancer development. In addition,
mutational analysis is also used for screening purposes in high risk populations (e.g. BRCA1/2
and hereditary breast cancer) as well as criteria for receiving targeted chemotherapy (e.g.
EGFR mutation and EGFR inhibitors). Many studies have been undertaken to identify
mutations in genes involved with important cellular processes pertinent to the cancer phenotype
such as DNA repair and cellular proliferation and have successfully identified key genes to a
5

number of different cancer types. Moreover, it can be classified that while oncogenes typically
harbour activating mutations, tumor suppressor genes often harbour inactivating mutations.
In lung adenocarcinoma, the most well known genes shown to be mutated are EGFR, KRAS,
LKB1 (or STK11), TP53 and CDKN2A [30, 54, 64, 65], with some mutations such as EGFR and
KRAS showing preferential mutation patterns based on smoking history. A recent study
assessing other well known oncogenes and tumor suppressor genes showed there were a
number of other genes also observed to be mutated in lung adenocarcinoma [64]. However,
due to technological and material limitations at the time, many of these studies only assess
small numbers of genes in a given study and thus, genome wide screening for somatic
mutations is unfeasible. While high throughput sequencing technologies to assess sequence
mutation on a genome scale have become available, challenges associated with cost and data
analysis preclude the use in a routine manner.
1.4 Epigenetic alterations in lung cancer
1.4.1 DNA methylation
Another DNA level mechanism which can affect gene expression is through the methylation of
DNA at gene promoters. DNA methylation is a reversible chemical modification which has
shown to have a prominent role in the silencing of tumor suppressor genes. Specifically, this
modification targets cytosines whereby a methyl (CH3) is added to the carbon 5 moiety of
cytosine.
It is thought that in cancer, the majority of the genome loses its methylation but small areas in
the gene promoters, known as CpG islands, gain methylation [66-69]. Generally, it is thought
that the acquired methylation targets tumor suppressor genes while the areas of lost
methylation facilitate the activation of repetitive areas of the genome which can lead to
increased genomic instability. In addition, aberrant DNA methylation of critical genes have been
6

utilized for early detection purposes as well as a target for therapeutic intervention [70-72],
emphasizing its key role in cancer.
In lung cancer, a number of specific genes such as CDKN2A (or p16), RASSF1A, and MGMT
have shown to harbour increased promoter methylation [73]. While many of these methylation
events were discovered using single locus assays, recent advances have allowed for the high
throughput analysis of 1000s of genes in a single experiment [74-79]. As such, applications of
these high throughput approaches in lung cancer are likely to identify novel methylated genes.
Similar to array CGH analysis, though many methylated genes are likely to be identified, it will
be important to validate if these alterations affect downstream gene expression.
1.5 Current level of integrative analysis
At the time this thesis started, there were a small number of whole genome integrative studies
which primarily focused on the integration of gene dosage and gene expression. In fact, the
majority of the integrative analysis would be done at single locus level such as the examination
of gene dosage and expression of HER2 (ERBB2) oncogene in breast cancer [80]. Moreover,
there were a limited number of gene dosage or gene expression studies in lung cancer.
However, from recent studies involving multiple cancer types, including lung cancer, it has been
shown that anywhere between 20% and 60% of genes in regions of copy number change also
exhibit a concerted change in gene expression [52, 81-84]. Conversely, when the proportion of
differential expression associated with gene dosage alteration was examined, it was found that
only 11% of the observed differential expression could be attributed to high level DNA copy
number change [83]. Thus, it is clear that gene dosage alterations are responsible for only a
part of the overall dysregulated gene expression and that other mechanisms are likely involved.
1.6 Need for an integrative approach to study lung cancer
As discussed earlier, a gene such as CDKN2A has been shown to be inactivated by both gene
dosage loss and increased promoter methylation. Thus, it is very likely that when examining a
7

large number of tumors, that a given gene may be affected by one mechanism in tumor (e.g.
gene dosage increase) and another mechanism in a different tumor (e.g. DNA
hypomethylation), but both leading to the same net effect (Figure 1.1). In addition, if the specific
event (e.g. gene dosage increase) occurs at a low frequency, but cumulatively, the deregulation
occurs at a high frequency, then examining only gene dosage or DNA methylation would
preclude the identification of such potentially important genes. Hence, it should be apparent
that an integrative, multi-dimensional genetic and epigenetic approach is needed to identify
novel genes which would have escaped previous, single dimensional analyses.
1.7 Bioinformatic tools for genomic analysis
While many software packages exist for the analysis of high throughput gene expression data
[85-88], at the start of my thesis project, software packages for the visualization and analysis of
DNA copy number data were very limited [89-95]. A summary of array CGH analysis
methodologies and software packages is provided in this review [96]. Moreover, three of the
key challenges at the time were (i) the increase in data generated from a single experiment, (ii)
the effective visualization of this data for easy interpretation, and (iii) the microarray platform
dependence of the majority of software packages.
With respect to the increase in data generation, the first generation of microarrays used for
array CGH typically comprised of two to three thousand data points. As such, software for both
visualization and analysis were developed to effectively handle this level of data complexity.
For example, since array CGH data in fact represents discrete levels of copy number
throughout the genome, one of the data analysis steps required is segmentation which
effectively smoothes data based on genomic position. The first version of DNACopy [97], one
of the first algorithms to segment array CGH data, would need a significant amount of time to
execute when applied to arrays with 100,000 data points or greater and eventually, a new
version of the program was developed a few years later [98]. Similarly, in terms of visualization,
most programs displayed array CGH data in an ordinal manner whereby the relative genomic
8

position was on the x-axis and the log ratio of the data point was drawn on the y-axis. While
this type of visualization can provide a quick genome summary of a single sample, it is difficult
to readily link to information such as protein coding genes from this type of visualization.
Finally, software developed by microarray manufacturers such as Affymetrix, Agilent or
Nimblegen were specifically tailored to handle data from their respective microarray platforms.
Thus, aggregate analysis of data emanating from different microarray platforms, but analyzing
samples with common characteristics, could not be analyzed in a concerted manner resulting in
under-utilization of the increasingly available array CGH data in the public domain. Most
importantly, no tools existed to integrate multiple dimensions of data such as global gene
dosage and gene expression, let alone integration with DNA methylation. Hence, it is clear that
with these apparent challenges, the development of such bioinformatic tools was needed.
1.8 Thesis theme
The theme of this thesis is the development and utilization of an integrative genetic and
epigenetic approach to identify novel aberrant genes and pathways that may be involved in the
tumorigenesis of lung adenocarcinoma. This will be achieved by employing genome wide
genetic and epigenetic profiling experiments of lung adenocarcinoma samples and the
subsequent integration of this data using novel bioinformatics tools and approaches.
1.9 Objectives and hypothesis
The objective of this work is to demonstrate the importance of employing an integrative
approach to understand genetic and epigenetic alterations and their consequence on gene
expression. The hypothesis can be broken down to three parts:
(A) Genes/pathways which are important to tumorigenesis are disrupted by multiple
mechanisms in lung cancer.
9

(B) By using an integrative approach, looking at the global genetic and epigenetic regulation of
gene expression, changes at the DNA level which have downstream effects at the gene
expression level will be identified.
(C) This approach will lead to the identification of more genes that are disrupted than previously
anticipated and these genes will be enriched in key pathways and functions important to lung
tumorigenesis.
1.10 Specific aims and outline of thesis
This thesis consists of four manuscripts assembled in a non-chronological order to best address
the objectives and hypothesis of this thesis.
Aim 1: Development of a platform for multi 'omics data integration and analysis
Chapter 2 discusses the development of an integrative analysis software package called a
system for the integrative genomic multi-dimensional analysis of cancer genomes, epigenomes,
and transcriptomes (SIGMA 2 ). The development of this application was necessary prior to the
undertaking of the analysis of the vast amount of data generated from the utilized high
throughput, genome-wide technologies.
As discussed earlier in this chapter, there were very few bioionformatic tools available for the
analysis of array CGH data, let alone for integrative analysis of gene dosage and gene
expression. Prior to the development of SIGMA 2 , I developed the pre-cursor version of this
software SIGMA [95]. SIGMA provided the basic framework in terms of the user interfaces,
database communication, data structures and "look and feel" that would be utilized in SIGMA 2 .
Moreover, one of the key challenges when SIGMA was developed was the effective
visualization and analysis of large datasets generated by newer, high density array CGH
platforms. At the time, the majority of data that were generated were on platforms comprised of
3000 measurements per sample but, newer technologies were being developed which
10

generated over 500,000 data points per sample, representing a 100-fold increase in information
obtained from each experiment [99]. Hence, the base software architecture used in SIGMA 2
was already capable of handling large amounts of data.
Aim 2: Demonstration of an integrative approach using model systems
Chapter 3 discusses the demonstration of an integrative, multi-dimensional approach on tumor
cell line model systems. Using a set of breast cancer cell lines, I examine the gene dosage,
allelic composition, DNA methylation, and gene expression profiles in an integrative manner to
delineate which genes and pathways would be missed or less significant if such an approach
was not used. This demonstrative study was needed to show the key advantages and benefits
of an integrative approach. While cell lines are artificial systems and may have acquired
alterations that are beneficial to grow in vitro, it is important that a sample source was used
where material limitations did not exist. For each of the genetic or epigenetic profiling studies,
sufficient amounts of DNA and RNA are needed and when more assays are done in a given
sample, more material is required. Moreover, when whole tumor samples are microdissected to
ensure high tumor cell purity, this inherently will reduce the amount of usable sample material.
As such, it is important that the quantitative and qualitative benefits of utilizing an integrative
approach are sufficient to warrant using clinical samples. At the time this study was initiated,
SNP array and array CGH profiles were available for breast cancer cell lines and thus, only
generation of DNA methylation and gene expression profiles were needed to complete this set.
Given the purpose of this study was to demonstrate the effectiveness of the integrative
approach, while data from lung cancer cell lines would have been most optimal, the source of
data has limited relevance to the purpose of this aim.
Aim 3: Characterization of DNA level alterations in lung adenocarcinoma
A number of studies have been done to identify gene dosage alterations in lung cancer and in
lung adenocarcinoma specifically. These studies were done on a number of different array
11

platforms, with one of the latest studies done using Affymetrix SNP arrays. One of the benefits
of Affymetrix SNP arrays is the ability to simultaneously detect changes in gene dosage as well
as allelic imbalance. Allelic imbalance, though should be determined using a patient matched
non-malignant sample as a control, has also been determined using a pool of unmatched non-
malignant samples. While the ability to detect imbalance using unmatched control samples is
important when matched control samples are not available, this may falsely score regions as
imbalanced but in fact are not, and vice versa. In addition, samples in these different studies
were typically not microdissected and thus, tumor cell purity in the samples would be variable.
Thus, those samples with low tumor cell content would make it difficult to detect genetic
alterations. Moreover, there has been a recent drastic increase in resolution with the newest
SNP arrays, with the ability to measure over 4X as many SNPs and over 8X as many spots for
gene dosage. Chapter 4 discusses the application of a new SNP array technology to
microdissected lung adenocarcinoma specimens with the goal of identifying genetic alterations
at the highest resolution currently available.
Aim 4: Application of an integrative approach to clinical lung adenocarcinoma
specimens
With the approach and necessary tools developed and now demonstrated to be beneficial using
a model dataset, chapter 5 discusses the application of the integrative approach to lung
adenocarcinoma specimens. While the published chapter provides an overview of cancer
genome and epigenome landscapes, sections 5.4.3 to 5.4.4 present some of the quantitative
and qualitative benefits of integrative analysis specific to the analysis of a lung adenocarcinoma
dataset. In addition, sections 5.4.5 to 5.4.6 discusses key findings in terms of genes and
pathways that were identified from this integrative analysis.
12

1.11 Description of high throughput data in this thesis
Throughout this thesis, a number of platform technologies were utilized to generate high
throughput, genome wide data. Below is a summary of all platforms used in each chapter.
In Chapter 3, for nine breast cancer cell lines and one control cell line (MCF10A), the following
profiles were generated: Affymetrix SNP 500K for the analysis of allelic status; whole genome
tiling path array CGH for the analysis of gene dosage; Illumina Infinium HumanMethylation27 for
DNA methylation analysis; and Affymetrix U133 Plus 2.0 for the analysis of gene expression.
In Chapter 4, for the 46 tumors and matched non-malignant tissue as well as the cancer cell
lines, the Affymetrix SNP 6.0 platform was utilized to measure total copy number and allelic
imbalance. For a subset of tumors, gene expression profiles were generated using a custom
Affymetrix platform designed by Rosetta Inpharmatics.
In Chapter 5, for the ten tumors and matched non-malignant tissue samples, the following
profiles were generated: Affymetrix SNP 6.0 for the analysis of allelic status and gene dosage;
Illumina Infinium HumanMethylation27 for DNA methylation analysis; and Affymetrix HuEx 1.0
ST array for the analysis of gene expression. Quantitative RT-PCR was performed using the
Applied Biosystems TaqMan gene expression assay.
1.12 Other relevant contributions not included as chapters in this
thesis
In this thesis, I have chosen to include a small portion of my overall work in order to achieve a
coherent theme. However, in this section, I have outlined specific contributions, which I’ve
either led or participated as 2 nd author, that I’ve deemed are relevant to the theme of lung
cancer and genomics.
13

1.12.1 Development of tools for genomic analysis
As mentioned earlier, the precursor version of SIGMA2 was SIGMA [95]. This tool was built as
an interactive database of cancer cell line array CGH profiles and provided a means for
effective visualization of high density array CGH data as well as sharing of data. One of the
other problems that arose for high density array CGH data is the availability of efficient analysis
algorithms to delineate gains and losses. Most algorithms that were developed for array CGH
analysis were developed for arrays with 2000 to 3000 data points and their execution times did
not scale up efficiently when the arrays were generating 100,000 to 1,000,000 data points. To
address this problem, I contributed to the development of a segmentation and calling algorithm
named FACADE [100].
1.12.2 Baseline gene expression in non-malignant lung tissue
Though gene expression studies studying malignant samples are important, it is also critical to
define what genes are expressed in non-malignant samples as these are used in reference to
determine aberrant gene expression. There were two studies I was involved with which
addressed this question. First, we examined gene expression of non-malignant, smoke
damaged bronchial epithelium using serial analysis of gene expression (SAGE) [37]. We found
that there were specific genes that showed high tissue specificity to the bronchial epithelium
with limited representation in other tissues and that there were differences between bronchial
epithelial samples and lung parenchyma, which are samples adjacent to tumors typically used
as non-malignant controls comprised of a mixture of different cells.
In the study described above, bronchial epithelium samples from current and former smokers
were grouped together. Hence, the next logical question was to assess the effect of active
smoking on the bronchial epithelium. In the second study, a group of never smoker samples
were added to the groups of current and former smokers and the gene expression profiles of
the three groups were compared. We first identified a set of genes which were differentially
14

expressed in response to smoke exposure and found a subset of genes that were reversible
upon smoking cessation and another subset of genes irreversible upon smoking cessation [35].
Those genes which were irreversibly altered after heavy smoke exposure may have implications
in affecting future risk of developing lung cancer. Moreover, these findings also suggest that
when trying to identify cancer-specific changes when unmatched control samples are not
available, clinical characteristics such as smoking status should be taken into consideration
when comparisons are made.
1.12.3 Differential gene expression analysis in lung cancer
With the non-malignant, baseline gene expression defined, differential gene expression in early
stages of lung cancer and locally invasive squamous cell carcinoma were then assessed [101].
It was found that genes associated with epidermal development were increased in expression
and mucociliary function were decreased in expression in carcinoma-in-situ as well as in
precancerous stages. Finally, genes associated with tissue re-modelling were also altered in
expression in local invasive cancer and also showed altered expression in carcinoma-in-situ,
suggesting this function is affected early in cancer development.
The Wnt pathway has been shown to be aberrant in many cancer types. At the time the study
began, there were two branches of the pathway that were known to exist, canonical and non-
canonical, whose activation resulted in different downstream consequences. While the
canonical branch was the primary focus of most researchers studying this pathway, we sought
to assess the role of the non-canonical branch in lung squamous cell carcinoma using semi-
quantitative and quantitative PCR of genes which were a part of the non-canonical branch [102].
From this study, it was found that (i) these non-canonical genes were expressed in the normal
lung and (ii) some of these non-canonical genes were differentially expressed in tumors.
An important consideration in the analysis of differential gene expression in cancer is the use of
suitable reference genes for data normalization. This consideration is critical to both
15

quantitative PCR experiments as well as microarray experiments where relative quantifications
are typically used. To address this, using SAGE, where quantification of expression is absolute,
genes whose expression was constant across both malignant and non-malignant samples were
identified [103]. Those genes demonstrated better constancy than some genes which are
typically used as controls for gene expression analysis.
1.12.4 Integration of gene dosage and gene expression in lung cancer
The first level of genomic integration that needed to be accomplished was the integration of
gene dosage and gene expression. In one study using cancer cell lines, hot spots of DNA
amplification were identified throughout the genome. When specifically examining lung cancer
cell lines and subsequently coupling this with gene expression data, it was found that 50% of
genes in these frequently amplified regions show correlation between gene dosage and gene
expression [52]. Moreover, it was also observed that different components of the EGFR
signaling pathway were amplified in different cell lines illustrating that for a given pathway, one
can underestimate the frequency of pathway disruption when only well known genes in the
pathway are assessed.
In a second study involving clinical lung tumors, a genomic region which was preferentially
amplified in squamous cell carcinomas as compared to adenocarcinomas was identified.
Further integration with gene expression data allowed for the identification of the target gene,
BRF2, in this amplified region [104]. Moreover, gene dosage and protein expression level
assessment of CIS samples for BRF2 showed that amplification and overexpression were
present, suggesting that this event is occurring at an early stage of tumorigenesis.
16

Figure 1.1
a Normal Tumor b Normal Tumor
c Normal Tumor
Copy Number Loss /
Loss of heterozygosity (LOH)
d
e
Normal
Tumor
Normal
Tumor
Allelic Imbalance
AATACGCGCGCGTCGCATCCAGCATGAACAGA
TTATGCGCGCGCAGCGTAGGTCGTACTTGTCT
AATACGCGCGCGTCGCATCCAGCATGAACAGA
TTATGCGCGCGCAGCGTAGGTCGTACTTGTCT
DNA Hypermethylation
AATACGCGCGCGTCGCATCCAGCATGAACAGA
TTATGCGCGCGCAGCGTAGGTCGTACTTGTCT
AATACGCGCGCGTCGCATCCAGCATGAACTGA
TTATGCGCGCGCAGCGTAGGTCGTACTTGACT
Somatic mutation
Figure 1.1. Multiple mechanisms of alteration leading to the same downstream consequences.
(a) Illustration of copy number loss. Loss of a particular chromosomal region
in tumors. (b) Illustration of allelic imbalance. While both alleles are present, there is a
preferential increase of one of the alleles. (c) Ilustration of uniparental disomy. While
overall the total number of DNA copies is normal, one part of an allele is lost and replaced
by a part from the other allele. (d) Promoter hypermethylation in tumors which results in
suppression of gene transcription. (e) Somatic mutation in the tumor which can lead to the
transcription of a truncated (possibly non-functional) transcript. Mechanisms shown in (a),
(c), (d), and (e) can lead to the same net downstream effect in loss of gene and protein
expression. For (a), (b), and (c), though whole chormosomes are shown, these events can
vary in scale from a focal region of change to a whole chormosome arm. The green arrow
represents the transcription start site.
17
Uniparental disomy (UPD)
Premature stop,
truncated transcript

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23

Chapter 2: SIGMA 2 : A system for the integrative genomic
multi-dimensional analysis of cancer genomes, epigenomes,
and transcriptomes 1
1 A version of this chapter has been published. Chari R, Coe BP, Wedseltoft C, Benetti M,
Wilson IM, Vucic EA, MacAulay C, Ng RT, Lam WL. (2008) SIGMA2: A system for the
integrative genomic multi-dimensional analysis of cancer genomes, epigenomes, and
transcriptomes. BMC Bioinformatics 9:422. doi:10.1186/1471-2105-9-422. Please see the
published version of this chapter for all supplementary materials.
24

2.1 Introduction
Multiple mechanisms of gene disruption have been shown to be important in the development of
cancer. Genetic alterations (mutations, changes in gene dosage, allele imbalance) and
epigenetic alterations (changes in DNA methylation and histone modification states) are
responsible for changing the expression of genes. High throughput approaches have afforded
the ability to interrogate the genomic, epigenomic and gene expression (transcriptomic) profiles
at unprecedented resolution [1-6]. However, a gene can be disrupted by one or by a
combination of mechanisms, therefore, investigation in a single 'omics dimension (genomics,
epigenomics, or transcriptomics) alone cannot detect all disrupted genes in a given tumor.
Moreover, individual tumors may have different patterns of gene disruption, by different
mechanisms for a given gene while achieving the same net effect on phenotype. Hence, a
multi-dimensional approach is required to identify the causal events at the DNA level and
understand their downstream consequences.
The current state of software for global profile comparison typically focuses on analyzing and
displaying data from a single dimension, for example CGH Fusion (infoQuant Ltd, London, UK)
for DNA copy number profile analysis and GeneSpring (Agilent Technologies, Santa Clara, CA,
USA) for gene expression profile analysis. Software for integrative analysis have been
restricted to working with datasets derived from limited combination of technology platforms
(Table 1) [7-10]. Though different software can analyze data generated from different
platforms, the ability to perform meta-analysis using data from multiple microarray platforms is
limited to a small number of software packages. Consequently, integrative analysis of cancer
genomes typically involves no more than two types of data, most commonly the integration of
gene dosage and gene expression data [11-16] and recently expanded to integrating allelic
information [17]. Software to perform multi-dimensional analysis are therefore greatly in
demand.
25

Here, we present SIGMA 2 , a novel software package which allows users to integrate data from
the various 'omics disciplines such as genomics, epigenomics and transcriptomics. Multi-
dimensional datasets can be simultaneously compared, analyzed and visualized with respect to
individual dimensions, allowing combinatorial integration of the different assays belonging to the
different 'omics. The identification of genes altered at multiple levels such as copy number,
LOH, DNA methylation and the detection of consequential changes in gene expression can be
concertedly performed, establishing SIGMA 2 as a tool to facilitate the high throughput systems
biology analysis of cancer. SIGMA 2 is freely available for academic and research use from our
website, http://www.flintbox.com/technology.asp?Page=3716.
2.2 Implementation
SIGMA 2 is implemented in Java, and requires version 1.6+ of the runtime compiler. In addition,
the statistical package R and database application MySQL are also required. The java interface
communicates with MySQL using a JDBC connector and with R using the JRI package by JGR
(Figure 2.1). MySQL is used for data storage and querying while R is used for the
segmentation and statistical analysis. All genomic coordinate information was obtained from
University of California Santa Cruz (UCSC) genome databases [18].
2.3 Results and discussion
2.3.1 Look and feel of SIGMA 2
The novel multi-dimensional ‘omics data analysis software SIGMA 2 is built on the framework of
a facile visualization tool called SIGMA, which can display alignment of genomic data from a
built-in static database [7]. The arsenal of functionalities introduced in SIGMA 2 is shown in
Table 2.1.
26

2.3.2 Description of application scope and functionality
SIGMA 2 is built to handle a variety of analysis techniques typically used in the high-throughput
study of cancer, allowing the combinatorial integration of multiple 'omics disciplines. The
hierarchy, which underlies the program, groups data into genome, epigenome, and
transcriptome is shown in Figure 2.2a and the overall functionality map is given in Figure 2.2b
and listed in Table 2.2. With each 'omics dimension, data sets may be imported representing
any of the major types of biological measurements being assayed, for example, (i) examining
both DNA copy number and LOH assays within the genomic bundle, (ii) examining both DNA
methylation and histone modification status within the epigenomics bundle, and (iii) examining
both gene expression profiles and microRNAs expression assays within the transcriptomic
bundle. Each assay may branch into data sources from a multitude of technology platforms.
2.3.3 Approach to integration between array platforms and assays
SIGMA 2 treats all data in the context of genome position based on the relevant human genome
build using the UCSC genome assemblies. An interval-based approach is used to sample
across different array platforms and assays and data from each interval are merged together.
Briefly, this is done by querying data at fixed genomic intervals for each platform and
subsequently taking an average of the measurements within each interval. The algorithm is
listed in Figure 2.3.
2.3.4 Format requirements of input data
Standard tab-delimited text files are used for the input of data for all of the assay types. For
genomic data, specifically array CGH, normalization is recommended using external algorithms
such as CGH-Norm and MANOR [19, 20]. Segmentation analysis can be performed within
SIGMA 2 , but results from external analysis can be imported and used in the consensus calling
feature. The algorithms which can be called within SIGMA 2 currently include DNACopy and
GLAD [21, 22]. Multiple sample batch importing is available to facilitate efficient loading of
27

datasets. To utilize this, the user must create an information file which describes each sample
in the dataset. Formatting requirements of the information file are specified in the manual.
Alternatively, for Affymetrix SNP array analysis, data should also be pre-processed and
normalized using the appropriate software, such as CNAG before importing into SIGMA 2 [23].
Genotyping calls should be made prior to importing, using the "AA", "AB" and "BB" convention.
If the genotype call does not exist, "NC" must be specified. For epigenomic data, data from
affinity based-approaches (MeDIP [6] and ChIP [24]) should contain a value representing the
level of enrichment and the genomic coordinates for each spot. Similarly, for bisulphite-based
approaches [25], a percent of converted CpGs should be provided along with the genomic
coordinates for each spot. Finally, for transcriptome data, gene expression data from Affymetrix
experiments can be directly imported and processed as CEL files and are normalized using the
MAS 5.0 algorithm implemented in the "affy" package of R. For any assay type, custom data
can be imported whereby the user provides a map of the platform based on the given genome
build, and the unique identifier for the map must be used for the data generated from those
experiments.
2.3.5 Description of user interface
The main user interface in SIGMA 2 utilizes a tabbed window-pane which allows the user to
open multiple visualizations simultaneously (Figure 2.4). The left part of the window manages
the analyses and projects which belong to the current user and button shortcuts for the main
functionality are spread along the top of the window. Using an example of an array CGH profile
from the Agilent 244K platform, we demonstrate the step-wise interrogation of a region of
interest [26]. Briefly, using the highlighting toolbar button, the user can select a region of
interest and subsequently, by clicking the right mouse button, the user can search for annotated
genes within the specified genomic coordinates.
28

2.3.6 Analysis of data from a single assay type
The first, and most basic, level of analysis is from a single assay type. For array CGH, multiple
options for segmentation algorithms are available within the program and results from externally
run segmentation can be imported as well. However, each segmentation algorithm has its
advantages and disadvantages depending on the type of data used and the quality of data at
hand. A unique feature of SIGMA 2 is the ability to take a consensus of multiple algorithms using
"And" or "Or" logic between algorithms. Moreover, a level of consensus can be specified
(Figure 2.5a). For example, if an experiment is analyzed using five approaches, the user can
select areas of gain and loss which were detected by one algorithm, at least three algorithms,
all five algorithms, etc. For LOH, basic analysis using the number of consecutive markers that
exhibit LOH is used to determine its status. Affinity-based approaches for DNA methylation and
histone modification states or bead-based percentage of CpG island methylation is analyzed by
either direct thresholding or z-transform thresholding. For any of the different assay types,
when examining across a number of samples, a frequency of alteration can be calculated and
plotted.
For data from different array platforms, but assaying the same biological measurement, the
algorithm for integration is used to derive common data. This feature is most applicable to DNA
copy number data due to the number of array CGH platforms. This allows for better utilization
of publicly available data and thus, increasing sample size for statistical analysis. Similar to the
multiple sample analysis of data on the sample platform, a frequency of altered states can be
generated and plotted. Figure 5A shows the concerted analysis of a sample profiled on the
Affymetrix 500K SNP array, Agilent 244K CGH array and the whole genome tiling path BAC
array (Figure 2.5b).
29

2.3.7 Analysis of data from multiple assays in a given 'omics dimension
Within a given 'omics dimension, multiple assay types can be analyzed in combination. For
example, it is useful to investigate copy number and LOH and the interplay between DNA
methylation and different states of histone modification. Typically, in regions of copy number
loss, LOH is also observed. However, LOH can also occur in regions which are copy number
neutral, indicating a change in allelic status which is not interpretable by one dimension alone.
Here, we show a sample for which copy number and LOH information exists, a region of copy
number loss associated with LOH (Figure 2.6). In terms of epigenetics, DNA methylation and
states of histone methylation and acetylation have been known to be biologically relevant. With
high throughput technologies available to assay these dimensions, this type of analysis will
become more prevalent.
2.3.8 Combinatorial analysis of multiple 'omics dimensions - gene dosage and gene
expression
The most common analysis of multiple 'omics dimensions is the influence of the genome on the
transcriptome. A number of software packages have started to incorporate approaches to
examining gene dosage and gene expression [8, 9, 27]. In SIGMA 2 , there are multiple
functionalities which allow the user to link DNA copy number to gene expression. For a single
group of samples, with matching DNA copy number and gene expression profiles, the user can
determine associations through two main options: a) using a correlation-based approach,
correlating the log ratios with the normalized gene expression intensities and b) using a
statistical-based approach comparing the expression in samples with copy number changes
against those without copy number change utilizing the Mann Whitney U test, analogous to
approaches taken in previous studies [27]. Spearman, Kendall or Pearson correlation
coefficients can be calculated for option a). Similarly, this functionality is also available for
correlating epigenetic profiles and gene expression.
30

In addition to single group analysis, two-dimensional genome/transcriptome analysis can be
applied to two-group comparison analysis. For example, if patterns of copy number alterations
are compared between two groups and a particular region is more frequently gained in one
group than another, the expression data can subsequently compared between the groups of
sample to determine if there is an association between gene dosage and gene expression.
That is, we would expect the group with more frequent copy number gain to have higher
expression than the other group. Notably, this functionality does not require both copy number
and expression data to exist for the same sample, but allows the user to select an independent
dataset for expression data comparisons (Figure 2.7).
2.3.9 Group comparison analysis - single ‘omics dimension
Finally, for two groups of samples, the user can compare the distribution of changes between
two groups to determine if the patterns are statistically different using a Fisher's Exact test. For
DNA copy number, it is the distribution of gain and losses; for DNA methylation or histone
modification states, the proportion of samples that meet the threshold of enrichment for each
group (low or high); and for LOH, proportion of samples with LOH for a region for each group.
2.3.10 Group comparison analysis - integrating multiple 'omics dimensions
This type of analysis can be performed with a single sample or multiple samples, thus allowing
combinatorial (“and”) analysis for large datasets. In addition, the user can also identify "or"
events, where a change in any of the dimensions can be flagged. This is more important in
multi-sample datasets as one dimension may not capture complex alterations of a particular
region.
2.3.11 Multi-dimensional analysis of a breast cancer genome
Using the breast cancer cell line HCC2218, we show the integration of genomic, epigenomic,
and transcriptomic data. Interestingly, when we examine the ERBB2 gene on chromosome 17,
we show concurrent amplification, LOH, loss of methylation and drastic increase in gene
31

expression (Figure 2.8). ERBB2 has shown to be an important gene in breast cancer
development and therapeutic intervention. This demonstrates the value in integrating multiple
dimensions to understand complex alteration patterns in disease samples where multiple
causes can lead to a single effect.
2.3.12 Exporting data and results
High resolution images can be exported for all types of visualizations in SIGMA 2 . Histogram
plots of gene expression, heatmaps with clustering of gene expression, karyogram plots and
frequency histogram plots are the main types of visualization available. Frequency histogram
data which is used to generate the plots can also be exported. Integrated plots with data plotted
serially or overlaid are also available for analysis involving multiple genomic and epigenomic
dimensions. Genes which are obtained from the conjunctive (And) and disjunctive (Or) multi-
dimensional analysis can be exported with their status. Results of statistical analysis such as
Fisher's exact comparisons and U-test comparisons of gene expression can be exported
against annotate gene lists based on user-specified human genome builds. Currently, April
2003 (hg15), May 2004 (hg17) and March 2006 (hg18) are the available genome builds [18].
As new builds are released, support for those builds will be available. Finally, data from multi-
platform integration can be exported based on based pair position for additional external
analysis if necessary.
2.4 Conclusions
With the increase in high-throughput data covering multiple dimensions of the genome,
epigenome and transcriptome, the approaches and tools to analyze this data must advance
accordingly to handle, analyze and interpret this data in an integrated manner. SIGMA 2 meets
these requirements and provides the framework for the incorporation of data from future
approaches and technologies. Specifically, with the movement from array to sequence-based
32

technologies, the ability to assimilate sequence data with the various 'omics data sets will
become a future requirement of software packages.
2.5 Availability and requirements
Project name: SIGMA 2
Operating system(s): Java SE V.1.6+, R Project V.2.5+, Windows XP or Vista
License: Free for academic and research use; commercial users please contact
33

Figure 2.1
R
-Segmentation
-Statistical analysis
RMySQL
MySQL
-Data storage
-Querying
SIGMA 2
JGR / JRI
JDBC
Java
-User interface
-Visualization
34
Link to
external
resources
Biological Databases
• PubMed
• OMIM
• NCBI Gene
• UCSC Genome Browser
• GEO Profiles
• Database of Genomic Variants
Figure 2.1. Main structural components of SIGMA2. Data and genome mapping information
is stored in the MySQL database. Segmentation analysis using DNACopy and
GLAD and statistical analysis is performed using R, with results stored in database. Java
was used to program the application, specifically for the user interface and the different
types of visualization. Base-pair positions and gene annotations are linked to other biological
databases to facilitate further interrogation by the user.

Figure 2.2
a
Omics
Assay
Platform
b
Combinatorial Integration
Genome Epigenome Transcriptome
DNA Copy Number Allelic Imbalance (LOH) DNA Methylation Histone
modification
BAC
array CGH
Single sample
Oligo
array CGH
Multiple samples (one group)
Multiple samples (two groups)
SNP Microsatellite
Arrays markers
Segmentation analysis for array CGH to identify regions of gain
A
and loss
Moving average thresholding for affinity based approaches
B (MeDIP for DNA methylation, ChIP-on-chip for histone
modification states)
C Regions of loss of heterozygosity (LOH)
D Regions of copy number change and LOH
E Regions of copy number neutrality and LOH (e.g. UPD)
F Regions of copy number AND methylation alteration ("two" hit)
Regions of copy number OR methylation alteration
G
(compensatory change with same net effect)
Epigenetic interplay between DNA methylation and various
H
modification states of histones
Correlation of copy number and gene expression (dataset with
I
matched copy number and expression profiles)
Statistical comparison of samples with copy number change
J versus without copy number change (dataset with matched copy
number and expression profiles) using Mann Whitney U-test
MeDIP - Bisulphite-
array CGH based
methods
Single Platform /
Single Assay
35
ChIP-onchip
Single ‘omics
(Multiple assays)
A,B,C,Q,R,S D,E,H
A,B,C,L,Q,R,S
A,B,C,L,M,Q,R,S
D,E,H
D,E,H
Gene & MicroRNA
Expression
SAGE Microarrays
Combinatorial Integration
(Multiple ‘omics)
F,G,O,P
F,G,I,J,K,O,P
F,G,I,J,K,N,O,P
Correlation of DNA methylation and gene expression (dataset
K
with matched DNA methylation and expression profiles)
Identify recurrent changes (copy number alterations, common
L
enrichment patterns [MeDIP, ChIP], regions of LOH)
Statistical comparison of patters of recurrent changes between
M
two groups using Fisher's exact test
Two-dimensional two-group comparisons (statistical comparison
N of expression profiles of genes in regions of difference identified
by Fisher's exact comparison)
Identify "And" events between three or more DNA-based
O dimensions (copy number, LOH, DNA methylation, histone
modification states)
Identify "Or" events between three or more DNA-based
P dimensions (copy number, LOH, DNA methylation, histone
modification states)
Q Cancer gene discovery
R Lists of genes for systems/function/pathway analysis
Linking to public biological databases (PubMed, NCBI Gene,
S OMIM, NCBI GEO Profiles, UCSC Genome Browser, Database
of Genomic Variants)
Figure 2.2. Data structure hierarchy. (a) Data hierarchy describing the relationship
between platforms, assays and 'omics disciplines. (b) Functionality map of SIGMA2. List of
the various functions and the output from that function that can be performed given the
number of samples or sample groups and dimensions. Multiple sample analysis (single
group and two group) are microarray platform independent. Functions listed in boxes are in
addition to those listed in the box preceding the arrows.

Figure 2.3
numSamples

37
Figure 2.4
b
d
c
e
a
Search for genes,
link to databases
Figure 2.4. SIGMA2 interface. Description of the SIGMA2 user interface using a single sample visualization as an
example. (a) Customizable toolbar with shortcut buttons, (b) Project/Analysis tree to track work within and between
sessions, (c) Main display area using tab-based navigation, (d) Information console and (e) Genome features tracks. Here, a
copy number change is displayed in the context of CpG islands (red), microRNAs (orange) and regions annotated in the
database of genomic variants (blue).

Figure 2.5. Consensus calling and heterogeneous array analysis. (a) Consensus calling
using multiple algorithms. Multiple algorithms (and different parameters) can be selected to
analyze a given array CGH sample and this can be defined for each array platform
independently as each platform may have exhibit different noise and ratio response
characteristics. (b) Heterogeneous array analysis using data from multiple array CGH
platforms. Sample from the Agilent 244K, Affymetrix SNP 500K and whole genome BAC array
were segmented to define areas of gain and loss. Subsequently, the results were aggregated
into a frequency histogram plot showing the common areas of gain and loss across the three
samples.
38

Figure 2.5
a
b
A�ymetrix
SNP 500K
Agilent
244K CGH
39
BCCA
WGTP 32K

Figure 2.6
HCC2218 HCC2218 HCC2218BL
Copy Number
Figure 2.6. Integrative genetic analysis of HCC2218. Parallel visualization and analysis
of the copy number and genotype profiles of the breast cancer cell line HCC2218. Genotype
profile of the matching normal blood lymphoblast line (HCC2218BL) is also provided to
define regions of LOH. DNA copy number profile was generated on the BCCA whole
genome tiling path BAC array and genotype profiles are from the Affymetrix SNP 10K array
{Zhao, 2004 #38}. This region of chromosome arm 3q has a defined segmental copy
number loss and the boundary of the change is evident from the LOH profile. In the genotype
profile, the horizontal blue lines indicate a SNP transition from heterozygous in normal
to homozygous in the tumor, indicating LOH.
40
LOH

Figure 2.7
a b
NSCLC SCLC
c
Figure 2.7. Two-group two dimensional comparison of 37 NSCLC and 16 SCLC
cancer cell lines. First, segmentation analysis is performed to delineate gains and losses
in each sample. Next, a statistical comparison of the distribution of gains and losses
between the two groups is done using the Fisher’s exact test. (a) Using the interactive
search, one of the regions of difference identified is on chromosome 7, with a NSCLC and
SCLC sample aligned next to each other. The NSCLC has a clear segmental gain of that
region, with the SCLC not having the gain. The right-most graph is a frequency plot summary
of two sample sets (NSCLC and SCLC). NSCLC is color-coded in red while SCLC in
green, and the overlap appears in yellow. The frequency of chromosome arm 7p gain is
higher in the red group. (b) A heatmap is shown representing 15 NSCLC and 15 SCLC gene
expression profiles, of the specific genes in the region highlighted in yellow. (c) When
examining gene expression data of EGFR specifically, a gene in this region, we can see that
the expression is drastically higher in NSCLC vs. SCLC, as predicted by the higher
frequency of gain in NSCLC vs. SCLC of that region. Gene expression data are represented
as log2 of the normalized intensities.
41

Figure 2.8. Multi-dimensional perspective of chromosome 17 of the HCC2218 breast
cancer cell line. Copy number, LOH, and DNA methylation, and profiling identifies an
amplification of ERBB2 coinciding with allelic imbalance and loss of methylation. When
examining the gene expression, the expression of HCC2218 is significantly higher than a panel
of normal luminal and myoepithelial cell lines [28].
42

43
Figure 2.8
DNA Copy Number Allelic imbalance (LOH) DNA Methylation
HCC2218 Luminal Myoepithelial

Table 2.1. Features required for integrative analysis
Features required for integrative
analysis
Nexus CGH
44
CGH Fusion
ISA-CGH
VAMP
*CGH
Analytics
Built-in segmentation for array CGH � � � � � � �
Consensus calling using multiple
segmentation algorithms
�
Array platform-independent
combined CGH analysis
� �
�
Custom microarray data handling � � � � � � �
Basic copy number and expression
integration
� � �
�
Alignment and analysis of genetic
and epigenetic data
Multi-dimensional visualization of
� � �
genetic, epigenetic and gene
expression data
�
Two group statistical comparison
Two group combinatorial gene
� � � �
dosage and gene expression
comparison
�
Linking to external biological
databases
� � � � � � � �
Linking to external gene expression
(GEOProfiles)
�
Context-based visualization of
genome features
� � � � �
Conversion of data between
different genome assemblies
� � � �
Free for academic/research use � � � � � �
MD-SeeGH
SIGMA
SIGMA 2

Table 2.2. Summary of Input, analysis, output for each dimension
'Omics
classification
Assay(s)
measured
Genomics Copy number Array CGH
Input Functionality*** Output
Segmentation
Direct thresholding
Moving average-based thresholding
Z-transformation of moving average
Whole genome visualization
45
Regions of gain and loss
Gene lists for further
analysis
High-resolution karyogram
images
Frequency histograms
Genomics LOH SNPs* LOH based on consecutive altered markers Regions of LOH
Genomics LOH
Microsatellite
markers
Same as above Same as above
Genomics
Epigenomics
Epigenomics
Epigenomics
Epigenomics
Copy number,
LOH
DNA
methylation
DNA
methylation
Histone
modification
states
DNA
methylation,
Histone
modification
states
Transcriptomics Gene
expression**
Transcriptomics Gene
expression**
Genomics,
Transcriptomics
Genomics,
Epigenomics
Genomics,
Epigenomics
Genomics,
Epigenomics,
Transcriptomics
Copy number,
Gene
expression
Copy number,
DNA
methylation
LOH, DNA
methylation
Copy number,
LOH, DNA
methylation,
Histone
modification
Gene Expression
MeDIP +
array CGH
Bilsulphitebased
ChIP-on-chip
Microarrays
SAGE
Identify regions of uniparental disomy
(UPD): LOH with no copy number change
Direct thresholding
Moving average-based thresholding
Z-transformation of moving average
Visualization against genome position
Thresholding of proportion of methylated
CpG’s
Direct thresholding
Moving average-based thresholding
Z-transformation of moving average
Epigenetic interplay
Heatmap visualization, clustering
Histograms
Statistical comparisons
Heatmap visualization, clustering
Histograms
Statistical comparisons
Correlation analysis of copy number and
expression
Statistical comparison of expression in
regions of copy number difference (two
group analysis)
Identify regions of concerted change in
BOTH copy number and methylation ("twohit")
Identify regions with change in copy number
OR DNA methylation
Identify allele-specific methylation events
Identify co-ordinate genetic, epigenetic and
gene expression changes
Regions of enrichment and
lack of methylation
Gene lists for further
analysis
Regions of enrichment and
lack of enrichment
Gene lists for further
analysis
Regions of mutually
exclusive change between
chromatin state and DNA
methylation
Expression of genes of
interested based on DNA
analysis
Expression of genes of
interested based on DNA
analysis
Genes whose expression
is strongly regulatd by copy
number
p-values for associations
p-values for group
comparison
Regions of allele specific
aberrant methylation
Genes altered at multiple
levels

2.6 References
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Albertson DG, Pinkel D, Marra MA et al: A tiling resolution DNA microarray with
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3. Khulan B, Thompson RF, Ye K, Fazzari MJ, Suzuki M, Stasiek E, Figueroa ME, Glass
JL, Chen Q, Montagna C et al: Comparative isoschizomer profiling of cytosine
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4. Lockwood WW, Chari R, Chi B, Lam WL: Recent advances in array comparative
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5. Rauch T, Li H, Wu X, Pfeifer GP: MIRA-assisted microarray analysis, a new
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other molecular profiles. Bioinformatics 2006, 22(17):2066-2073.
10. Chi B, deLeeuw RJ, Coe BP, Ng RT, MacAulay C, Lam WL: MD-SeeGH: a platform for
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11. Carrasco DR, Tonon G, Huang Y, Zhang Y, Sinha R, Feng B, Stewart JP, Zhan F,
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14. Lockwood WW, Chari R, Coe BP, Girard L, Macaulay C, Lam S, Gazdar AF, Minna JD,
Lam WL: DNA amplification is a ubiquitous mechanism of oncogene activation in
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15. Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, Clark L, Bayani N, Coppe JP,
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16. Stransky N, Vallot C, Reyal F, Bernard-Pierrot I, de Medina SG, Segraves R, de Rycke
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Spek PJ, Lowenberg B, Valk PJ: SNPExpress: integrated visualization of genomewide
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normalization of array CGH data. BMC Bioinformatics 2005, 6:274.
20. Neuvial P, Hupe P, Brito I, Liva S, Manie E, Brennetot C, Radvanyi F, Aurias A, Barillot
E: Spatial normalization of array-CGH data. BMC Bioinformatics 2006, 7:264.
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Chiba S, Bailey DK, Kennedy GC et al: A robust algorithm for copy number
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25. Bibikova M, Lin Z, Zhou L, Chudin E, Garcia EW, Wu B, Doucet D, Thomas NJ, Wang Y,
Vollmer E et al: High-throughput DNA methylation profiling using universal bead
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26. Coe BP, Ylstra B, Carvalho B, Meijer GA, Macaulay C, Lam WL: Resolving the
resolution of array CGH. Genomics 2007, 89(5):647-653.
27. van Wieringen WN, Belien JA, Vosse SJ, Achame EM, Ylstra B: ACE-it: a tool for
genome-wide integration of gene dosage and RNA expression data. Bioinformatics
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47

Chapter 3: An integrative multi-dimensional genetic and
epigenetic strategy to identify aberrant genes and pathways
in cancer 2
2 A version of this chapter has been published. Chari R, Coe BP, Vucic EA, Lockwood WW,
Lam WL. (2010) An integrative multi-dimensional genetic and epigenetic strategy to identify
aberrant genes and pathways in cancer. BMC Systems Biology, 4(1):67, 1-14. Please see the
published version of this chapter for all supplementary materials.
48

3.1 Background
Genomic analyses have substantially improved our knowledge of cancer. Gene expression
profiling, for example, is utilized to delineate subtypes of breast cancer, and has facilitated the
derivation of predictive and prognostic signatures [1-5]. However, not all of the gene expression
changes observed are causal to cancer development, and global gene expression analysis
alone cannot distinguish between causal and reactive changes. Corresponding alteration at the
DNA level is regarded as evidence of causality; for example, gene deletion or gene silencing by
methylation. Hence, examining genetic and epigenetic events in conjunction with the changes
in gene expression pattern should improve the identification of causal changes that lead to
disease phenotype.
Analysis of gene copy number alone has correlated breast cancer genome features with poor
prognosis based on the degree of genomic instability observed [6]. In terms of gene discovery,
specific genomic regions containing important loci have been shown to be frequently gained or
lost [7-11]. Integrative analyses of gene dosage and gene expression in breast cancer have
revealed specific genes which are deregulated at the gene expression level as a result of
changes in DNA copy number. From a global perspective, studies have shown a broad range
in concordance between DNA amplification and overexpression of genes. This variability is
attributable to the sensitivity of the methods used in detecting gene copy number and gene
expression changes as well as the number of genes examined [12-15]. Conversely, when
examining gene overexpression, it was found that only 10.5% of the overexpression could be
attributable to gene amplification [14]. It is certain that altered gene expression can not only be
attributed to disruption of regulatory/signaling cascades and downstream effects, but also to a
multitude of causal genetic and epigenetic aberrations.
We reason that by examining multiple genomic dimensions simultaneously, with a dimension
representing a genome wide assay measuring DNA level alterations such as gene copy number
or DNA methylation, we are likely to achieve the following: (i) explain a greater fraction of the
49

observed gene expression deregulation as compared with explaining expression deregulation
using only a single dimension, (ii) improve the discovery of critical oncogenes and tumor
suppressor genes (TSGs) by focusing on those genes altered simultaneously at multiple
genomic dimensions, and (iii) begin to understand the complex mechanisms of dysregulation of
oncogenic pathways. In this study, we demonstrate the power of an integrative genomics
approach by performing multi-dimensional analyses (MDA) of the genome, epigenome, and
transcriptome of breast cancer cell lines. We illustrate and demonstrate the need for integrative
analysis of multiple genomic dimensions by showing the co-operative contribution of DNA
mechanisms to explaining differential gene expression. Using a strategy to identify genes
exhibiting congruent alteration in copy number, DNA methylation, and allelic (or loss of
heterozygosity, LOH) status, which we term multiple concerted disruption (MCD) analysis, we
find genes representing key nodes in pathways as well as genes which exhibit prognostic
significance. In examining the neuregulin pathway, we observe the variability among samples
in the mechanism of dysregulation of this commonly altered breast cancer pathway, highlighting
the importance of multi-dimensional correlative analysis of a given pathway in individual tumor
samples -- in addition to the conventional approach of identifying loci simply based on frequency
of disruption in a cohort. Finally, examining the subset of triple negative breast cancer cell
(TNBC) lines, we show that a downstream target of FGFR2, a recently implicated oncogene in
TNBC, COL1A1 is frequently affected by MCD even though in FGFR2 itself is rarely affected.
Notably, this is the first such in-depth genomic, epigenomic, and transcriptomic analyses of
breast cancer.
3.2 Methods
3.2.1 Data generation and acquisition
Commonly used breast cancer (HCC38, HCC1008, HCC1143, HCC1395, HCC1599, HCC1937,
HCC2218, BT474, MCF-7) and non-cancer (MCF10A) cell lines were selected for analyses
(Additional File 1 or Appendix II). Copy number profiles were obtained from the SIGMA
50

database [11, 16]. These profiles were generated using a whole genome tiling path microarray
CGH platform [17, 18]. Expression profiles for BT474 and MCF-7 were obtained from the NCI
Cancer Biomedical Informatics Grid (caBIG, https://cabig.nci.nih.gov), MCF10A profile from
GEO (GSM254525), and the rest were generated using Affymetrix U133 Plus 2.0 platform at the
McGill University and Genome Quebec Innovation Centre. Affymetrix 500K SNP array data
were obtained from caBIG. DNA methylation profiles were generated using the Illumina
Infinium methylation platform at the Genomics Lab, Wellcome Trust Centre for Human
Genetics. A summary of the sources of all the data used is provided in Additional File 2 or
Appendix III. Gene expression and methylation data generated were deposited in NCBI GEO
(GSE17768 and GSE17769).
3.2.2 Data processing and normalization
Array CGH data were normalized using a stepwise normalization framework [19]. In addition,
data were filtered based on a stringent standard deviation cut-off of 0.075 between replicate
spots, with those exceeding this cut-off excluded from further analysis. To identify regions of
gain and loss, smoothing and segmentation analysis was performed using aCGH-Smooth [20]
as previously described [21]. Copy number status for clones which were filtered from above
were inferred using neighboring clones within a 1 Mb window.
Affymetrix SNP array data were normalized and genotyped using the "oligo" package in R,
specifically using the crlmm algorithm for genotyping [22]. Genotype calls whose confidences
were less than 0.95 were termed "No Call" (NC). Subsequently, genotype profiles were
analyzed using dChip [23] and LOH was determined using a panel of 60 normal genotypes from
the HapMap dataset [24] as provided by dChip, as matching blood lymphoblast profiles were
not available. LOH ("L"), Retention ("R"), and No Call ("N") status was determined for every
marker in each sample. Analysis parameters used were as specified in the dChip manual.
51

Raw gene expression profiles from all ten cell lines were normalized using the "rma" package in
R (Additional File 3). Gene expression data were further filtered using the Affymetrix MAS 5.0
Call values ("P","M", and "A"). Since the comparison of differential expression was one cancer
line to one normal, both call values could not be "Absent" in order to be retained for analysis.
Methylation data were normalized and processed using Illumina BeadStudio software
(http://www.illumina.com/software/genomestudio_software.ilmn, Illumina, Inc., San Diego, CA,
USA). Beta-values and confidence p-values were retained for further analysis. Beta-values
with associated confidence p-values > 0.05 were excluded. Data from all genomic dimensions
were mapped to the hg18 (March 2006) genome assembly.
3.2.3 Strategy for integrative analysis
Copy number and LOH profiles were mapped to genes using the mapping of the Affymetrix
U133 Plus 2.0 platform as well as the UCSC Genome Browser [25]. Methylation data were
linked to the other three types of data using either the RefSeq gene symbol as specified by the
Illumina mapping file (Illumina), or the RefSeq accession number. Differential expression was
determined by subtracting the expression value in the non-malignant line MCF10A from the
value in each cancer line. Since the obtained gene expression values after RMA normalization
were represented in log2 space, a gene was considered differentially expressed if the difference
between the cancer line and MCF10A was greater than 1, which corresponded to a two-fold
expression difference. DNA methylation status was determined by subtracting beta-values, with
hypermethylation defined as a positive difference between tumor and normal (≥ 0.25) and
hypomethylation defined as a negative difference between tumor and normal (≤ -0.25). Briefly,
a beta value for a given CpG site ranges from 0 to 1 and represents the ratio of the methylated
signal over the total signal (methylated plus unmethylated signal). These thresholds are
comparable to those used in previous studies using an earlier Illumina methylation platform [26].
Using this mapping strategy, 12,910 unique genes were mapped across platforms
corresponding to 24,708 of the ~27,000 Illumina Infinium probes and to 27,053 probes of the
52

Affymetrix U133 Plus 2.0 platform. Visualization of multi-dimensional data was performed using
the SIGMA2 software [27].
To determine the genetic events that caused (or could explain) gene expression status, we first
identified a set of overexpressed and underexpressed genes for each cell line sample relative to
MCF10A based on differential expression criteria mentioned above. Each cancer sample may
have a different number of differentially expressed genes. Second, for each differentially
expressed gene in each sample, we examined the copy number status, methylation status, and
allelic status. A differential expression was considered "explained" when the observed
expression change matched the expected change at the DNA level. If a gene was
overexpressed, the causal copy number status would be a gain, DNA methylation status would
be hypomethylation, or allelic status would be allelic imbalance. Conversely, if a gene was
underexpressed, the causal copy number status would be a loss, DNA methylation status would
be hypermethylation, or allelic status would be LOH. From this point forward, when a change in
allele status with overexpression is discussed, it will be denoted as allelic imbalance (AI).
Conversely, for underexpression, a change in allele status will be denoted as loss of
heterozygosity (LOH). While changes in methylation or changes in gene dosage leading to
differential expression are more commonly discussed, previous studies have shown that
changes in allele status without change in copy number (copy neutral AI or LOH) can also lead
to differential gene expression due to preferential allelic expression [28-30].
3.2.4 Multiple concerted disruption (MCD) analysis
To determine what are likely key nodes in pathways and functions, we hypothesize that, in
addition to being altered frequently (by one mechanism or multiple mechanisms), these genes
also exhibit multiple concerted disruption (MCD) in a given sample. That is, a congruent
change in gene copy number (gain or loss) accompanied by allelic imbalance and change in
DNA methylation (hypomethylation or hypermethylation) resulting in a change in gene
expression (over or underexpression). Moreover, the MCD events would be used as a similar
53

screening approach to gene amplifications (multi-copy increases) or homozygous deletions
whereby the expectation is that these events would occur at a lower frequency than disruptions
through one mechanism alone and observation of these events would signify importance to the
genes in question.
In this study, the MCD strategy can be broken down into four sequential steps. First, using a
pre-defined frequency threshold, we identify a set of the most frequently differentially expressed
genes. Second, we identify the most frequently differentially expressed genes from step 1
whose expression change is frequently associated with concerted change in at least one DNA
dimension (either DNA copy number, DNA methylation or allelic status) within the same sample.
Next, we further refine this subset of genes from step 2 by selecting those having concerted
change in all dimensions in the same sample which we term as MCD. Finally, we introduce an
additional level of stringency by requiring a minimum frequency of MCD in the given cohort. At
the end of the process, we identify a small subset of genes which exhibit disruption through
multiple mechanisms and show consequential change in gene expression.
3.2.5 Simulated data analysis
Using the status of DNA alteration and expression for every gene in every sample, data within
each sample were shuffled and randomized ten times to create ten simulated datasets. Each
dataset was analyzed for overall disruption frequency and MCD and all results were then
aggregated to determine the frequency distribution of different thresholds observed in the
randomized data analysis.
3.2.6 Pathway enrichment analysis
For pathway analysis, Ingenuity Pathway Analysis software was used (Ingenuity Systems, CA,
USA). Specifically, the core and comparison analyses were used, with focus on canonical
signaling pathways. Briefly, for a given function or pathway, statistical significance of pathway
enrichment is calculated using a right-tailed Fisher's exact test based on the number of genes
54

annotated, number of genes represented in the input dataset, and the total number of genes
being assessed in the experiment. A pathway was deemed significant if the p-value of
enrichment was ≤ 0.05 (adjusted for multiple comparisons using a Benjamini-Hochberg
correction).
3.2.6 Survival and differential gene expression analysis in publicly available datasets
For survival analysis, Kaplan-Meier analysis was performed using the statistical toolbox in
Matlab (Mathworks). For each gene, the expression data were sorted from lowest to highest
expression across the sample set and survival times were compared between the top 1/3 and
bottom 1/3 of the samples. Two publicly available gene expression microarray datasets with
survival data were utilized for this analysis [4, 31]. For the Sorlie et al dataset, individuals whose
cause of death was not breast cancer were excluded from the analysis and missing data due to
quality control issues were filled using the knn method in the “impute” package in Bioconductor
[32]. Of the 23 genes selected by our MCD analysis (see Results), 17 were represented in
either dataset. Survival distributions were compared using a log rank test and two-tailed p-
values unadjusted for multiple comparisons were reported.
Subsequently, these 17 genes were further evaluated for differential expression in publicly
available expression datasets of clinical breast cancer samples using the Oncomine database
[33].
3.3 Results and discussion
3.3.1 Analysis of individual genomic dimensions
When examining each genomic dimension alone, we see that many of the common features
identified are consistent with the current knowledge of breast cancer genomes, for example,
previously reported chromosomal regions of frequent copy number gain, segmental loss and
loss of heterozygosity (LOH) / allelic imbalance (AI) (Figure 3.1a) [6, 8, 11, 12, 34]. While
55

many regions of frequent LOH/AI do overlap with regions of copy number change, others are in
regions of neutral copy number. Key genes implicated in breast cancer reside in these specific
regions and are altered expectedly (Figure 3.1b).
3.3.2 Multi-dimensional analysis (MDA) reveals a higher proportion of intra-sample
deregulated gene expression can be explained when more dimensions are analyzed
The impact of integrative, multi-dimensional analysis on gene discovery is observed at two
levels: (i) within an individual sample as well as (ii) across a set of samples. Within a given
sample, we see that by sequentially examining more genomic dimensions at the DNA level, i.e.
gene dosage, allelic status, and DNA methylation, we can explain a higher proportion of the
differential gene expression changes observed. Interestingly, although this proportion may vary
between samples, it always increases with every additional dimension examined (Figure 3.2a).
For example, in HCC1395, a single genomic dimension alone can explain as much as 64.4% of
overexpression but when using all three DNA based dimensions, whereby gene overexpression
can be explained by disruption at the DNA level in at least one dimension, as much as 75.7% of
aberrant overexpression can be explained. Similarly, in HCC1937, an increase from 56.9% to
74.7% explainable underexpression is observed when moving from one to three genomic
dimensions respectively. Conversely, in HCC2218, we observe 44% and 36% of
overexpression and underexpression respectively when using all three DNA dimensions. This
suggests that the majority of differential expression in sample HCC2218 is most likely a result of
complex gene-gene trans-regulation and consequently, highlights the individual differences
between samples.
3.3.3 MDA reveals genes are disrupted at higher frequencies when examining multiple
dimensions as compared to any single dimension alone
When considering across a sample set, we see that analysis of multiple genomic dimensions
leads to the discovery of more disrupted genes than what would be detected using a single
dimension of analysis alone. For each identified gene, we gain insight in how multiple
56

mechanisms are complementary in gene disruption (Figure 3.2b). For example, the tumor
suppressor gene caspase 1 (CASP1) has been thought to be deactivated through DNA
hypermethylation in multiple cancer types [35, 36]. The gene is underexpressed in all nine
cases examined in this study. In a subset of these cases, the observed underexpression can
be attributed to copy number loss. Interestingly, in the remaining cases, DNA hypermethylation
and copy neutral LOH are observed. Similarly, in another example, GNAS is differentially
expressed in all nine cases, with a subset of cases showing concerted copy number change
while the remaining cases reveal concerted change in DNA methylation. Notably, our
conclusion is supported by recent studies of glioblastoma, that also showed higher than
expected disruption frequencies of specific genes when multiple genomic dimensions were
analyzed [37, 38]. These examples illustrate how deregulated genes can be detected in more
cases when multiple, but complementary, approaches are used.
Until very recently, multi-dimensional genomic analysis typically represented the parallel
examination of gene dosage and gene expression. To demonstrate the power of examining
multiple dimensions, we examine the frequency of gene expression deregulation explained by
congruent alteration at the DNA level. Briefly, for each gene, a sample is determined to have a
DNA explained gene expression change if any of the following criteria are met; gene
overexpression should be accompanied with either (i) copy number gain, (ii) copy neutral allelic
imbalance, or (iii) hypomethylation and gene underexpression should be accompanied with
either (i) copy number loss, (ii) copy neutral LOH, or (iii) hypermethylation.
To determine an appropriate frequency of disruption threshold, ten random, simulated datasets
were generated and a distribution plot was generated for all of the observed frequencies from
0/9 to 9/9 across all simulations (Figure 3.3a). The proportion of observed frequencies ≥ 5/9
was 0.086 but for ≥ 6/9, the proportion was 0.020. Thus, since the 6/9 threshold was the first
threshold ≤ 0.05, 6/9 was used for further analysis. Using this threshold, we found that 437
differentially expressed genes have a corresponding change in gene dosage. Scaling this
57

approach to examining the whole genome at multiple dimensions, we anticipate identifying more
disrupted genes. When we added the remaining dimensions to account for differential
expression, at the same frequency cut-off, we identified the mechanism of disruption for 1162
deregulated genes (Figure 3.3b, Additional File 4).
The impact of multi-dimensional integrative analysis on cancer gene discovery is the enhanced
detection of genes which are disrupted by multiple mechanisms but at lower frequencies for
individual mechanisms. Collectively, the detection of gene dosage, allelic conversion and
change in methylation status enable the identification of such genes as frequently disrupted.
Using the list of 1162 genes, the distributions of alteration frequencies for each genomic
dimension or combination of dimensions were assessed (Figure 3.4a). Examining the median
frequencies in each box plot, there is a sequential increase in the median as more dimensions
are examined. This point can be further validated using specific genes. For example, the CD70
and ENG genes are underexpressed in the majority of samples. Using copy number analysis
alone, the observed frequency of disruption (loss and underexpression) is 44% and 22%
respectively. If we then examine the methylation status, in the remaining cases not explained
by DNA copy number, we observe an additional 33% of cases exhibiting hypermethylation and
underexpression for ENG (red) and 22% for CD70 (blue). Finally, when we also examine allelic
status, we observe an additional 22% of cases with copy neutral LOH and gene
underexpression for CD70 and 11% for ENG. In total, by using all three dimensions, the
cumulative frequency of disruption is 88% for CD70 and 77% for ENG (Figure 3.4b). This
example demonstrates the utility of a multi-dimensional approach to elucidate events which
would escape conventional single dimensional analysis.
3.3.4 MDA identifies significantly enriched cancer related pathways
Using the set of 1162 genes identified by MDA (Additional File 4) and the similar lists of genes
identified from each of the simulated datasets, pathway analyses were performed with Ingenuity
Pathway Analysis. From the pathway analysis of MDA genes and focusing only on canonical
58

signaling pathways, 53 pathways were significantly enriched for at a Benjamini-Hochberg
corrected p-value of 0.05 (Additional File 5). In contrast, using the gene lists from the 10
simulated datasets, nine of the 10 pathway analyses yielded no significant pathways enriched
for at the same p-value with one of the pathway analyses yielding one significant pathway.
Similar results from Gene Ontology analysis were obtained using the publicly available
GATHER database [39] (Additional File 6). Specific pathways involved in breast cancer,
ovarian cancer, and prostate cancer were amongst the ones identified as most significant
(Figure 3.5). Consequently, these results suggest that the genes identified using MDA have a
high degree of biological relevance.
3.3.5 MDA of the Neuregulin signaling pathway reveals a complex pattern of deregulation
Among the 53 pathways which were statistically over-represented from our list of 1162 genes,
one of the pathways identified is the neuregulin pathway. This pathway contains the well known
breast cancer oncogene ERBB2 as well as other genes known to be affected in breast and
other cancers [40-43]. Examining the components of this pathway, we observe that some are
genes commonly altered while others are infrequently altered across our sample set by multiple
patterns of genomic alteration, and some genes which behave oppositely in different samples
(Figure 3.6).
While genes such as HRAS (down), BAD (down), HSP90AB1 (up), SOS2 (up) and RPS6KB1
(up) generally exhibit consistent differential expression with concerted change at the DNA level
across our sample set, genes such as GRB7, PTEN, and MAP2K1 exhibit both overexpression
and underexpression, with concerted DNA change, in different samples. For example, if we
examine PTEN, we observe copy number loss, LOH, DNA hypermethylation and consequent
underexpression in HCC1395 while HCC1008 contains copy number gain, with DNA
hypomethylation and consequent overexpression (Figure 3.7). The impact of such a difference
on a downstream targets was recently shown in a breast cancer study where AKT and mTOR
phosphorylation were higher in cases with low PTEN expression compared to those with high
59

PTEN expression [44]. Using this pathway as an example, though average features across a
sample set are important, those differences between samples in the same pathway may also
play an important role and thus, may have a consequence on the biology of the tumor.
3.3.6 Genes exhibiting multiple concerted disruption (MCD) - biological and clinical
significance
We have demonstrated that we can identify more disrupted genes in a given sample when
considering any mechanism of disruption. On the other hand, those genes which exhibit
multiple concerted disruptions (MCD) across all DNA dimensions -- i.e. overexpression of a
gene due to increased gene dosage, which led to allelic imbalance, and DNA hypomethylation
at the same locus relieving regulation -- may likely have strong biological significance.
Likewise, underexpression due to reduced gene copy number, resulting in LOH, and
complementary DNA hypermethylation, leading to gene silencing may also be significant. By
employing multiple dimensions of interrogation, genes exhibiting MCD are captured.
To determine what frequency of MCD was deemed significant, we performed a similar analysis
of the 10 simulated datasets from before and assessed the proportion of events at each
frequency of MCD from 0/9 to 1/9 (Figure 3.8a). It was found that by random chance, a gene
exhibiting MCD in 1/9 would occur 0.3% of the time. Thus, using this threshold of at least one
MCD event, 974 genes were identified (Additional File 7). Interestingly, the overlap of the
MDA list (1162 genes) with the MCD list (974 genes) yielded 375 genes.
The MCD strategy sequentially refines the roster of target genes with the intent of identifying
critical genes for tumorigenesis (Additional File 8 or Appendix IV). Such genes which exhibit
multiple mechanisms of deregulation, for example, may represent important nodes in pathways
such as hub proteins [45], whereby disruption of the gene has an effect on multiple downstream
targets or genes with biological and/or clinical relevance. Thus, although these genes may not
be affected at a high frequency across the sample set, their disruption at multiple levels in
60

individual samples would signify importance in tumorigenesis. As shown earlier, 375 genes
identified by both MDA and MCD. If we further employed a criterion of frequent MCD, whereby
this event occurs in 4/9 of cases (signifying high recurrence), we detect 23 genes (Additional
File 8 or Appendix IV). Among the 23 genes identified are TUSC3 (8p22), ELK3 (12q23), and
CCNA1 (13q12.3-q13).
TUSC3 resides at 8p22, a locus frequently deleted across multiple epithelial cancers [46-49].
ELK3 is an ETS domain transcription factor which, in mice, acts as a transcriptional inhibitor in
the absence of RAS, but is a transcriptional activator in the presence of RAS [50]. Recently,
ELK3 was shown to be underexpressed in a panel of breast cancer lines as well clinical breast
tumor specimens [51]. CCNA1 was shown to be hypermethylated in multiple cancer types,
including breast cancer [52].
To validate the relevance of the 23 MCD genes in clinical breast cancer samples, we evaluated
gene expression levels associated with survival and examined multiple publicly available
microarray datasets using the Oncomine database [33]. Of these 23 genes, 17 were
represented in either the van de Vijver et al or Sorlie et al datasets. Interestingly, eight of these
genes, demonstrated a statistically significant association with patient survival in at least one of
the two independent datasets (Additional File 9 or Appendix V, Additional File 10 or
Appendix VI) [4, 31]. Moreover, when comparing the percentage of survival-associated genes
(8/17, 47.1%) in the MCD gene list with what was expected without pre-selection (27.1%), the
increased percentage was statistically significant based on the binomial test (p = 0.04131806).
To further evaluate the clinical significance of these genes, we utilized the Oncomine database
(Additional File 9 or Appendix V). It should be noted the caveat of the Oncomine analysis is
that it may not detect all low levels of differential expression. TUSC3 is shown as an example of
one of the genes whose expression correlates with survival (Additional File 8 or Appendix IV,
see Methods). Notably, in ovarian cancer, TUSC3, in conjunction with EFA6R, also correlated
with poor survival [53]. The observations that TUSC3 is altered frequently by multiple
61

mechanisms at the DNA and RNA level and shows a strong association with patient survival,
highlight the use of MCD in systematically identifying biologically, and potentially clinically,
relevant genes.
3.3.7 Association of genes exhibiting MCD and triple negative breast cancers (TNBC)
In this study, the majority of samples used (5/9) were of the triple negative subtype of breast
cancer; a subtype which is estrogen receptor (ER) negative, progesterone receptor (PR)
negative, and HER2 negative and represents between 10% and 20% of all diagnosed breast
malignancies [54-57] . Genomic analyses of triple negative breast cancers (TNBCs) have been
previously performed [58-61] and they revealed a heterogeneous and complex view of this
breast cancer subtype. A recent study, however, had implicated fibroblast growth factor
receptor 2 (FGFR2) as novel therapeutic target amplified in TNBCs [57]. Interestingly, from a
meta-analysis of array CGH data, this gene was found to be amplified in 4% of TNBC cases
[57]. Thus, we assessed the status of FGFR2 and its downstream targets in our multi-
dimensional dataset.
While FGFR2 is not amplified in any of the five TNBC cell lines, all of the five cell lines showed
overexpression of FGFR2 with one of the cell lines exhibiting a low level gain of a region
encompassing FGFR2 (HCC1937). From this analysis, within the sample set of TNBC cell
lines, though FGFR2 is overexpressed, it was not frequently associated with DNA level
alterations.
However, examining downstream targets of FGFR2 revealed a striking finding. Using the
knowledge database of Ingenuity Pathway Analysis, one of the downstream components
affected at the expression level, which was also on both the MDA (Additional File 4) and MCD
(Additional File 7) lists, was COL1A1. Remarkably, of the five TNBC cell lines, four exhibited
DNA alteration associated overexpression of COL1A1 with two lines exhibiting MCD at COL1A1
and two other lines having DNA copy number associated overexpression. The remaining line
62

exhibited DNA copy number associated overexpression of FGFR2 (Figure 3.8b). Hence, every
TNBC line was affected at either FGFR2 or COL1A1 at both the DNA and RNA levels.
Interestingly, COL1A1 has been shown to be both prognostic and predictive in multiple cancer
types, including breast cancer [3, 5, 62, 63].
3.4 Conclusions
In conclusion, we have demonstrated that a multi-dimensional genomic approach is superior to
analysis of one or two genomic dimensions alone. Each additional genomic dimension
surveyed increases the amount of aberrant gene expression that can be explained within
individual samples. As a by-product, when examining across a sample set, multi-dimensional
genomic analysis can identify relevant genes that may be overlooked due to low frequencies of
disruption by the individual mechanisms. The increased frequency of gene disruption detected,
due to the consideration of multiple mechanisms of disruption, could potentially reduce the
sample size of study cohort needed for gene discovery.
Secondly, while the increased detection of genes disrupted using multi-dimensional analysis is
useful for achieving a more comprehensive identification of deregulated pathways and gene
networks, it also presents a challenge in prioritizing which genes are likely key nodes or hubs in
the affected pathways and networks. Hence, one way to prioritize is to identify genes with
evidence of multiple concerted disruption. The Knudson two-hit hypothesis suggests that tumor
suppressor genes require two allelic hits to disrupt gene function. Bi-allelic alteration, such as
homozygous deletion, or concerted genetic and epigenetic changes, are well documented
causal mechanisms of gene disruption. Likewise, hypomethylation and increased gene dosage
are known mechanisms for gene overexpression. The bi-allelic disruption phenomenon
(leading to loss or gain of function) provides a means to identify causative genes; hence,
parallel analysis of the genome and epigenome in the same tumor is of great benefit. In this
study, we have developed a stepwise gene selection strategy to identify multiple concerted
disruptions using an integrative genomics approach.
63

In this study, three DNA dimensions, which have current affordable high throughput assays,
were examined. However, we envision that new techniques for analysis of additional aspects
such as histone modification states and gene mutation status will reveal mechanisms that would
explain even more gene expression changes within individual samples. The identification of a
number of key cancer-related genes and pathways using a relatively small sample size
suggests that limitations in requiring large sample sizes for studies to identify relevant genes
and pathways may be circumvented by our comprehensive approach. Consequently, this
concept can be projected to current technologies such as high throughput sequencing where it
may prove more prudent to perform this analysis in multiple dimensions in a smaller number of
samples rather than in one dimension in many more samples at a comparable cost. Finally,
observing the same gene in a given pathway being deregulated in a completely different
manner between samples highlights one of the shortcomings of group-based analysis and
highlights the eventual need to move to systems analysis of tumors as individual entities.
64

Figure 3.1
a
CN Gain
Frequency
CN Loss
Frequency
LOH
Frequency
CN Neutral LOH
Frequency
b
BRCA2
ESR1
ERBB2
BRCA1
1
0.5
0
0
1
0.5
0.5 1 1.5 2 2.5 3
0
0
1
0.5 1 1.5 2 2.5 3
0.5
0
0
1
0.5 1 1.5 2 2.5 3
0.5
ESR1
0
0 0.5 1 1.5 2 2.5 3
HCC38
HCC1008
HCC1143
HCC1395
HCC1599
HCC1937
HCC2218
BT474
MCF7
TP53
Genomic Position (Gbp)
HCC38
HCC1008
HCC1143
HCC1395
HCC1599
HCC1937
Copy Number Gain
Copy Number Loss
LOH
Retention (no LOH)
Figure 3.1. Genomic profiles of breast cancer cell lines. (a) Whole genome frequency
analysis copy number gain (red), copy number loss (green), loss of heterozygosity/allelic
imbalance (AI) (top blue) and copy number neutral LOH/AI (bottom blue). Vertical lines
through all four graphs represent the genomic location of key breast cancer genes, using the
hg18 build of the human genome map. (b) Illustration of copy number and LOH/AI status for
ESR1, BRCA1, BRCA2, ERBB2 and TP53 in each of the samples. Each of these DNA
events is evident in all of these genes.
65
HCC2218
BT474
MCF7
BRCA2
TP53
ERBB2
BRCA1

Figure 3.2. Quantitative and qualitative benefits of integrative analyses. (a) Heatmap and
bar plot illustration of the additive benefit of multi-dimensional DNA analysis for the explanation
of consequential differential gene expression. Within a sample, when sequentially adding a DNA
dimension of analysis, an increasing percentage of observed differential gene expression can
be explained. For each dimension or combination of dimensions, in the bar plot, the median
value is used (grey bars). Heatmaps display the percentage of differential expression explained
by DNA mechanisms, with values near to 100 either dark red (overexpression) or green
(underexpression) and values closer to 0 in white. (b) Two specific genes GNAS and CASP1
are given as examples to show multiple and complementary mechanisms of gene disruption,
illustrating the importance of multi-dimensional analysis (MDA).
66

67
Figure 3.2
a
Hypo
AI
CNG
CNG Or Hypo Or AI
Hyper
LOH
CNL
CNL Or Hyper Or LOH
b
GNAS
Gene Expression
DNA Copy Number
DNA Methylation
Allelic Status
CASP1
Gene Expression
DNA Copy Number
DNA Methylation
Allelic Status
HCC38
HCC1008
HCC1143
HCC1395
HCC1599
HCC1937
HCC2218
BT474
0.197 0.266 0.176 0.134 0.203 0.171 0.144 0.194 0.180
0.319 0.325 0.325 0.337 0.215 0.421 0.132 0.271 0.122
0.708 0.401 0.372 0.644 0.440 0.464 0.321 0.458 0.500
0.821 0.686 0.655 0.757 0.612 0.743 0.435 0.679 0.629
0.103 0.062 0.126 0.236 0.145 0.161 0.183 0.172 0.166
0.425 0.512 0.516 0.523 0.316 0.569 0.197 0.348 0.226
0.367 0.584 0.573 0.499 0.408 0.473 0.203 0.549 0.419
0.522 0.705 0.790 0.702 0.562 0.747 0.363 0.721 0.558
HCC38
HCC1008
HCC1143
HCC1395
HCC1599
HCC1937
HCC2218
BT474
MCF7
MCF7
0 0.2 0.4 0.6 0.8
Proportion of Overexpression Explained
0 0.2 0.4 0.6 0.8
Proportion of Underexpression Explained
Legend:
GE: Gene Expression: Over Under
CN: DNA Copy Number: Gain Loss
L: Allelic Status: LOH
M: DNA Methylation: Hypo Hyper

Figure 3.3
a
Proportion of genes in
random simulations
b
0.30
0.25
0.20
0.15
0.10
0.05
CN Or AI/LOH Or Meth
CN
0
Meth
AI/LOH
0 1 2 3 4 5 6 7 8 9
Disruption frequency
0 200 400 600 800 1000 1200
# of genes at 6/9 cut-o�
Simulated Data
Experimental Data
1400
Figure 3.3. Determination and application of a disruption frequency threshold. (a)
Results of the analyses of ten simulated datasets. Aggregating the results of the simulated
analyses, the proportion of random simulations at the observed frequency thresholds are
shown. From these analysis, approximately 2% of the simulations were ≥ 6/9. (b) Using a
frequency cut-off of 6/9, the number of genes disrupted at that frequency using a single or
combination of DNA dimensions. With a single dimension alone, we can maximally identify
437 genes which are differentially expressed and exhibit a concerted change at the DNA
level in a minimum of 6/9 samples. However, using all three dimensions, we find that 1162
genes are in fact differentially expressed and contain at least one concerted change in one
of the DNA dimensions. This represents over a two-fold increase in the number of genes
identified.
68

Figure 3.4
a
b
Disruption Frequency
Cumulative Frequency
9
8
7
6
5
4
3
2
1
0
DNA
Methylation
9
8
7
6
5
4
3
2
1
0
Copy Number
LOH
DNA Methylation
Copy Number
LOH
Copy Number Or LOH
AI/LOH Or
DNA Methylation
Copy Number
Or AI/LOH
Copy Number Or
DNA Methylation
Copy Number Or
DNA Methylation
Copy Number Or
AI/LOH Or
DNA Methylation
LOH Or DNA Methylation
Frequency
threshold
Copy Number Or
LOH Or DNA
Methylation
Figure 3.4. Impact of multi-dimensional analysis on low frequency events. (a) Box
plot analysis of the frequency distribution of single and multi-dimensional analyses (MDA) of
the 1162 genes differentially expressed with a concerted change in one of the DNA dimensions.
The area in red represents the number of genes (of the 1162) that would be missed if
only a single DNA dimension was examined, while the area in blue represents the genes
that would be detected. Examining the median values for the three right-most boxes, we
see that by even using the box with the highest median (copy number), we would not be
able to detect about 50% of the 1162 genes. (b) Two specific examples highlighting the
importance of multi-dimensional genomic analysis. Using single dimensional analyses
(green shade) alone, CD70 (blue line graph) and ENG (red line graph) disruption occur at
very low frequencies (44% and 33% respectively). However, when examining two (red
shade) or three genomic dimensions (blue shade), the disruption of these genes occurs at
very high frequencies, 88% and 77% respectively. Frequency threshold of 6/9 is denoted
with a black dotted line.
69

70
Figure 3.5
-log(pvalue)
5.0
4.0
3.0
2.0
1.0
0.0
Molecular Mechanisms
of Cancer
Cell Cycle: G1/S Checkpoint
Regulation
Aryl Hydrocarbon
Receptor Signaling
Breast Cancer Regulation
by Stathmin1
Legend:
Multi-Dimensional Analyis
Simulated Data Sets
Ovarian Cancer Signaling
Prostate Cancer Signaling
p53 Signaling
Neuregulin Signaling
PI3K/AKT Signaling
Threshold
Cell Cycle: G2/M DNA Damage
Checkpoint Regulation
Figure 3.5. Pathway analysis of the 1162 genes identified by multi-dimensional analysis. Ingenuity Pathway
Analysis of the 1162 genes identified by MDA as well as genes meeting the same frequency criteria (6/9) from the
analysis of the ten simulated datasets. In total, using the list of 1162 MDA genes, 53 canonical signaling pathways
were identified as significant after multiple testing correction using a Benjamini-Hochberg correction (Additional File
5). In contrast, using the same statistical criteria, nine of the 10 simulated datasets yielded no significant pathways
with one of the datasets yielding one pathway. In this figure, ten of the most well known, cancer-related pathways are
shown. The yellow threshold line represents a Benjamini-Hochberg corrected p-value of 0.05 with bars above that
line deemed significant.

Figure 3.6
*
*
ERBB2
ERBB2
HSP90AB1
GRB2
SOS2
HRAS
RAF1
MAP2K1
ERK1/2
ERK1/2
ELK1
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
MYC
Proliferation &
Differentiation
EREG
ERBB2
PRKCI
ERBB4
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
*
GRB7
ERBB2IP
STAT5
ERRFI1
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
Cell Cycle
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
Mitogenic
Signalling
Figure 3.6. Complex deregulation of the Neuregulin/ERBB2 signaling pathway. Each
gene is color-coded red and green to represent over and underexpression respectively.
Genes colored both represent genes which are over and underexpressed in different
samples. Beside each gene is the status for gene expression, copy number, LOH/AI and
DNA methylation, with the alterations in each dimension colored as per the legend. DNA
alterations are only shown when a change in gene expression is observed. It should be
noted that LOH can be derived from multiple mechanisms. In this study, we do not distinguish
between the which mechanisms. Likewise, methylation changes may affect one or
both alleles. In this study, we do not distinguish the status of the alleles individually. Genes
denoted with * have one sample exhibiting multiple concerted disruption (MCD). Samples
are coded as follows: S1 = HCC38, S2 = HCC1008, S3 = HCC1143, S4 = HCC1395, S5 =
HCC1599, S6 = HCC1937, S7 = HCC2218, S8 = BT474, and S9 = MCF7.
71
PDK1
AKT2
mTOR
RPS6KB1
RPS6KB1
*
PIK3R1
*
*
ERBB4
EREG
PIP2 PIP3
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
ERBB4
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
*
PTEN
BAD
CDKN1B
RPS6
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
PI3K-AKT
Signalling
Survival &
Proliferation
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
S1 S2 S3 S4 S5 S6 S7 S8 S9
GE
CN
L
M
Legend:
GE: Gene Expression: Over Under
CN: DNA Copy Number: Gain Loss
L: Allelic Status: LOH
M: DNA Methylation: Hypo Hyper

72
Figure 3.7
Sample: HCC1008
Copy Number Gain Retention
Beta value
Log2 Intensity
0.4
0.2
0
12
8
4
0
DNA Methylation
MCF10A HCC1008
Gene Expression
MCF10A HCC1008
Sample: HCC1395
PTEN
Copy Number Loss LOH
Log2 Intensity
0.8
0.4
Beta value 1.2
0
10
8
6
4
2
0
DNA Methylation
MCF10A HCC1395
Gene Expression
MCF10A HCC1395
Figure 3.7. Deregulation of PTEN occurs differently between samples. In HCC1008 (left), PTEN is overexpressed with an
associated gain in copy number and hypomethylation. Conversely, in HCC1395 (right), PTEN is underexpressed, with an
associated loss in copy number, LOH, and DNA hypermethylation. This illustrates how each tumor may behave differently
from another.

Figure 3.8. Multiple concerted disruption (MCD) analysis and its application to triple
negative breast cancer. (a) Analysis of ten simulated datasets to determine the proportion of
random simulations at each observed frequency of MCD. Notably, 99.7% of random
simulations had a MCD frequency of 0/9, with the remaining 0.3% at 1/9. Moreover, no
simulations showed a frequency ≥ 2/9. Thus, the observation of an MCD event suggests the
event is likely non-random. (b) Using the knowledge database of Ingenuity Pathway Analysis,
upstream and downstream components of FGFR2 were selected to assess their role in the
subset of triple negative breast cancer (TNBC) cell lines. Only components which were shown
to have a direct or indirect expression level relationship were selected. Of the seven
components identified (four upstream and three downstream of FGFR2), one upstream
component (FGF2) and one downstream component (COL1A1) were present in both the MDA
list (Additional File 4) and MCD list (Additional File 7). FGF2, colored in green, is shown to be
frequently underexpressed while COL1A2, colored in red, is frequently overexpressed.
Interestingly, examining FGFR2 and COL1A1, while FGFR2 overexpression is not frequently
associated with DNA level alteration, COL1A1 is frequently affected at DNA level. Moreover, in
the five TNBC cell lines examined, four have DNA level alteration of COL1A1 and the remaining
line has DNA level alteration of FGFR2.
73

Figure 3.8
a
b
Proportion of random simulations
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0 1 2 3 4 5 6 7 8 9
MCD Frequency
FGFR2
Gene Expression
DNA Copy Number
DNA Methylation
Allelic Status
COL1A1
Gene Expression
DNA Copy Number
DNA Methylation
Allelic Status
FGF2
HCC38
* *
HCC1008
IGF2
HCC1143
TP63
HCC1599
HCC1937
74
FGFR2
COL1A1
*Sample has MCD
RUNX2
TGFB1
Legend:
GE: Gene Expression: Over Under
CN: DNA Copy Number: Gain Loss
L: Allelic Status: LOH
M: DNA Methylation: Hypo Hyper

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78

Chapter 4: Uniparental disomy is a prevalent genetic
mechanism of oncogene disruption in lung adenocarcinoma 3
3 A version of this chapter will be submitted for publication with the following author list: Chari
R, Lockwood WW, Soh J, Coe BP, Tam K, MacAulay CE, Minna JD, Lam S, Gazdar AF, Lam
WL. (2010) Uniparental disomy is a prevalent genetic mechanism of oncogene disruption in
lung adenocarcinoma.
79

4.1 Introduction
Genetic alterations play a significant role in a variety of malignancies [1, 2]. Typically, these
alterations have been represented by either changes in gene dosage (DNA copy number) or
somatic mutations such as total copy number gain or activating mutations of oncogenes and
total copy number loss or inactivating mutations of tumor suppressor genes. Loss of
heterozygosity is also a common alteration whereby one allele is lost and often, results in a loss
of total copy number. However, there are instances in which where one allele is lost but the
remaining allele is duplicated resulting in no net change in copy number, termed copy neutral
loss of heterozygosity or somatic uniparental disomy (UPD).
Although somatic UPD had been shown previously in malignancies such as retinoblastoma [3],
recent studies have shown an increased prominence of this alteration [4]. This largely been a
result of advances in technology to detect somatic UPD and advances in the methodologies to
define UPD [5, 6]. Moreover, frequent regions of somatic UPD have been identified in many
different cancer types such as colorectal cancer [7, 8], lymphoma [9, 10], myelodysplastic
syndrome (MDS) [11-13], basal cell carcinoma [14], hepatoblastoma [15], and ovarian cancer
[16]. In addition, while the target gene of some of these regions have been associated to tumor
suppressors such as RB1 and TP53, where the gene is likely mutated, the targets have also
been associated with oncogenes. For example, mutation with somatic UPD has been observed
at loci such as JAK2 [6, 17], CBL [12, 18], FLT3 [19] in hematological malignancies. However,
such associations have been limited in epithelial malignancies.
Recently, we have illustrated the concept of mutant allele specific imbalance (MASI) in lung
cancer [20]. It was found that a highly activated state for EGFR and KRAS is achieved through
either copy number amplification of the mutated allele for EGFR and UPD of the mutated allele
for KRAS. With the observed frequency of UPD at KRAS as such, we sought to assess the
impact and prevalence of UPD in the lung adenocarcinoma genome. Strikingly, we found that
the amount of the genome affected frequently by UPD was comparable to that of copy number
80

gain and loss. When examining major oncogenes and tumor suppressor genes, while most
oncogenes were associated with frequent areas of gain, we found a subset of both known and
novel oncogenes that were frequently affected by UPD. Finally, examining oncogenes with
homozygous mutation in multiple cancer types, we observe frequent UPD at these genes
suggesting this mechanism of oncogene activation is prevalent across multiple cancer types.
4.2 Methods
4.2.1 Genome wide profiling of clinical lung adenocarcinoma specimens
Forty-six lung adenocarcinoma cases were obtained from Vancouver General Hospital under
approved ethics. Cases were reviewed by a pathologist and tumors were microdissected to
ensure maximal tumor cell content (≥ 70%). Five hundred nanograms of genomic DNA were
extracted from each tumor and adjacent non-malignant tissue were prepared and hybridized to
the Affymetrix Genome-Wide Human SNP 6.0 array platform as per manufacturer's instructions.
CEL files, the raw data files generated, were then processed using the Affymetrix Genotyping
Console version 3.0.2 to generate .chp files using the birdseed v2 genotyping algorithm.
4.2.2 Determination of regions of uniparental disomy (UPD) in clinical lung tumors
CEL files and .chp files were imported into Partek Genomics Suite (PGS) using the software's
recommended default settings. First, to determine total copy number, paired copy number
intensities were calculated for each sample using the intensity in the tumor vs. it's matched non-
malignant sample. Paired copy number intensities were then analyzed using the Genomic
Segmentation method in PGS with all parameters run at default except for the number of
markers which was set to 50. Subsequently, allele specific copy number (ASCN) analysis was
used to determine regions of allelic imbalance. A region was deemed imbalanced if the
imbalance proportion was ≥ 0.15 (as recommended by PGS). Finally, a region was called UPD
if the region was imbalance and no change in total copy number was present.
81

4.2.3 Determining frequent regions of UPD, gain and loss
To determine frequent regions of gain, loss, and UPD, the frequency of each alteration was
determined for each SNP probe on the somatic chromosomes. A frequency threshold of 40%
was used. To smooth out regions of UPD (and for gain and loss as well), a three step process
was performed. First, adjacent probes with frequencies greater than the threshold were merged
together. Second, to account for dips in frequency where one region is split into two, if the dip is
less than 1 Mb in size, the regions were merged. Finally, smoothed regions of UPD, gain, or
loss which were less than 100 probes in size were removed.
4.2.4 Determination of UPD in cancer cell lines
Raw SNP 6.0 data (.CEL files) from cancer cell lines were obtained from the Wellcome Trust
Sanger CGP database. CEL files were then genotyped similarly as above to generate CHP
files. To define a total copy number and allele specific copy number reference, SNP 6.0 data,
generated from 72 CEPH HapMap samples, were obtained from Affymetrix and were also
genotyped. Unpaired copy number and allele specific copy number analyses were performed,
as described above, to determine regions of allelic imbalance without a change in total copy
number using Partek Genomics Suite.
4.2.5 Expression analysis of genes in focal regions of UPD
For 16 of the 46 tumor/non-malignant tissue pairs, gene expression profiles on a custom
Affymetrix chip were generated. The 32 samples were normalized using the RMA algorithm
[21] in the Bioconductor software suite in R [22]. To determine overexpression in a given
sample pair for a given gene, since expression values are in log2 space, expression values in
non-malignant samples were subtracted from expression values in the tumor. A two-fold
expression change was deemed significant for this analysis.
82

4.3 Results
4.3.1 Detection of UPD using allele specific copy number analysis
To determine regions of UPD, we first determined regions of allelic imbalance using an allele
specific copy number based approach. This approach has been shown to identify more regions
of imbalance than previous call-based approaches [6, 12]. In the first example, where no UPD
is present, we observe a chromosome exhibiting no change in total copy number as compared
to its matched control and also no imbalance between the alleles, represented by shift between
the blue and red data points (Figure 4.1a). However, in the next two samples, we do observe
large shifts between the blue and red data points. Specifically, one example illustrates a region
of UPD with a region of gain on chromosome arm 12q (Figure 4.1b) and another example
illustrates a whole chromosome UPD event on chromosome 14 (Figure 4.1c). The blue data
points in the UPD regions are not completely at zero but slightly above due to cells that do not
carry the UPD alteration.
4.3.2 UPD is prevalent and non-random in the lung cancer genome with comparable
frequencies to gain and loss
With the ability to detect UPD as shown above as well as identifying UPD at the KRAS
oncogene from a previous study, we then assessed the prevalence of UPD in the genome.
Using a 40% frequency threshold, we determine the regions of the genome affected by UPD at
this frequency. In total, 153 regions were identified (Table 4.1). Moreover, when examining
areas of frequent gain and loss (at similar frequency thresholds), we observe that the amount of
the genome affected by frequent UPD is comparable to that of frequent gain and loss (Figure
4.2). While there was some overlap with the regions of loss and UPD, there was very little
overlap between gain and UPD, even though we would expect some level of overlap by random
chance. Using megabases of the genome as a metric, we observe 650 Mb affected by gain,
500 Mb by loss and 400 Mb by UPD, with 7 Mb overlap in gain and UPD and 58 Mb overlap
83

etween loss and UPD (Figure 4.3). Strikingly, all three alterations cover over 49% of the
genome. It should also be noted that the observation of comparable levels of gain, loss and
UPD at the frequency level is also seen when examining samples on an individual basis (Figure
4.4).
4.3.3 Overlap of major oncogenes and tumor suppressor genes in regions of gain, loss,
and UPD
We then assessed how major oncogenes and tumor suppressor genes associated with the
three levels of genetic alteration. Using a list of 112 genes derived from a number of sources
[23, 24] (Table 4.2), we found 52 of these genes to overlap with at least one of frequent gain,
loss, or UPD. Major oncogenes such as EGFR, MYC, AKT1, MDM2, and ERBB2 are affected
frequently by copy number gain, which has been shown previously [25-29] (Table 4.3).
Similarly, major tumor suppressor genes such as FHIT, RARB and CDKN2A are affected by
frequent copy number loss (Table 4.4). Interestingly, while expected tumor suppressor genes
such as BRCA2 and RB1 are affected by UPD, a subset of seven oncogenes were affected by
UPD. Specifically, UPD was observed at KRAS, as shown previously, PIK3CA, BCL6 and
FLT3. Moreover, examining KRAS (Figure 4.5a) and RB1 (Figure 4.5b) specifically, we see
that the UPD events are of different sizes between different samples.
4.3.4 UPD is prevalent at oncogenes across multiple cancer types
We observed frequent UPD at oncogenes in lung cancer. We sought to assess the prevalence
of UPD at oncogenes across multiple cancer types. For this analysis, we utilized SNP 6.0 array
data for over 700 cancer cell lines from the Wellcome Trust Sanger database where somatic
mutation data were also available. In total, 67 instances of homozygous mutation at 13
oncogene loci were assessed (Table 4.5). It was found that while copy number gain was the
most prevalent genetic alteration, a significant proportion of samples exhibited UPD (Figure
4.6a, Table 4.6). Examining the genes with the most samples harbouring homozygous
84

mutation, KRAS and BRAF, the overall trend is consistent with what is observed at these two
loci (Figure 4.6a). An example of UPD at KRAS in NCI-H2030 and BRAF in A427 are
illustrated in Figure 4.6b.
For this analysis, cancer cell lines were utilized as the samples represent a more homogeneous
population of cells. In contrast, clinical tumors, even after microdissection, still may contain
small amounts of contaminating normal cells. As such, determining if a mutation is
homozygous in clinical lung tumors is challenging. With available KRAS mutation data, we
assessed the frequency of gain, loss and UPD in KRAS mutant tumors and observe a similar
distribution pattern observed in the cell lines (Figure 4.6c)
4.3.5 Identification of novel candidate oncogenes using focal regions of UPD
Selecting the more focal regions of UPD within the set of 153 regions, we identified 35 of the
regions which contained three or less RefSeq annotated genes. In total, 64 RefSeq genes were
identified across all 35 regions (Table 4.7) and amongst these genes was E2F3 (Figure 4.7a).
Examining paired gene expression for a subset of the 46 tumor/normal pairs, it was found that
10/16 pairs showed overexpression of E2F3 (Figure 4.7b). E2F3 has previously shown to be
overexpressed in lung cancer and also shown to have a role in other cancer types [30, 31].
4.4 Discussion
We have shown the unexpected and wide prevalence of UPD in the lung adenocarcinoma
genome and have also observed a large number of both known and novel oncogenes harbored
in these regions of frequent UPD. While there have been previous studies utilizing SNP arrays
on lung adenocarcinoma tumors [26, 30], there are likely a number of reasons why these
frequent regions were likely missed. First, the tumors utilized in this study were microdissected
to ensure a high proportion of tumor cells (≥ 70% was required) were analyzed. This is
important as previous studies have shown the impact of tissue heterogeneity and the ability to
detect alterations [32, 33]. Secondly, for every tumor used, matched non-malignant tissue was
85

obtained, profiled and used as the control. While it has been shown that unmatched references
can be used to detect UPD, the resultant UPD may not be called correctly all the time. Finally,
the progression from call-based approaches to allele specific copy number-based approaches
can also increase the detection of UPD [6, 12]. Taken together, these improvements could
explain the observed results.
While it is interesting to observe these frequent regions of UPD in the lung adenocarcinoma
genome, the larger implications of these findings may not be readily apparent. In the cases of
somatically mutated oncogenes or tumor suppressor genes, the existence of UPD in these
cases is clear as UPD is used to select the mutated allele to result in a homozygous mutation
state. We have previously shown that mutant allelic specific imbalance (MASI), either through
allele specific amplification or UPD, is associated with a poorer prognosis [20]. To assess the
prevalence of UPD at homozygously mutated oncogene sites, we analyzed cancer cell lines
encompassing multiple cancer types for UPD at mutated oncogenes. While the most frequent
genomic alteration observed is copy number gain, frequent UPD also occurs. The distribution
of alterations observed across all genes is consistent with the most frequently mutated
oncogenes, KRAS and BRAF. The result of these UPD events is preferential expression of the
mutated allele.
It should also be noted that with the amount of frequent UPD detected, there are regions likely
selected for reasons other than somatic mutation. For example, like in the cases of imprinted
regions, there could be preferential selection of an unmethylated or methylated allele which in
turn, could regulate downstream gene expression. Previous studies have assessed the
relationship between regions of UPD and DNA methylation patterns in cancer [8, 34, 35].
Alternatively, in order to achieve downstream differential expression, in addition to preferential
selection based on methylation, it has also been shown that for a given gene, transcription may
involve only one of the alleles [36-39] and thus, selection may be based on transcriptional
efficiency. Hence, it is important that the genetic data on UPD be integrated with methylation
86

and gene expression data to refine these regions of UPD with many genes to a small number of
candidate oncogenes and tumor suppressor genes.
Though many of the regions of UPD identified were large and encompassed a number of
genes, approximately 1/5 of the regions identified contained three or less genes. As such, this is
one approach for narrowing down candidate gene targets. Using gene expression data on a
subset of the profiled cases used for UPD, we assessed the gene expression profiles of the 64
genes encompassed in the 35 focal regions. Of the 64 genes, 57 were represented on the
gene expression microarray platform used. Fifteen of the 57 genes were overexpressed in at
least 25% of the samples (4/16) (Table 4.8). In addition to E2F3, other genes within the set of
15 genes have shown interesting biological function. For example, GPR39 has been shown to
activate EGFR signaling as well as protect cells form apoptosis [40, 41]; SLC7A11 has been
shown to have a role in drug resistance [42] and was assessed as a therapeutic target for small
cell lung cancer [43]; PDGFD has been implicated in many different cancer types [44]; and
PRDM8, a histone methyltransferase, is a member of the PRDM transcription factor family and
these factors have been implicated as proto-oncogenes [45].
4.5 Conclusion
In summary, we have shown an unexpectedly high prevalence of UPD in the lung
adenocarcinoma genome, with comparable amounts of the genome affected being comparable
to copy number gain and loss. While a number of known oncogenes were shown to be in
regions of frequent UPD, potentially novel lung oncogenes have also been shown to be affected
by UPD with downstream consequential change in gene expression. Further studies are needed
to elicit their roles in lung adenocarcinoma.
87

Figure 4.1. Detection of UPD using allele specific copy number. Total copy number (top)
and allelic specific copy number (bottom) plots. In the allele specific copy number plot, the red
data points represent the level of the major allele and the blue data points represent the level of
the minor allele. The total copy number plot represents a the sum of the allele specific copy
number. (a) Sample with neutral copy number and no imbalance of chromosome 12. While the
total copy number is neutral, when examining the allele specific copy number, imbalance
between the alleles is evident. (b) Sample with regions of copy number gains and UPD (in
orange) on chromosome 12q. (c) Sample with whole chromosome UPD of chromosome 14.
88

Figure 4.1
a
b
c
# of copies
# of copies
4
2
0
3
2
1
0
# of copies
# of copies
# of copies
# of copies
4
2
0
3
2
1
0
4
2
0
3
2
1
0
Chromosome 12
Chromosome 12q
Chromosome 14
89
Total copy number
Allele speci�c
copy number
Total copy number
Allele speci�c
copy number
Total copy number
Allele speci�c
copy number

Figure 4.2. Comparison of frequent regions of gain, loss and UPD in the lung
adenocarcinoma genome. Frequent regions of gain (red), loss (green) and UPD (blue) in the
lung adenocarcinoma genome. Only regions which were altered in at least 40% of the samples,
by either gain, loss, or UPD, are shown. Frequent regions of gain (such as 5p, 7p, 8q, 17q and
20q) and loss (such as 3p, 8p, 9p, 13q), which have previously been shown, are detected. The
fourth column, composite ("C"), represents areas of overlap between gain and UPD (red) and
loss and UPD (green).
90

Figure 4.2
1 G L U C 2 G L U C 3 G L U C 4 G L U C
5 G L U C 6 G L U C 7 G L U C 8 G L U C
9 G L U C 10 G L U C 11 G L U C 12 G L U C
13 G L U C 14 G L U C 15 G L U C 16 G L U C
17 G L U C 18 G L U C 19 G L U C 20 G L U C
21 G L U C 22 G L U C
91
G - Gain
L - Loss
U - UPD
C - Composite

Figure 4.3
Gain Loss
642 441
7
Figure 4.3. Venn diagram illustrating the amount of the genome covered by frequent
gain, loss, and UPD. Numbers provided are in megabases (Mb) of genome sequence.
92
335
UPD
58

Figure 4.4. Genomic profile of an individual lung adenocarcinoma sample. Regions of
gain (red), loss (green), and UPD (blue) are shown in this single lung adenocarcinoma profile.
Comparable amounts of the genome are affected by all three of these alterations.
93

94
Figure 4.4
1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
17 18 19 20 21 22 Gain
Loss
UPD

95
Figure 4.5
a
chr12 (p12.1-p11.21) 12q12 15 22
b
85060201
85070205
85070159
85060358
85050147
85060186
85050235
85070021
85070093
85060276
85050241
85040031
85060354
85060256
85050172
85040001
85050140
85060342
85060206
85060311
85060251
85050207
85060098
85070205
85060186
85060098
85060342
85060251
85050177
85070081
85050011
85060358
85060256
85050147
85040001
85060221
85070085
85060216
85060068
85050172
85070061
85060311
85070093
85040031
KRAS
KRAS UPD Regions
chr13 (q13.3-q21.1) 13 p12 11.2 21.1 q31.1 q34
RB1 UPD Regions
Figure 4.5. Examination of UPD events at the KRAS and RB1 loci. KRAS shown in (a) and RB1 shown in (b). The
region of UPD encompassing these loci varies in size between samples, with some samples illustrating larger sizes of UPD
than others. The existence of these different size events are likely a result of a different underlying mechanism of UPD.
RB1

Figure 4.6
a b
c
Percent of cases
All Genes (n=67)
KRAS (n=33)
BRAF (n=11)
Gain
Loss
UPD
Neutral
50 KRAS (n=21)
40
30
20
10
0
Gain UPD Loss Neutral
# of copies # of alleles
# of copies # of alleles
3
2
1
0
4
2
0
3
2
1
0
4
2
0
A427
Chromosome 7 BRAF
NCI-H2030
KRAS Chromosome 12
Figure 4.6. Relationship of homozygous mutation at oncogenes and genomic alteration.
Using the Wellcome Trust Sanger COSMIC database for somatic mutation data and
SNP 6.0 data available for over 700 cancer cell lines from their database, prevalence of
UPD was assessed in this dataset. Specifically, only those cell lines with oncogenes and
homozygous mutation were analyzed. (a) In total, 67 instances of homozygous mutation at
an oncogene loci were identified. While a large fraction of cases exhibited copy number
increase (51%), the second most prominent alteration is UPD (34%). Of the 12 different
genes assessed, KRAS and BRAF are the most frequently homozygously mutated oncogenes
and those two genes show similar frequency distribution patterns of genomic alteration
to the whole set. (b) An example of UPD at BRAF in A427 and KRAS in NCI-H2030
where both BRAF and KRAS are homozygously mutated. (c) With available mutation data
on KRAS from the 46 lung tumor/matched non-malignant tissue pairs, similar analysis was
performed and it was found that the patterns of genomic alteration were similar to what was
observed in cancer cell lines.
96
Allele Speci�c
Copy Number
Total Copy Number
Allele Speci�c
Copy Number
Total Copy Number

Figure 4.7. Identification of E2F3 in a focal region of UPD. (a) One of the focal regions
identified was located on chromosomal region 6p22.3. There were only three RefSeq
annotated genes that were completely encompassed within this region: E2F3, ID4, and
MBOAT1. The UCSC Genome Browser (genome build hg18) was used to identify genes and
visualize region [46]. (b) Analyzing gene expression amongst a subset of the tumors profiled on
the SNP array, it was found that E2F3 was the most frequently overexpressed amongst the
three genes assessed, with a frequency of overexpression of 62.5%.
97

Figure 4.7
a
b
6p25.3
Log2 fold change
6p25.1
2.5
2.0
1.5
1.0
0.5
0
-0.5
6p24.3
6p24.1
6p23
6p22.3
6p22.2
6p22.1
Samples 1 to 16
98
6p21.33
6p21.31
6p21.2
6p21.1
6p12.3
6p12.2
6p12.1
6p11.2
6p11.1

Table 4.1. Regions of the genome exhibiting frequent UPD
Chr BPStart BPEnd # of Chr BPStart BPEnd # of
markers
markers
1 57240523 57708781 261 6 8651004 9629747 293
1 88627690 88937185 107 6 18605671 20757531 903
1 213006484 213325225 153 6 71651506 72037387 152
2 33915294 34575063 336 6 73271789 76714973 996
2 102028604 102246459 130 6 82824129 84992961 566
2 103342214 104703078 369 6 87778426 91268388 1147
2 107288361 107909534 151 6 97800818 101044085 940
2 113475801 113597836 110 6 105272060 114467061 2939
2 123104889 123771933 262 6 116466834 119757979 966
2 129285355 130086326 353 6 121042746 123214382 558
2 132852444 133093232 131 6 125234328 126352419 406
2 134976541 137102451 519 6 130012107 130399246 197
2 138356995 142103903 1239 6 131442876 133188489 644
2 148597921 149606831 175 6 134265257 144908460 3331
2 150732270 153458836 918 6 147524934 170759956 9496
2 154569594 157096948 576 7 109870908 110924988 272
2 158283600 163854187 1560 7 119981096 121751585 455
2 165114877 166441984 353 7 122940488 123963613 280
2 167549158 172376141 1670 7 125810873 126573092 258
2 173385819 175183422 649 8 82600012 84910522 562
2 178506144 178971533 144 8 87475660 91661039 1094
2 182593441 183843529 379 8 109473113 113912000 1026
2 185976166 192057947 1482 9 32370194 32717136 127
2 195998062 198251401 604 10 86487543 87543389 442
2 214938398 217672315 1092 10 97401670 97992222 155
2 222098215 223638933 514 10 99819620 100845692 389
2 224915228 225683208 295 11 7088070 8626013 675
2 234086493 235425331 673 11 9745400 16571856 2963
3 38947562 40467146 489 11 17785506 20708448 1441
3 75597086 77391013 520 11 22527476 27641093 1997
3 120734475 121565472 248 11 31290899 36427391 2111
3 126442524 128090913 466 11 37624038 46083889 3066
3 131310768 131908449 194 11 78488179 78811363 210
3 133156739 140816203 2231 11 81391754 83571743 737
3 141841646 145572883 1095 11 85133890 86695809 613
3 148635117 153815363 1592 11 92858003 94403193 618
3 154850963 162426559 2109 11 99344667 102033351 1020
3 163593089 164340331 178 11 103294671 104342367 382
3 171033453 175715353 1551 11 106728968 107654049 275
3 179650656 194058084 4585 11 111090113 111676300 118
99

Chr BPStart BPEnd # of Chr BPStart BPEnd # of
markers
markers
4 56867092 57264514 111 11 114516077 115632014 460
4 59586524 62756073 878 11 121222174 122326244 431
4 68014200 73981908 1418 11 127365170 128934004 622
4 75048874 79252113 1473 11 131240209 132411274 555
4 81069345 81632642 125 12 4901875 5859088 616
4 83519602 86772381 1036 12 7251496 15972576 2966
4 95266916 96342489 299 12 19068978 20077939 414
4 99713479 100262528 179 12 21583225 28079788 2489
4 102215835 109269168 1774 12 29252288 31395946 1005
4 110431176 111696150 384 12 36144018 38218718 356
4 113392931 114074009 165 12 43846459 44709526 206
4 119368629 120609480 324 12 46714417 47193357 116
4 122057241 123147706 383 12 49541041 50471244 235
4 128685983 130719464 455 12 53439918 53858695 150
4 138792901 139993006 454 12 74639231 75745944 244
5 52835375 56296363 1274 12 92376456 96256397 1605
5 59963217 62083765 600 12 97428291 100976394 1075
5 64017546 65834248 562 12 102585961 120121516 6328
5 70702961 72963048 707 12 124477764 127353782 1525
5 74029627 81781885 2453 12 128730700 129400678 383
5 86329552 88138619 328 13 17943628 32731810 6266
5 90373713 90752434 102 13 35198318 36512766 543
5 95109222 96467147 461 13 39604817 41933751 808
5 98108008 98876920 190 13 43687180 52149041 2627
5 106982444 108758672 584 13 80666670 81857944 333
5 113412673 114060668 291 13 97080915 99917507 1076
5 115177955 116152174 521 14 26463057 27074470 206
5 118498618 118830120 101 14 39744666 40655458 316
5 123995175 124348209 112 15 23337531 24061503 396
5 130208827 131907815 390 17 1646832 4192054 695
5 139359170 140952015 262 17 5443385 6771106 606
5 145140525 146950721 652 17 8395366 9333459 369
5 148164516 149400891 515 17 10552154 14949757 1968
5 153600774 154479059 292 20 8028477 9014932 524
5 156203870 159492184 1245 20 53205763 53952115 387
5 162483304 163524481 416
5 165711472 166223866 189
5 180068185 180629495 176
100

Table 4.2. List of major oncogenes and tumor suppressor genes assessed
Gene Chr Gene Chr Gene Chr
ABL1 9 EVI1 3 NF2 22
ABL2 1 FBXW7 4 NKX2-1 14
AKT1 14 FEV 2 NOTCH1 9
AKT2 19 FGFR1 8 NRAS 1
ALK 2 FGFR2 10 NTRK1 1
APC 5 FGFR3 4 NTRK3 15
ATM 11 FH 1 PDGFB 22
BCL2 18 FHIT 3 PDGFRA 4
BCL3 19 FLT3 13 PDGFRB 5
BCL6 3 FOXO1A 13 PHOX2B 4
BMPR1A 10 FOXO3A 6 PIK3CA 3
BRAF 7 FOXP1 3 PIK3R1 5
BRCA1 17 GNAS 20 PIM1 6
BRCA2 13 GSTP1 11 PRKAR1A 17
BUB1B 15 HRAS 11 PTCH 9
CAV1 7 HRPT2 1 PTEN 10
CBL 11 ITK 5 PTPN11 12
CCND1 11 JAK2 9 RARB 3
CCND2 12 JAK3 19 RASSF1A 3
CCND3 6 KIT 4 RB1 13
CD44 11 KRAS 12 REL 2
CDH1 16 LCK 1 RET 10
CDH11 16 MAF 16 RUNX1 21
CDH13 16 MAFB 20 SEMA3B 3
CDK4 12 MAML2 11 SMO 7
CDK6 7 MAP2K4 17 STK11 19
CDKN2A 9 MDM2 12 SUFU 10
CEBPA 11 MEN1 11 SYK 9
CHEK2 22 MET 7 TCF1 12
CRK 17 MLH1 3 TIMP3 22
CTNNB1 3 MLL 11 TP53 17
CYLD 16 MPL 1 TSC1 9
DAPK1 9 MSH2 2 TSC2 16
EGFR 7 MSH6 2 TSHR 14
ERBB2 17 MYC 8 VHL 3
ERCC2 19 MYCL1 1 WT1 11
ERG 21 MYCN 2
ETV6 12 NF1 17
101

Table 4.3. Overlap of oncogenes in frequent regions of genomic alteration
Gene
Symbol
Location Gain Loss UPD
ABL1 9q34.1 X
ABL2 1q24-q25 X
AKT1 14q32.32 X
AKT2 19q13.1q13.2
X
BCL6 3q27 X
CCND1 11q13 X
CCND3 6p21 X
CD44 11p13 X
CDK4 12q14 X
CEBPA 19q13.11 X
CRK 17p13.3 X
EGFR 7p12.3-p12.1 X
ERBB2 17q21.1 X
ETV6 12p13 X
FEV 2q36 X
FGFR3 4p16.3 X
FLT3 13q12 X
GNAS 20q13.2 X
HRAS 11p15.5 X
ITK 5q31-q32 X
KRAS 12p12.1 X
LCK 1p35-p34.3 X
MAFB 20q11.2q13.1
X
MDM2 12q15 X
MEN1 11q13 X
MPL 1p34 X
MYC 8q24.12q24.13
X
MYCL1 1p34.3 X
NOTCH1 9q34.3 X
NTRK1 1q21-q22 X
PDGFB 22q12.3q13.1
X
PDGFRB 5q31-q32 X
PIK3CA 3q26.3 X
PIM1 6p21.2 X
PRKAR1A 17q23-q24 X
SMO 7q31-q32 X
102

Table 4.4. Overlap of tumor suppressor genes in frequent regions of genomic alteration
Gene
Symbol
Location Gain Loss UPD
BRCA1 17q21 X
BRCA2 13q12 X X
CDH1 16q22.1 X
CDKN2A 9p21 X
CYLD 16q12-q13 X
FH 1q42.1 X
FHIT 3p14.2 X
GSTP1 X
MAP2K4 17p11.2 X X
NF1 17q12 X
PTPN11 12q24.1 X
RARB 3p24.2 X
RB1 13q14 X X
TSC1 9q34 X
TSC2 16p13.3 X
WT1 11p13 X
103

Table 4.5. Cell lines and oncogene loci with homozygous mutation
Sample Primary Tissue Gene Sample Primary Tissue Gene
EFM-19 breast PIK3CA NCI-H460 lung KRAS
NCI-ADR-RES breast ERBB2 NCI-H727 lung KRAS
OCUB-M breast PIK3CA PC-14 lung EGFR
AM-38 central nervous system BRAF SHP-77 lung KRAS
OMC-1 cervix PIK3CA SW1573 lung KRAS
HEC-1 endometrium KRAS KYSE-450 oesophagus NOTCH1
ECC4 gastrointestinal tract KRAS OVCAR-5 ovary KRAS
BE-13 haematopoietic and
lymphoid tissue
NOTCH1 AsPC-1 pancreas KRAS
HEL haematopoietic and
lymphoid tissue
JAK2 CAPAN-1 pancreas KRAS
OPM-2 haematopoietic and
lymphoid tissue
FGFR3 HuP-T4 pancreas KRAS
LS-174T large intestine CTNNB1 MIA-PaCa-2 pancreas KRAS
LS-411N large intestine BRAF PANC-08-13 pancreas KRAS
RCM-1 large intestine KRAS SW1990 pancreas KRAS
SK-CO-1 large intestine KRAS YAPC pancreas KRAS
SNU-C2B large intestine KRAS A375 skin BRAF
SW1463 large intestine KRAS COLO-679 skin BRAF
SW403 large intestine KRAS CP66-MEL skin NRAS
SW620 large intestine KRAS GAK skin NRAS
A427 lung CTNNB2 HT-144 skin BRAF
A549 lung KRAS MEL-HO skin BRAF
COLO-668 lung KRAS MEL-JUSO skin HRAS
COR-L23 lung KRAS SH-4 skin BRAF
COR-L23 lung RUNX1 SK-MEL-2 skin NRAS
IA-LM lung KRAS SK-MEL-28 skin BRAF
LCLC-97TM1 lung KRAS SK-MEL-28 skin EGFR
LU-65 lung KRAS UACC-62 skin BRAF
NCI-H1092 lung CTNNB3 RD soft tissue NRAS
NCI-H1155 lung KRAS BCPAP thyroid BRAF
NCI-H1395 lung BRAF CAL-62 thyroid KRAS
NCI-H1793 lung KRAS BB49-HNC upper
aerodigestive
tract
HRAS
NCI-H2030 lung KRAS 639-V urinary tract PIK3CA
NCI-H2122 lung KRAS T-24 urinary tract HRAS
NCI-H2291 lung KRAS UM-UC-3 urinary tract KRAS
NCI-H2347 lung NRAS
104

Table 4.6. Summary of homozygous mutation analysis in cancer cell lines
Gene # of Hz mutations # UPD # Gain # Loss # Neutral
KRAS 33 10 18 4 1
BRAF 11 4 6 1 0
NRAS 5 1 3 1 0
PIK3CA 4 1 2 1 0
CTNNB1 3 2 0 1 0
HRAS 3 2 1 0 0
EGFR 2 1 1 0 0
NOTCH1 2 0 1 1 0
FGFR3 1 0 1 0 0
JAK2 1 0 1 0 0
ERBB2 1 1 0 0 0
RUNX1 1 1 0 0 0
Total 67 23 34 9 1
105

Table 4.7. RefSeq genes in focal regions of UPD
Gene Symbol Chr Gene Symbol Chr
DAB1 1 TNFAIP8 5
IL1R1 2 ZNF608 5
IL1RL2 2 E2F3 6
GPR39 2 ID4 6
EPC2 2 MBOAT1 6
KIF5C 2 B3GAT2 6
MBD5 2 C6orf191 6
OSBPL6 2 IMMP2L 7
RBM45 2 LRRN3 7
CUL3 2 GRM8 7
DOCK10 2 ODZ4 11
FAM124B 2 CASP4 11
FRG2C 3 DDI1 11
ZNF717 3 PDGFD 11
COL29A1 3 CADM1 11
COL6A6 3 HNT 11
LPHN3 4 OPCML 11
ANTXR2 4 ANO2 12
FGF5 4 KCNA5 12
PRDM8 4 NTF3 12
ADH5 4 AEBP2 12
EIF4E 4 PLEKHA5 12
METAP1 4 ALG10B 12
SLC7A11 4 CPNE8 12
ARRDC3 5 KIF21A 12
CHD1 5 TMEM132B 12
RGMB 5 FZD10 12
FBXL17 5 ZNF10 12
FER 5 ZNF140 12
PJA2 5 ZNF268 12
KCNN2 5 ATP10A 15
DMXL1 5 PLCB1 20
106

Table 4.8. Genes overexpressed in focal regions of UPD
Probe ID Gene Symbol
107
Frequency of
Overexpression
merck-NM_001508_a_at GPR39 13
merck-AJ270693_at PLEKHA5 12
merck-NM_014331_at SLC7A11 10
merck-NM_001949_at E2F3 10
merck-C17174_at CUL3 9
merck-AA651853_at ARRDC3 7
merck-AF336376_a_at PDGFD 6
merck-CR624190_a_at KIF21A 6
merck-NM_016522_at HNT 5
merck-NM_003854_at IL1RL2 5
merck-NM_000845_at GRM8 5
merck-AY358331_s_at HNT 5
merck-CR625009_at ZNF140 5
merck-X52332_a_at ZNF10 5
merck-AK127693_s_at PLCB1 4
merck-NM_020226_at PRDM8 4

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Chapter 5: Integrating the multiple dimensions of genomic and
epigenomic landscapes of cancer 4
4 Sections 5.1 to 5.3, 5.4.1 to 5.4.4, 5.5 and 5.6 of this chapter has been published. Chari R,
Thu KL, Wilson IM, Lockwood WW, Lonergan KM, Coe BP, Malloff CA, Gazdar AF, Lam S,
Garnis C, MacAulay CE, Alvarez CE, Lam WL. (2010) Integrating the multiple dimensions of
genomic and epigenomic landscapes of cancer. Cancer and Metastasis Reviews, 29(1):73-93.
doi: 10.1007/s10555-010-9199-2. Sections 5.4.5 to 5.4.6 were not published previously.
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5.1 Introduction
In the past decade, advancements in genome profiling technologies have greatly improved our
ability to understand the landscape of cancer genomes. From the emergence of array based
comparative genomic hybridization (CGH) and spectral karyotyping (SKY) to the current state of
next generation sequencing (NGS), the improvement in resolution at which the genome can be
described has been over a million fold [1-6]. Likewise, the recent development of integrative
platforms to relate multiple dimensions of DNA features (such as copy number, allelic status,
sequence mutations, and DNA methylation) to gene expression pattern, has dramatically
improved our ability to identify causal genetic events and decipher their downstream
consequences in the context of gene networks and biological functions [7, 8] (Table 5.1).
Landmark events in cancer genomics, from the launch of Cancer Genome Anatomy Project at
the beginning of the decade to the recent publications of complete cancer genome sequences,
are highlighted in Figure 5.1 [3-6, 8-43].
Multiple levels of genetic and epigenetic disruption are instrumental to cancer development,
whereby specific genes may be altered by a variety of mechanisms. For example, the tumor
suppressor CDKN2A can be inactivated through copy number loss, DNA hypermethylation, or
sequence mutation. These mechanisms of disruption can occur in a tumor-specific manner or,
may occur concurrently in the same tumor, i.e. a two hit scenario. Moreover, in the former
situation, if a given gene or pathway's frequency of alteration is low when examined by one
mechanism or dimension, it is likely the gene/pathway would be overlooked by the analysis.
However, when multiple dimensions of disruption are considered in the analyses, alteration of
the gene in question may be detected at a high frequency, albeit at low frequencies by any one
mechanism. This illustrates the need for and the benefit of integrative analytical approaches. In
this article, we discuss the impact of multi-dimensional genomic analyses on our view of the
cancer genome landscape, and the contribution of such new knowledge to our understanding of
cancer progression and metastasis.
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5.2 Genomic alterations
5.2.1 Chromosomal aberrations
Chromosomal aberrations and rearrangements, such as translocations and gains/losses of
whole or portions of chromosome arms are detected through direct examination using molecular
cytogenetic techniques such as G-banding, SKY, fluorescence in situ hybridization (FISH) and
CGH [2, 44-48]. The manifestation of such alterations are generally attributed to mitotic errors,
where centrosomal aberrations and telomere dysfunction play key causative roles [49-53].
Aberrations such as gains and losses have been further refined using technologies such as
microarray CGH (see below). While primarily associated with different types of leukemia and
lymphomas, recent genomic studies have identified translocations in epithelial tumors such as
prostate and lung cancer [54-61]. A compilation of cumulative cytogenetic data from three main
sources - NCI/NCBI SKY/M-FISH & CGH Database, NCI Mitelman Database of Chromosome
Aberrations in Cancer, and NCI Recurrent Aberrations in Cancer – are now integrated into
NCBI's Entrez system as Cancer Chromosomes [62] (Table 5.2).
5.2.2 Gene dosage, allelic imbalance, mutational status
Gene dosage. Genomic DNA copy number alterations are a prominent mechanism of gene
disruption that contributes to tumor development [63]. Segmental amplification may lead to an
increase in gene and protein expression of oncogenes, while deletions may lead to
haploinsufficiency or the loss of expression of tumor suppressor genes. Since its development
in the mid 1990s, advances in microarray-based CGH technology have dramatically increased
genome coverage and target density, improving both the resolution and sensitivity of detection
of copy number alterations [64, 65]. The first genome-wide array CGH analysis utilized cDNA
microarrays originally designed for gene expression profiling [66]. Since these first
experiments, whole genome tiling path arrays with tens of thousands of bacterial artificial
chromosome (BAC) clones, oligonucleotide (25-80 bp nucleotide probes) and single nucleotide
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polymorphism (SNP) arrays with over one million DNA elements and the essential
bioinformatics tools for visualization and analysis of high density array CGH data have been
developed (Figure 5.1) [7, 33, 67-71]. These innovations have enabled increasingly precise
mapping of the boundaries and magnitude of genetic alterations throughout the genome in a
single experiment, greatly increasing our understanding of the cancer genome landscape in the
context of DNA copy number [33, 72-76]. While early attempts have been made utilizing
sequence-based approaches [77-80], recent studies have begun to illustrate the improvement in
detection resolution through the advances in high throughput sequencing technologies [6, 11,
13, 14]. The popularity of genome sequencing will depend on further cost reduction in data
generation and major advancements in analysis [81].
Copy number variation. The discovery of a vast abundance of germ line segmental DNA copy
number variation (CNV) in the normal human population has not only provided a baseline for
interpretation of cancer genome data, but also highlighted the need for comparison against
paired normal tissue [18, 19, 31, 32, 82-89]. Moreover, it has been shown that many of the
reported CNVs overlap with loci involved with sensory perception and more importantly, disease
susceptibility. While the role of CNV in cancer is not well understood, a recent study showed
that these regions are more susceptible to genomic rearrangement and may initiate subsequent
alterations during tumorigenesis [90]. Moreover, CNV at 1q21.1 was recently shown to be
associated with neuroblastoma and implicated NBPF23, a new member of the Neuroblastoma
Breakpoint Family, in tumorigenesis [91]. A database of all known CNVs is available at
http://projects.tcag.ca/variation [31]. In addition, as copy number profiles of cancer genomes
accumulate, hotspots for amplification and deletion are becoming evident, and signature
alterations associated with specific diseases and cancer histologic subtypes are emerging [92-
96]. The manifestation of “oncogene addiction” through lineage specific DNA amplification is a
case in point [38, 39, 97-100].
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Allelic status. Single nucleotide polymorphism (SNP) arrays are best known for their application
in genome wide association studies (GWAS), where the correlation of haplotype with phenotype
implicates disease susceptibility [101, 102]. SNP array platforms have shown tremendous
advances in resolution, with the number of SNPs that can be simultaneously measured
increased by 1000-fold since initial development. Currently, for example, the Affymetrix SNP
6.0 array platform measures 1.8 million elements representing 906,600 SNP elements and >
946,000 CNV elements. Likewise, on the Illumina HumanOmni1 platform, over 1,000,000 sites
(representing a mixture of SNP and CNV elements) can be simultaneously assessed. In
addition to their application in GWAS, SNP arrays can also be used to detect somatic
alterations and when applied in this context, can allow for the simultaneous detection of copy
number alteration and allele imbalance in tumor genomes. In the example in Figure 5.2, when
the SNP array profile of a lung cancer genome is compared against that of its paired non-
cancerous lung tissue, it is not only possible to distinguish regions of allelic balanced copy
neutrality (Figure 5.2a) from allelic imbalance (Figure 5.2b, 5.2c), but also regions of allelic
imbalance due to segmental DNA copy number alteration (Figure 5.2b) from those without
change in total copy number (Figure 5.2c).
Mutational profiling and whole genome sequencing. In cancer, oncogenes are thought to
harbor mutations which lead to increased protein expression or constitutive protein activation
while tumor suppressor genes are thought to harbor mutations which are inactivating, either
through total loss of protein expression or expression of mutant, non-functional protein. In
addition, activating and inactivating mutations can also be accompanied by changes in gene
dosage or allele status (see below). Traditionally, mutation screening has been focused on
specific oncogene and tumor suppressor loci. With the availability of newer and cheaper
sequencing technologies [103], recent studies have expanded from single gene analyses to
genome-wide screens [6, 11, 13, 14, 104]. For example, in studies using small cell lung cancer
and melanoma cell lines, tens of thousands of somatic mutations were identified in each cell
line, with a proportion of these mutations being attributed to cigarette smoke (G to T
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substitutions) and UV exposure (C to T), respectively [4, 5]. It will be interesting to see if other
cancers have such mutation signatures. Another observation made in both studies was that the
uneven distribution of mutations suggests that DNA sequence integrity is largely maintained by
transcription-associated DNA repair. While these and future studies will uncover a vast number
of mutations, the contribution of those mutations to tumorigenesis will need to be determined
[105, 106].
5.2.3 Genomic landscape: Gains, losses and uniparental disomy
Individually, the study of genomic dimensions has yielded a global description of cancer
genomes in terms of gene dosage, allelic status and somatic mutation. Collectively, however,
the integration of these three dimensions has brought two concepts to the forefront: allele
specific copy number alterations and uniparental disomy (UPD) (Figure 5.2). Typically, the
relationship between somatic mutation and allele specific copy number alterations have been
associated with tumor suppressor genes (e.g. RB1 and TP53) whereby mutation is combined
with loss to achieve bi-allelic inactivation [107, 108]. However, recent studies have shown
preferential amplification of alleles encoding mutated oncogenes as well [109-114]. In non-
small cell lung cancer, mutant allele specific imbalance (MASI) is frequently present in mutant
EGFR and KRAS tumor cells, and is associated with increased mutant allele transcription and
gene activity [114].
UPD is the presence of two copies of a chromosome segment from one parent, and the
absence of that DNA from the other parent. Somatic UPD, also known as copy neutral LOH
(CNLOH), results in loss of heterozygosity (tumor versus normal), without a change in total DNA
copy number [115-117]. UPD is observed at tumor suppressor gene loci whereby upon loss of
the wild type allele, the mutated allele is duplicated resulting in a diploid state with homozygous
mutation of the target gene [118]. Interestingly, UPD events are also detected at mutated
oncogenes [114, 119-121]. Until recently, due to limitations in the resolution of genomic array
platforms, the prevalence of this event has been widely underestimated and underappreciated.
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Recent studies have shown that UPD events are frequently observed in tumor genomes, with
most of the findings reported from hematological malignancies [122-131]. Our genome wide
analysis of segmental gain, loss and UPD in the T47D breast cancer cell line genome identified
that a significant portion of the genome exhibits UPD, rivaling the proportion of the genome
affected by segmental gain and loss, and highlighting the potential of UPD as a prominent
mechanism of gene disruption in epithelial cancer (Figure 3). Interestingly, PIK3CA and TP53
mutations in T47D are noted in the Catalogue of Somatic Mutations in Cancer [132]. Integrative
analysis at these loci detected copy number increase at PIK3CA and copy number loss at TP53
illustrating the MASI concept described above (Figure 3).
Somatic UPD also exists at genes without mutation. The potential significance of this somatic
event is not readily apparent, but it raises the intriguing possibility of allelic conversion of
epigenetic status [117, 122, 133].
5.3 Epigenomic alterations
5.3.1 The cancer methylome
Abnormal DNA methylation patterns occur in cancer, whereby focal hypermethylation at many
CpG islands is evident in a background of global DNA hypomethylation [134-137]. Broad
hypomethylation may lead to genomic instability, while hypermethylation of CpG islands
silences transcription of specific genes [136, 138-140]. Non-random methylation of multiple
CpG islands observed in colon cancer led to the discovery of CpG island methylator phenotype
(CIMP), which is causally linked to microsatellite instability via silencing of the mismatch repair
gene, MLH1 [141-143].
The determination of DNA methylation status relies on the ability to discriminate between
methylated and unmethylated cytosines. This is achieved by exploiting methylation-
sensitive/insensitive isoschizomer restriction-enzyme pairs [144-150], chemical conversion of
unmethylated cytosine to uracil [151-156], and the affinity for methylated DNA of specially
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developed antibodies and methylated-DNA binding proteins [24, 157-163]. Several
computational methods have been developed for deriving approximations of actual methylation
levels from the relative levels generated by most microarray and locus specific sequencing
assays [147, 162, 164, 165]. However, it is important to note that CpG targets represented on
microarrays may or may not be the only elements controlling gene expression. Recently, it was
shown that in the human colon cancer methylome sequences up to 2 kb away from CpG
islands, termed CpG shores, exhibited more methylation than CpG islands and had greater
influence on gene expression than CpG islands [166]. Furthermore, while excess promoter
methylation is typically associated with transcriptional repression, the loss of required
methylation within gene bodies, proximal to promoters, can have the same effect [167]. DNA
methylation of epigenetic neighborhoods in the megabase size range has also been reported
[168]. Validation of methylation-mediated control of gene-specific expression, and evaluation of
biological significance, can be achieved via pharmacologic manipulation of DNA methylation, for
example by 5-azacytidine treatment, to relieve methylation silencing and invoke re-expression
[20, 169].
The first single-base-resolution maps of the human methylome have recently been generated
by sequencing of bisulfite converted DNA from human embryonic stem cells and fetal fibroblasts
[12, 170]. This landmark study will greatly advance the analysis of DNA methylation by
providing whole genome reference maps of methylation in these specific cells. However, it is
well known that DNA methylation is tissue specific and that it changes throughout development
thus, methylome maps for all tissues at various stages of development may be necessary to
provide adequate maps of 'normal' methylation patterns for use in deciphering aberrant
methylation patterns characteristic of tumors [171-176]. In recognition of this, the Human
Epigenome Project was launched in 2004 to map the methylomes of all major human tissues
[177].
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5.3.2 Integration of cancer genomic and epigenomic events
DNA methylation and genomic instability. Cancer-specific aberrant DNA methylation is
associated with reduced genomic stability and subsequent copy number alterations, including
preferential loss of certain imprinted alleles (LOI) [178-184]. Mechanistically, this instability may
be related to the susceptibility of hypomethylated DNA to undergo inappropriate recombination
events [185]. Another mechanism known to negatively impact genomic integrity in lung cancer
is the relaxation of transposable element control that is mediated by DNA methylation [186-190].
DNA hypomethylation and DNA amplification. Preliminary evidence of specific
demethylation of somatic segmental amplifications (or amplicons) has been put forth in lung
cancer, perhaps representing a novel mechanism of aberrant oncogene activation [189, 191].
Further studies using large-scale sequencing of bisulfite treated DNA will help to clarify this
phenomenon [12]. Hypomethylation has also been implicated in the formation of specific copy
number alterations in glioblastoma multiforme [192]. One potentially interesting application for
DNA methylation profiling of cancer amplicons such as these, is in the discrimination between
"driver" and "passenger" genes within the amplified sequence. It may be that DNA methylation
within the promoters or gene bodies of these genes is responsible for the lack of uniform
overexpression of genes residing within amplicons.
DNA hypermethylation and copy number loss. The relationship between DNA
hypermethylation and allelic loss is well documented. Tumor suppressor genes are frequently
found in regions of common LOH, and these same TSGs are frequently found to be
hypermethylated, perhaps best exemplified by the FHIT gene on chromosome 3p [193].
Although it is unclear whether loss or hypermethylation occurs first, both are known to be very
early events in tumorigenesis preceding any histologic alterations [194-196]. With the advent of
high resolution genome-wide technologies it has become possible to comprehensively search
for genes that are inactivated by both mechanisms simultaneously [197].
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Histone modification states. While DNA methylation and gene dosage profiling technologies
have become accessible, technologies for global assays of other key epigenetic marks including
histone modifications are not widely available. One of the main challenges to conducting the
highest quality studies of genome wide chromatin-immunoprecipitation on microarray (ChIP-
chip) or on sequencing platform (ChIP-seq) experiments is the requirement of high quality DNA
from pure cells – which essentially means growing cells in culture. It is thus difficult to analyze
these dimensions from clinical specimens. However, much has been learned from studies of
the relationship between different histone modification states and transcriptional activation or
repression in model systems. Such examples utilizing ChIP-chip include: cell or context
specific histone modification patterns related to cell or context specific gene expression; histone
3 lysine 27 (H3K27) trimethylation patterns associated with prostate, lung and breast cancers;
and H3K9 and H3K79 modification patterns in leukemia [198-204]. Examples utilizing ChIP-seq
include: the analysis of the growth inhibition program of the androgen receptor, and the
chromatic interaction network of the estrogen receptor [205, 206].
5.4 Relating genetic and epigenetic events to changes in the
transcriptome through integrative analysis
Aberrations in individual genetic or epigenetic dimensions are prominent across various cancer
types, culminating in changes to the transcriptome. However, for a given gene, most of the
events documented previously, such as copy number amplification, homozygous deletion,
somatic mutation, or DNA hypermethylation, do not occur in 100% of tumors for a given cancer
type. Moreover, it has been observed that the same gene may be activated or inactivated by
different mechanisms. Since most of the studies described above analyzed single DNA
dimensions, it is likely many genes would be overlooked due to a low frequency of alteration in
a single dimension; the same gene may be detected at a high frequency when multiple
dimensions are considered. Thus, analysis of more dimensions may reveal higher frequency
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gene-specific disruption with corresponding transcriptome aberrations for particular cancer
types, as would be expected for genes causative to cancer development.
5.4.1 Multiple mechanisms of gene disruption
Expression profiling studies have been instrumental in detecting genes dysregulated in cancer
[207-209]. However, aberrant expression of some genes may simply reflect incidental genome
instability or secondary dysregulation. Global gene expression profiling alone may not
distinguish causal events and bystander changes. One of the first studies to relate gene
expression changes with gene dosage status on a global scale was a parallel analysis of DNA
and mRNA [66, 210]. The same cDNA microarray platform was used to investigate impact of
DNA copy number alterations on the expression of over 6,500 genes. This study determined
that 62% of genes located within regions of DNA amplification showed elevated expression in
breast cancer. Subsequent studies in other cancer types revealed a broad range in the
correlation between increased gene dosage and expression levels for protein coding genes
(19% to 62%) [92, 207, 210-213]. Studies integrating gene dosage and gene expression have
identified cancer subtype-specific pathway activation and signatures associated with clinical
outcome [96, 214-217]. In addition, when examining known disease-relevant pathways, it has
been shown that even though individual components of a pathway are disrupted at a low
frequency, collectively, these alterations can result in frequent disruption of a given pathway [16,
92]. Similarly, alterations in DNA methylation or histone modification status can also affect gene
expression and have subsequent pathway level consequences (see above).
5.4.2 Multiple mechanisms of disrupting non-coding RNA levels
Segmental DNA copy number alterations also affect the expression of non-coding RNAs
(ncRNA) [218-222]. MicroRNAs (miRNA) have been shown to have a significant role in cancer
development with specific miRNAs implicated in a number of different cancer types [26, 223-
225]. Specific miRNA expression signatures are associated with critical steps in tumor initiation
and development including cell hyperproliferation, angiogenesis, tumor formation and
121

metastasis [226]. High throughput analysis of microRNAs has been of interest and microarrays
have been developed to assess essentially all annotated microRNAs. To date, >700 miRNAs
have been annotated in the genome (http://mirdb.org/miRDB/statistics.html, [227]), with more
likely to be discovered. For example, we recently demonstrated that a deletion on chromosome
5q leads to the reduced expression of two miRNAs that are abundant in hematopoietic
stem/progenitor cells. This study revealed haploinsufficiency and reduced expression of miR-
145 and miR-146a as mediators of a subtype of myelodysplastic syndrome [221]. Although the
genomic loss and underexpression implicates a tumor-suppressive role for these specific
miRNAs, others undergo activating genomic alterations and elevated expression and hence are
thought to be oncogenic [228, 229].
Just as copy number alterations can alter miRNA activity, epigenetic alterations have also been
shown to affect miRNA expression [230-232]. Aberrant methylation of miRNAs has been
reported in a variety of cancer types, and the disruption of epigenetically-mediated miRNA
control has been shown to have oncogenic effects due to downstream gene deregulation [233].
For example, abnormal DNA methylation of miRNAs has been associated with tumor
metastasis, leading to the appreciation of a group of metastasis-related miRNAs [229].
5.4.3 Multi-dimensional integration of genome, epigenome, and transcriptome
Large scale initiatives. Since multiple genomic/epigenomic mechanisms can influence gene
expression and lead to disruption of a given function, an integrative multi-dimensional analysis
is necessary for a more comprehensive understanding of the cancer phenotype (Figure 4).
Specific programs and initiatives such as those by The Cancer Genome Atlas (TCGA) project
and the cancer Biomedical Informatics Grid (caBIG) enable parallel and multi-dimensional
analysis of cancer genomes [8, 16] (Table 5.2). Recently, studies in glioblastoma and
osteosarcoma have shown that integrative genomic and epigenomic approaches can indeed
reveal the specific genetic pathways involved in different cancers [16, 234].
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Gene disruption by multiple mechanisms. One of the two key reasons for using an
integrative approach is the ability to detect critical genes that are disrupted by multiple
mechanisms across a sample set, but are disrupted at a low frequency by any one mechanism.
These genes would have been overlooked in previous, single dimensional studies. The second
key advantage of integrative approaches is the ability to identify genes that are simultaneously
disrupted by multiple mechanisms -- two hits -- in a single sample. Using a dataset comprised
of DNA copy number, allelic status, DNA methylation, and gene expression profiles from ten
lung adenocarcinomas and matched non-malignant tissue controls, we illustrate these benefits
below.
If gene expression changes are a consequence of alterations at the DNA level, then a higher
proportion of the observed expression changes can be directly attributed to a defined causal
event when multiple types of DNA alterations are examined (Figure 5.5a). While some
samples have over 70% of the expression associated with DNA level changes (Sample 7,
Sample 8), other samples have only 30% (Sample 5, Sample 9). Additionally, consequential to
associating more gene expression changes with DNA level changes within a sample, more
disrupted genes are detected, and in turn, more disrupted pathways are identified across a
sample set (Figure 5.5b, 5.5c). In fact, in our example, nearly five times as many genes
(~1100 compared to ~200) are detected as disrupted in at least 50% of the samples when we
account for multiple mechanisms of disruption (vs. one mechanism alone) (Figure 5.5c). This
result illustrates that without using an integrative approach, many potentially important genes
would be dismissed as they are disrupted by low frequency events when a single DNA
dimension is analyzed. This also holds true at the pathway level when the identified genes are
grouped based on their biological function (Figure 5.5d). For example, the Hepatic
Fibrosis/Hepatic Stellate Cell Activation pathway and the RAR Activation pathway, which are
identified when all DNA dimensions are considered, would not be detected as significantly
altered when using individual DNA dimensions alone.
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Implications on sample size requirements. In the example above, we illustrate that a
significant number of genes and pathways exhibit a low frequency of disruption when examining
single dimensions (and thus would be overlooked) but, indeed exhibit a high frequency of
disruption when multiple dimensions are considered (Figure 5.5). Notably, these findings imply
that integrative multi-dimensional analysis of individual samples may directly impact the cohort
sample size required for gene discovery on the basis of frequency of disruption (Figure 5.5e).
Reduction in sample size requirements means that one can extend this approach to situations
involving rare specimens where accrual of hundreds of samples in a reasonable timeframe is
not possible. Moreover, reduced sample sizes are particularly applicable to familial cancers or
to isolated populations at increased risk for specific cancers.
Bi-allelic gene disruption. Two-hit bi-allelic inactivation of genes and high level gene
amplifications are typically considered to be causal mechanisms that inflict gene expression
changes. When examining multiple DNA dimensions, concerted bi-allelic disruption of a gene in
the same sample can be readily identified; copy number loss with hypermethylation resulting in
underexpression, or copy number gain with hypomethylation and overexpression are examples.
Indeed, we do identify genes harboring concerted disruptions using the same lung
adenocarcinoma dataset mentioned above. The MUC1 locus exhibits concurrent copy number
increase with hypomethylation and overexpression (Figure 5.4). MUC1 has previously been
shown to be important in lung and breast cancers and is currently a target for therapeutic
intervention [235-237]. Collectively, we have demonstrated how an integrative, multi-
dimensional approach can be utilized for cancer gene and pathway discovery.
5.4.4 Disruption of multiple components in biological pathways
We described above how an integrative, multi-dimensional approach improves the detection of
disrupted genes, especially those affected by multiple low frequency mechanisms. This
concept can be extended to identify biological pathways, where multiple pathway components
are disrupted at low frequencies (see above; Figure 5.5d). The EGFR signaling pathway is a
124

well documented dysregulated component of lung cancer. Using the same multi-dimensional
profiling dataset from Figure 5 above, seven genes were detected with gene dosage alteration
at a frequency ≥30%. However, when we considered alterations in gene dosage, allelic status,
DNA methylation and somatic mutation collectively (for KRAS and EGFR only), 18 genes in the
pathway were identified to be altered at ≥30% frequency (Figure 5.6). The detection of the
additional 11 genes illustrates the benefit of employing an integrative approach and extends the
sample size reduction argument to the pathway level.
5.4.5 Identification of a novel gene involved with EGFR signaling deregulated in
adenocarcinoma
In the section above, I have shown that more of the well known components are frequently
altered when we examine multiple DNA dimensions as opposed to a single DNA dimension,
such as DNA copy number, alone. When this analysis is expanded to include more genes
based on literature evidence, I found that the most frequently disrupted gene is signal-regulatory
protein alpha (SIRPA) (Figure 5.7).
SIRPA has been shown to be down-regulated when EGFR is activated in glioblastoma and up-
regulated when EGFR is suppressed [238, 239]. SIRPA has also shown to be a tumor
suppressor gene in multiple cancer types including liver and breast cancer [240, 241].
Moreover, in the resting lung, SIRPA has been thought to modulate the inflammatory response
through SHP-1 (also known as PTPN6) and eventually, NFKB [242]. While most studies have
documented the association of SIRPA with SHP-2 (also known as PTPN11) [243-245], few
studies have shown the association of SIRPA with SHP-1.
To discern the association of SIRPA with SHP-1 and SHP-2, mutual information network
analysis was utilized [246, 247]. Briefly, using our gene expression dataset and a publicly
available dataset [248], Affymetrix exon array datasets were normalized separately using the
aroma.affymetrix package (Bengtsson et al 2008 Berkeley). Subsequently, each dataset was
125

analyzed using the "minet" package in the Bioconductor suite in R [247, 249]. From these
analysis, for each gene, a score between each gene and every other gene is calculated. The
top 5% of gene-gene interactions (based on the score) from each dataset were retained and
those interactions which were in the top 5% of both analyses were retained. Finally, gene-gene
interactions involving SIRPA were extracted, resulting in a total of 310 genes found to highly
correlate with SIRPA expression (Table 5.3). Within this list of genes, PTPN6 was present and
PTPN11 was not, suggesting that SIRPA is likely involving PTPN6 rather than PTPN11 in lung
adenocarcinoma.
5.4.6 Prevalence of SIRPA deregulation and association with clinical characteristics
Given that the sample set we examined comprised of only 10 samples, we then wanted to
assess the expression in a larger panel of samples to validate the frequency of underexpression
observed in the initial set. Using 59 lung adenocarcinoma and matched non-malignant sample
pairs, the prevalence of SIRPA underexpression was assessed and the correlation of SIRPA
and PTPN6 was re-evaluated. It was found that 47/59 pairs exhibited at least a 1.5-fold
reduction of SIRPA in tumors as compared to matched non-malignant tissue, representing
~80% of tumors assessed (Figure 5.8a). In addition, correlating SIRPA and PTPN6 expression
using a Pearson correlation, a correlation coefficient of 0.907 was found (Figure 5.8b).
It should also be noted that there was a small number of samples which exhibited
overexpression of SIRPA. This finding was somewhat unexpected given the high prevalence of
underexpression observed in the initial dataset. However, the initial ten tumors were from
individuals who were former smokers and the set of 59 tumors was comprised of 23 current
smokers, 21 former smokers and 15 never smokers. When stratifying the differential gene
expression based on smoking status, overexpression was not observed in any of the current or
former smokers, while overexpression was only observed in a subset of never smokers (Figure
5.8c).
126

Finally, using publicly microarray datasets with patient survival information [250-252], Kaplan-
Meier analysis was performed on each of these datasets based on SIRPA expression levels.
The association was deemed significant if the gene had a p-value ≤ 0.05 based on a Mantel-
Cox (or log ranks) test. Two of the five datasets showed a statistically significant association
between SIRPA expression levels and overall patient survival with an additional two datasets
close to significance with p values ≤ 0.18 (Figure 5.9).
5.5 Tracking clonal expansion in spatial dimensions
Delineating the clonal relationship between multiple tumors in the same patient is relevant not
only to clinical management of disease but also to the understanding of metastasis. Multiple
tumors in the same patient may not necessarily share an identical genomic profile. The
similarities and differences in genomic landscape between tumors are quantifiable and therefore
can be used for delineating relatedness. Whole genome comparison based on array CGH
profiles is a new tool for distinguishing metastatic from primary synchronous carcinomas. A
multitude of genomic features, for example the boundaries of segmental deletions, are used to
delineate the presence and the sequence of events in clonal evolution [253-261].
Furthermore, signature genetic alterations can be used to track clonality in a cell population,
putting genetic events in the context of tumor tissue architecture. By assessing the appearance
of pre-selected markers in individual nuclei on a tissue section by FISH, the clustering and the
expansion of clonally related cells can be delineated by analyzing the marker patterns of
neighboring cells (Figure 5.10).
5.6 Evaluating the biological significance of integrative genomics
findings
The utilization of an integrative genomic, epigenomic and transcriptomic approach will
undoubtedly improve our ability to identify gene disruptions and their effects on gene
127

expression. The next challenge is to develop approaches for the determination of functional
and phenotypic evidence of the biological relevance of such gene disruptions in a high
throughput manner -- for example, functional genomic screens by RNAi, proteomic profiling and
metabolite profiling. Forced expression of genes and RNAi knockdown of gene expression are
commonly used methods for assessing growth and invasion phenotypes in cell models.
Genome wide RNAi screens, comprised of large libraries of short hairpin RNA sequences
redundantly targeting thousands of genes, have been used to identify genes essential to
tumorigenesis, including tumor suppressor genes as well as cooperative genes with oncogenic
mutation in several malignancies [22, 28, 29, 262-270]. Animal models are also instrumental to
functional validation of genes singly or in combination, but this topic is beyond the scope of this
article. Cross referencing genomic findings with proteomic profiles will determine the functional
consequences yielding information on expression levels, post-translational modification, and
protein-protein interactions [271-275]. As recent studies have highlighted the importance of the
metabolome in cancer, the genomic landscape can also be integrated with metabolome profiles
to determine the role of genetic and epigenetic alterations in cellular physiology relevant to
cancer development [276-278].
The progress made in the development of technologies and approaches to analyze the
genome, epigenome, and transcriptome have allowed for much improved understanding of
cancer landscapes. With the increased application of sequence based approaches to analyze
genetic and epigenetic dimensions and the additional complexity with the proteome and
metabolome to follow, an unprecedented definition of the cancer cell can be achieved. The next
key challenge will be the synthesis of this information to better understand fundamental cancer
processes such as progression, metastasis and drug resistance.
128

Figure 5.1. Advances in cancer genomic landscape post Y2K.
2009
2007
2006
2005
2004
2002
DNA nanoballs sequencing technology [3]
Breast, lung & skin cancer genomes sequen ced [4-6,11]
Human met hylomes sequen ced [12]
Acute myeloid leukemia genome sequen ced [13,14]
International Cancer Genome Consortium initiated
1000 genomes p roject launched [15]
Integrative study of glioblas toma [16]
Genome RNAi database established [9,10]
2nd gene ration human haplo type map with >3M SN Ps [17]
Next generation, massi vely parallel sequencing technolo gies
Copy number variation maps [18,19]
5-Azacytidine re-expression of met hylated cancer genes [20]
Exome sequencing mut ation detection [21]
The RNAi Consortium (TRC) [22]
Bead Arrays for bisulfite DNA methylation [23]
NIH Cancer Genome Atlas (CGA) initiated
Methylome map by MeDNA immunop recipitation [24]
First human genome haplo type map [25]
MicroRNA expression profiles classify can cers [26]
Catalogue of som atic mutations in can cer (COSMIC) [27]
Large scale RN Ai-based sc reens [28,29]
Cancer Gene Census published [30]
Large scale copy number variation in humans [31,32]
Whole genome tiling p ath CGH microarrays [33]
Tiling path analysis of human t ranscribed sequen ces [34]
Cancer Biomedical Informatics Grid (caBIG) launched [8]
The Ensembl genome d atabase project [35]
The human genome b rowser at UCSC [36]
BeadArray genotyping pl atforms [37]
Concept of oncogene addi ction [38,39]
First human genome sequen ces [40,41]
CGAP launched [42,43]
Figure 5.1. Advances in cancer genomic landscape post Y2K. The timeframe of events
are estimated based on time of publication.
129

Figure 5.2
# of copies
Total copy number
4
2
0
Allele speci�c copy number
3
# of copies
2
1
0
(a)
# of copies
# of copies
4
2
0
3
2
1
0
130
Total copy number
Allele speci�c copy number
Neutral (b) Gain (c) UPD
Figure 5.2. SNP array analysis to identify areas of altered copy number and allelic
composition in a clinical lung cancer specimen. Shown here are (a) a region that is copy
neutral with no observed allelic imbalance and regions containing a (b) segmental gain and
(c) UPD. Examining the allele specific copy number plot, the gain (in b) is likely a single
copy change and the UPD event (in c) is signified by the shift in allele levels while maintaining
total copy number neutral status.

Figure 5.3
TP53
1 2 3 4 5 6 7 8
9 10
PIK3CA
11 12 13 14 15 16
17 18 19 20 21 22
131
Gain
Loss
UPD
Figure 5.3. Overlay of chromosomal regions of gain, loss and UPD (copy number
neutral LOH) inherent to the T47D breast cancer cell line. The chromosomal loci for
PIK3CA and TP53 (modified by activating and inactivating mutations, respectively, in this cell
line), are indicated. The majority of the genome is affected by any one of the three genomic
alterations. Raw SNP 6.0 array data was obtained from the Sanger database with mutation
status obtained from the COSMIC database [132]. Copy number and allelic status changes
were determined using Partek Genomics Suite and reference genomes used were 72
individuals from the HapMap collection. Data was visualized using the SIGMA2 software
[7].

Figure 5.4. Integration of copy number, allelic status, DNA methylation, and gene
expression for a single lung adenocarcinoma sample. (a) Copy number and (b) allele
status analyses revealed a high level allele-specific DNA amplification (highlighted in yellow,
image generated with Partek Genomics Suite); (c) individual CpG loci within this region were
assessed for differential methylation between tumor and non-malignant tissue.
Hypomethylation at the indicated CpG locus, which corresponds to the MUC1 gene, is observed
(visualized with Genesis). (d) Expression analysis revealed four-fold overexpression of the
MUC1 transcript when a tumor sample was compared to matched, adjacent non-malignant
tissue. Copy number and allele status profiling was performed using the Affymetrix SNP 6.0
array; DNA methylation profiling using the Illumina Infinium HM27 platform; and gene
expression using the Affymetrix Human Exon 1.0 ST array.
132

Figure 5.4
a
b
c
d
# of copies
# of copies
4
2
0
3
2
1
0
Normal
Tumor
Relative expression
7000
6000
5000
4000
3000
2000
1000
0
Total copy number: Ampli�cation
Allele speci�c copy number
DNA hypomethylation
Overexpression
MUC1
Normal Tumor
133

Figure 5.5. Integration of copy number, allelic status, DNA methylation, and gene
expression for a single lung adenocarcinoma sample. Enhanced analysis of the cancer
phenotype using an integrative and multi-dimensional approach. (a) On average, a higher
proportion of differential gene expression can be associated with genomic alterations when
examining multiple DNA dimensions relative to single dimensions. (b) Using a fixed frequency
threshold of 50 %, more genes are revealed to be frequently disrupted when multiple
mechanisms of genomic alteration (e.g. altered copy number, DNA methylation, or copy number
neutral LOH) are considered, (~200 genes versus more than 1000 genes). (c) Pathway
analyses performed using gene lists derived from a multi-dimensional approach, identifies an
enhanced number of aberrant pathways relative to those identified from a uni-dimensional
approach. (d) Functional pathways identified using the integrated gene list are of relatively high
significance; the top 10 such pathways are shown. This suggests that the additional identified
genes associate with specific pathways rather than with random functions. The four bars
represent, from left to right: all dimensions, copy number, DNA methylation, and UPD.
Ingenuity Pathway Analysis was used for analyses in (c) and (d). (e) Example of two genes that
are missed when a single DNA dimension is studied, but captured when multiple DNA
dimensions are examined. Both ribonucleotide reductase M2 (RRM2) [279, 280] and retinoic
acid receptor responder (tazarotene induced) 2 (RARRES2) [281, 282] are known to be
deregulated in multiple cancer types.
134

Figure 5.5
a
Proportion of di�erentially expressed genes
d
-log(pvalue)
e
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
6
4
2
0
0
DNA Methylation
Hepatic Fibrosis /
Hepatic Stellate
Cell Activation
RAR Activation
RARRES2
Copy Number Neutral
LOH (CNNLOH)
Macropinocytosis
Copy Number
Complement System
Leveraging All
Dimensions
Leukocyte Extravasation
Signaling
135
Reelin Signaling
in Neurons
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Sample 6
Sample 7
Sample 8
Sample 9
Sample 10
Average
Copy Number Copy Number
DNA Methylation DNA Methylation
CNNLOH CNNLOH
All Dimensions All Dimensions
b
c
Oncostatin M
Signaling
RRM2
# of genes identi�ed
# of Signi�cant Pathways
IL-8 Signaling
1400
1200
1000
800
600
400
200
60
50
40
30
20
10
0
0
CNNLOH
Copy Number
Copy Number
DNA Methylation
Acute Phase
Response Signaling
DNA Methylation
All Dimensions
CNNLOH
All Dimensions
CXCR4 Signaling
0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80
Frequency of Disruption (%) Frequency of Disruption (%)

Figure 5.6
SHC1
GRB2
SOS2
RRAS
ER
RAF1
MAP2K1
MAPK1
ITPR1
IP3
PLCG1
DAG
Ca2+
DUSP4
MAPK1 CCND1
EGF
Proliferation &
Differentiation
EGFR
PIK3R1
136
TGFA
ERBB2
PDK1
MUC1
PIP2 PIP3
PRKCA
CASP9
MYC
Cell Cycle
RASSF5
MST1
Apoptosis
AKT2 AKT1
FOXO3
Apoptosis
X
KRAS
RASSF1
X
CCND1
Proliferation
BAD
Figure 5.6. Identification of multiple disrupted components in a biological pathway.
Integrative analysis identifies more genes affected in the EGFR signaling pathway than a
single dimensional analysis alone. In this example, multi-dimensional profiling data were
generated from ten lung adenocarcinomas and their paired non-cancerous lung tissue.
Analysis of DNA copy number (gene dosage) alterations that affected expression, identified
7 genes (in green) that are disrupted at ≥ 30% frequency. However, when alterations in
copy number, DNA methylation, sequence mutation and/or copy neutral LOH were considered,
17 genes disrupted at ≥ 30% frequency were identified to be associated with a change
in expression, with an additional gene, KRAS, harboring frequent mutation. The 11 additional
genes are indicated in red. Genes in gray are not significant in this dataset as they
did not meet the frequency criteria.

Figure 5.7
SHC1 CN
S7 S9S10
GE
M
L
S5 S6S7
GE
CN
GRB2 M
L
S1 S8
GE
CN
SOS2 M
L
*
SIRPA CN
S1 S2S3 S4S5 S6 S7S8S9S10
GE
M
L
PI3K-AKT
Signalling
RRAS CN
S1 S2S3 S4S6 S9 S10
GE
M
L
ER
RAF1
MAP2K1
MAPK1
ITPR1 CN
S1 S2S3 S4 S6 S7S8S9
GE
M
L
S8 S10
GE
CN
M
L
S2 S6 S7S8
GE
CN
M
L
S6 S7S8
GE
CN
M
L
Ca2+
DUSP4
MAPK1 CCND1
S1 S2S4 S6S7 S8 S10
S1 S3S4 S5S9
GE * GE
CN
CN
M
EGF TGFA M
L
L
S1 S2S4 S7S8 S9 S10
GE
CN
M
L
µ µ
IP3
Proliferation &
PLCG1 CN
S2
GE
M
L
DAG
PRKCA CN
S1 S2 S4 S6 S8
GE
M
L
S5 S6S7 S8S10
GE
CN
M
L
MYC
Cell Cycle
EGFR
CASP9
PIK3R1
S1 S2S3 S4S5 S6 S7S8S9S10
GE
CN
M
L
RASSF5
RASSF1
Legend:
GE: Gene Expression: Over Under
CN: DNA Copy Number: Gain Loss
L: Allelic Status: LOH
Differentiation
M: DNA Methylation: Hypo Hyper
Figure 5.7. Multi-dimensional analysis of the epidermal growth factor receptor signaling
pathway. Integrative analysis identifies more genes affected in the EGFR signaling
pathway than a single dimensional analysis alone. In this example, multi-dimensional
profiling data were generated from ten lung adenocarcinomas and their paired noncancerous
lung tissue. Analysis of DNA copy number (gene dosage) alterations that
affected expression, identified 7 genes (in green) that are disrupted at ≥ 30% frequency.
However, when alterations in copy number, DNA methylation, sequence mutation and/or
copy neutral LOH were considered, 17 genes disrupted at ≥ 30% frequency were identified
to be associated with a change in expression, with an additional gene, KRAS, harboring
frequent mutation. The 11 additional genes are indicated in red. Genes in gray are not
significant in this dataset as they did not meet the frequency criteria. Genome profiles were
generated using the Affymetrix SNP 6.0 platform, DNA methylation data were genrated
using the Illumina Infinium HM27 platform and gene expression profiles were generated
using the Affymetrix Exon Array.
137
*
ERBB2
PIP2 PIP3
S2 S3S4 S5S8 S9 S10
GE
CN
M
L
MUC1 CN
S3 S5S6 S7S10
GE
M
L
PDK1 CN
S2 S3S4 S5S8 S9
GE
M
L
S7 S10
GE
CN
M
L
S3 S4S5 S6 S7S8
GE
CN
M
L
AKT2 AKT1
FOXO3
S1
GE
CN
M
L
Apoptosis
X
S1 S3S4 S6S8 S9 S10
GE
CN
M
L
KRAS CN
S1 S5S6 S8S9
GE
M
L
µ µ µ µ µ
X
CCND1
Proliferation
MST1
Apoptosis
S2 S5S8S9S10
GE
CN
M
L
S1 S3S4 S6S7 S10
GE
CN
M
L
BAD
S2
GE
CN
M
L

Figure 5.8
a
Log2 Fold Change (T vs. N)
b
c
4
3
2
1
0
-1
-2
-3
-4
% of underexpressing cases
100
80
60
40
20
0
PTPN6
r = 0.907426
SIRPA
Figure 5.8. Prevalence of SIRPA underexpression and its relationship with PTPN6
and smoking status. (a) Analysis of SIRPA and PTPN6 expression in 59 lung adenocarcinoma
tumor/non-malignant pairs using quantitative PCR. Plotted are the log2 fold changes
of each tumors versus its matched non-malignant sample. PCR data were normalized with
Beta-Actin. All samples were done in triplicate. Threshold lines denote a 1.5-fold change.
(b) Pairwise comparison of SIRPA and PTPN6 fold changes in the 59 sample pairs. Spearman
correlation coefficient was calculated. (c) Stratification of qPCR results based on
smoking status. While the majority of current smokers (CS, n=22) and former smokers
(n=22) show underexpression, a subset of never smokers (NS, n=15) exhibit overexpression.
138
% of overexpressing cases
30
20
10
SIRPA
PTPN6
0
CS FS NS CS FS NS
40
Threshold

Figure 5.9
Survival Ratio
Survival Ratio
1
0.9
0.8
0.7
0.6
0.5
0.4
Duke H. Lee Mo�tt
LowExpression
HighExpression
0.3
p = 0.009
0.1
p = 0.009
0.2
0 5 10 15 20 25 30 35 40 45
0
0 20 40 60 80 100 120
Time (months)
Time
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
MSKCC
LowExpression
HighExpression
p = 0.150 p = 0.180
0
0 50 100 150 200 250
Time
Figure 5.9. Kaplan-Meier analysis of SIRPA in four independent microarray datasets.
Using publicly available gene expression microarray data, Kaplan-Meier analysis was
performed to assess the association of SIRPA expression levels and overall patient survival.
Briefly, for each dataset, samples were sorted based on ascending SIRPA expression and
survival distributions of the top 1/3 of samples expressing SIRPA and bottom 1/3 of samples
expression SIRPA were compared. In total, five datasets were tested with two of the datsets
(Duke, H. Lee Moffitt) showing a stastistically significant association. In an additional two
datasets (MSKCC, Michigan), the p-values were close to statistical significance. All expression
data were normalized using RMA. P-values were calculated using a Mantel-Cox log
rank test.
Survival Ratio
Survival Ratio
139
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Michigan
LowExpression
HighExpression
LowExpression
HighExpression
0.2
0 20 40 60 80
Time
100 120 140 160

Figure 5.10
(a) (b)
Figure 5.10. Automated detection of selected clonal populations of cells within a
cancer biopsy tissue section. All nuclei (~150,000 in this example) are detected and
FISH probe signal counts are enumerated for each nucleus. FISH signal pattern for each
cell is compared against its neighbor in order to define spatial association (or neighborhood).
A mathematical model is then applied to determine clonal cell relationships. (a)
Mapping cancer cells on a tissue section. A gain or loss of any one of three FISH markers
indicates a cancer cell. This image shows the density of cancer cells (so defined) in neighborhoods
as a color overlay. Red indicates high fraction of cancer cells, yellow indicates
medium fraction of cancer cells and blue indicates low to none (see scale bar). Most of the
section is highlighted except for the surrounding normal stromal infiltrates. (b) Mapping
clonal cells. The same image data were analyzed for concurrent gains of each of the three
of the markers. The two clusters of cells, magnified within the white boxes, are cells harboring
gain of all three markers.
140

Table 5.1. List of software for integrative analysis
Software
Agilent
Genomic
Workbench
5.0
Source:
Commercia
l (C)
or
Academic
(A)
Genome
Epigenome
Transcriptome
Integrative
141
Citatio
n
C X X X X N/A
SIGMA2 A X X X X [7]
Integrative
Genomics
Viewer
Nexus Copy
Number
Website (http://www.)
chem.agilent.com/enus/products/instruments/dnamicroar
rays/
dnaanalyticssoftware/pages/default.
aspx
flintbox.com/technology.asp?page=
3716
A X X X N/A broadinstitute.org/igv/
C X X X N/A biodiscovery.com/index/nexus
CGH Fusion C X X N/A infoquant.com/index/cghfusion
ISA-CGH A X X X [283] isacgh.bioinfo.cipf.es
VAMP
Partek
A X X X X [284] bioinfo-out.curie.fr/projects/vamp/
Genomics
Suite
C X X X X N/A partek.com/partekgs

Table 5.2. List of genomic resources and databases
Name Description Citation Website (http://www.)
ArrayExpress Gene
Expression Atlas
Gene expression analysis of public
datasets
[285] ebi.ac.uk/gxa
BioDrugScreen
Catalogue of Somatic
Protein/Small molecule interaction
database
[286] biodrugscreen.org
Mutations in Cancer
(COSMIC)
Listing of somatic mutations in cancer [132] sanger.ac.uk/cosmic
Cancer Gene Expression
Database (CGED)
Gene expression analysis of cancer [287] cged.hgc.jp
Database of Differentially
Expressed Proteins in
human Cancers (dbDEPC)
Differentially expressed proteins in
cancer
[288] dbdepc.biosino.org/index
Database of Genomics
Variants
Reported normal copy number
variations
[31] projects.tcag.ca/variation
European Bioinformatics
Institute (EBI)
Integrated database of multiple
biological resources
[289] ebi.ac.uk
GeneCards
Integrated database of multiple
biological resources
[290] genecards.org
GenomeRNAi RNAi experiment results [10] rnai2.dkfz.de/GenomeRNAi
Human DNA Methylome
Whole genome methylation sequences
of multiple individuals
[12]
neomorph.salk.edu/human
_methylome
Human Histone Modification
Database (HHMD)
Histone modification database [291] bioinfo.hrbmu.edu.cn/hhmd
microRNA.org Annotated microRNAs and their targets [292] microRNA.org
miR2Disease Deregulated microRNAs in cancer [293] miR2Disease.org
miRDB Annotated microRNAs and their targets [227] mirdb.org
miRGen Annotated microRNAs and their targets [294]
diana.cslab.ece.ntua.gr/mir
gen
National Center for
Biotechnology Information
(NCBI)
Integrated database of multiple
biological resources
[295] ncbi.nlm.nih.gov
NCBI Cancer Chromosomes
Curated cytogenetic alterations in
cancer
[295]
ncbi.nlm.nih.gov/sites/entre
z?db=cancerchromosomes
NCBI GEO Profiles
Gene expression analysis of public
datasets
[296]
ncbi.nlm.nih.gov/sites/entre
z?db=geo
Oncomine
Gene expression analysis of public
datasets
[297] oncomine.org
PROGENETIX
Copy number aberrations in cancer by
CGH
[298] progenetix.net
PRoteomics IDentifications
Database (PRIDE)
Mass spectrometry results [299] ebi.ac.uk/pride
Sanger CGP LOH And Copy
Number Analysis
Copy number and LOH profiles of
cancer cell lines
-
sanger.ac.uk/cgibin/genetics/CGP/cghviewe
r/CghHome.cgi
siRecords
System for Integrative
RNAi experiment results [300]
siRecords.umn.edu/siRecor
ds
Genomic Microarray
Analysis (SIGMA)
Array CGH profiles of cancer cell lines [301] sigma.bccrc.ca
The Cancer Genome
Anatomy Project (CGAP)
Gene expression analysis of cancer [43] cgap.nci.nih.gov/
The Cancer Genome Atlas
(TCGA)
Multi-dimensional description of cancer
genomes
[16]
cancergenome.nih.gov/dat
aportal/data/about/
UCSC Genome Browser
Integrated database of multiple
biological resources
[302]
genome.ucsc.edu/cgibin/hgNear
142

Table 5.3. Genes interacting with SIRPA as identified by network analysis
Gene Gene Gene Gene Gene Gene Gene Gene
ABCG1 C5AR1 DOK2 GPR65 LILRA1 NTNG1 RECK STK10
ABI3BP C7orf44 DPEP2 GPR85 LILRA5 NTRK3 RHOG STK33
ACOT1 CANT1 DSE GPX3 LILRB1 NUP62CL RHOJ STX11
ACP5 CCND2 EMP3 GSPT2 LILRB2 OGN RNASEK SULF1
ACSL4 CD14 EMR1 GTDC1 LILRB5
LOC440
OLFML1 RTN1 TACSTD1
ACVRL1 CD163 ETS1 GYPC 295 OR1J1 RUNX1T1 TARP
ACY1 CD300C EVI2B HCK LOXL2 PARVB SAMHD1 TARS2
ADAMTSL4 CD300LF FAAH2 HERPUD1 LPAR1 PCDH15
PDCD1LG
SELPLG TCEAL2
ADARB1 CD33 FAM107A HIST2H4A LPIN1 2 SH2B3 TCF21
ADC CD34 FAM65A HSD11B1 LPXN PDE3B SIGLEC7 TDRKH
ADCY4 CD4 FBLN5 HSPB7 LRCH2 PHEX SIP1 TFE3
ADCY7 CD53 FBXL17 HVCN1 LRRC25 PHKA1 SIRPB1 TGFBR3
ADPRH CD86 FBXL2 IFI30 LRRC33
LRRC37
PIK3AP1 SLA TLN1
ADRA1A CD93 FCER1G IGSF10 A
LRRC8
PIK3R5 SLC15A3 TLR4
ADRBK1 CD97 FCGR1A IGSF2 C PILRA SLC16A2 TLR8
AGER CDH1 FCGR3A IL17RA LST1 PLEK SLC22A25 TM6SF1
AKR7A3 CDKL3 FERMT3 IL8RA LTBP2 PLEKHA8 SLC25A10 TMED3
TMEM183
AKT1 CFD FGD2 IRF6 MAF PLEKHO2 SLC25A29 B
MAN1C
TMEM184
ALS2CR12 CFP FGF2 IRF8 1
MAP3K
PMP22 SLC2A9 A
ANGPTL1 CLEC4E FGFR4 ITGA5 3
MARCH
PNPLA6 SLC31A2 TMEM47
ANKRD36 CLN3 FGL2 ITGAL 1 PPAP2B SLC7A11 TMTC1
TNFRSF1
AOC3 CMKLR1 FGR ITPR3 MARCO PPM1F SLC7A7 B
ARHGAP3
MCOLN PPP1R14
0 COASY FHL1 ITPRIP 1 B SLC8A1 TPK1
ARRB2 COG1 FIBIN JAM2 MFNG PRCP SLCO2B1 TPRG1
ATP6V1B2 COMMD3 FIGF JUNB MMP19 PREX1 SLFN13 TRPV2
BAALC CPA3 FLI1 KCNJ5 MORC2 PROM1 SMARCA2 TSPAN18
BHLHB3 CPVL FLJ22662 KCNK1 MRAS PRUNE2 SMYD3 TTC13
BRCC3 CSF1R FPR1 KCTD12 MRC2 PTGDS SNN TTC30B
BTK CSF2RB
CSGALNA
FRMD4A KIF26B MS4A15 PTGER4 SORBS1 TYROBP
BVES CT1 FXYD6 KLF13 MSRB3
MTMR1
PTGIS SORD TYW1
BZW2 CYP4Z1 GFRA2 KLF4 0 PTPN6 SPARCL1 USP48
C10orf54 CYTH4 GIMAP1 KMO MYCT1 PTPRG SPI1 VAMP7
C10orf72 CYYR1 GIMAP4 LAIR1 MYD88 PVRL4 SPN VASH1
C14orf49 DAB2 GIMAP5 LAMB3 NCF1
NCKAP
QKI SPOCK2 VAT1
C1orf38 DNAH7 GIMAP6 LAMC2 1L QSER1 SPON1 VSIG4
C1QA DNAJB4 GIMAP8 LAPTM5 NEK4 RAD54B SRGN VWF
C1QB DNASE1L3 GLIPR2 LAT2 NEXN RASSF2 SRPX WDR60
C1QC DOCK11 GMFG LCP1 NLRC4 RBM17 STAP2
C1RL DOCK8 GNAI2 LDB2 NTAN1 RBM35A STAT5A
143

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Chapter 6: Conclusions
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6.1 Summary
Lung adenocarcinoma is the most commonly diagnosed form of lung cancer today, with a large
percentage of patients exhibiting poor overall survival and prognosis. Genomic analysis has
provided much insight into this disease with the identification of specific differentially expressed
genes, somatically mutated genes, hyper- and hypomethylated genes and genes which are
amplified and deleted at the DNA copy number level. While tools and platforms for whole
genome analysis of gene dosage and gene expression are widely accessible and have
improved in resolution, only until recently have technologies to assess DNA methylation in a
high throughput manner been made available. Hence, the logical step with these vast amounts
of data is to integrate the information from these different assays in a parallel manner to gain a
better understanding of the biology of lung adenocarcinoma.
6.1.1 Development of the integrative genetic and epigenetic approach
In chapter 2, the development of the SIGMA2 software package is discussed [1]. At the time
the package was developed, there were no analysis tools for integrative genetic and epigenetic
analysis, let alone tools to integrate gene dosage and gene expression, which were two well
established high throughput platforms. Moreover, for array CGH data alone, limited number of
tools existed. Hence, as a precursor to SIGMA2, SIGMA [2], was developed and used as a
framework for SIGMA2.
In chapter 3, I demonstrated how when this integrative approach is applied to model systems,
that we learn much more using multiple dimensions as compared to when only looking at a
single dimension alone. Specifically, we show that we can associate more of the dysregulated
gene expression with alterations at the DNA level, with some cell lines illustrating as much as
80% of the observed gene expression changes being able to be associated with DNA level
changes. In addition, I also illustrated two key concepts: (i) the “Or” (multiple alternate hit
mechanisms) concept where across a sample set and using a fixed frequency of disruption, we
163

can identify nearly three times as many genes when we can account for multiple types of
disruption as opposed to accounting for only a single type of alteration and (ii) the “And”
(coupled multiple hits) concept where we identify genes which are targeted my multiple
mechanisms in the sample and show that these genes can have significant biological and/or
clinical relevance.
6.1.2 Identification of a prevalent genetic alteration in lung adenocarcinoma
Genetic and epigenetic alterations have been shown to be prominent in lung adenocarcinoma.
Within genetic alterations, the majority of documented alterations have involved alterations in
gene dosage, somatic mutation, and loss of heterozygosity (LOH) or allelic imbalance (whereby
an allele or a portion of the allele is lost or gained in the tumor). In terms of allelic imbalance,
the majority of the time this event is captured as a decrease or increase in gene dosage.
However, there are also cases where allelic imbalance exists but there is no net change in gene
dosage, termed copy neutral LOH or somatic uniparental disomy (UPD). Chapter 4 discusses
the unexpected prevalence of UPD in the lung adenocarcinoma genome.
Though previous studies were done using SNP arrays on lung adenocarcinoma tumors, the
prevalence of UPD was likely underestimated due to a number of reasons. Amongst the
reasons include the use of heterogeneous samples with high normal cell contamination due to
lack of microdissection, lower resolution of alterations identifiable by previous SNP arrays, use
of non patient-matched controls as reference and movement from call-based algorithms to
algorithms which use allele specific copy number [3].
In addition to the prevalence of UPD, the other key finding from this chapter is the presence of
frequent UPD at both known and novel oncogenes. While UPD has previously been shown to
affect tumor suppressor genes such as RB1 [4], the association of UPD at oncogene loci has
not been reported as often in solid tumors. Moreover, in the previous studies in hematological
malignancies such as leukemias, lymphomas, or myeloid dysplastic syndromes, the observed
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UPD at oncogenes was typically accompanied by an acquired homozygous mutation at the
locus [3, 5-8]. From our data, though UPD was also observed at mutated KRAS, as shown
previously [9], there was also frequent UPD in cases where KRAS was not mutated. This
finding suggests that in the cases which UPD occurs without somatic mutation, that the UPD
event may in fact be used a mechanism for preferential allele selection. Specifically, this could
be preference for the methylated allele for tumor suppressor genes or unmethylated allele for
oncogenes [10, 11]. Alternatively, the preference could be for a more transcriptionally active (or
inactive) allele as it has been shown that for specific genes, the two alleles may differ in rates of
transcription [12-16]. Thus, integration of genetic data with epigenetic and gene expression
data would help decipher the target of these frequently observed UPD events.
6.1.3 Application of the integrative approach to lung adenocarcinoma specimens
Thus far, I have shown that the integrative genetic and epigenetic approach is beneficial in
identifying important genes; both which would have been missed if single assays alone were
used and those which have concurrent alteration at multiple levels. Chapter 5 discusses how
upon application of this approach to lung adenocarcinoma specimens, we see that this trend
also holds true in clinical samples. Specifically, I show that novel canonical signaling pathways
are significantly enriched for when multiple DNA-based dimensions are used but are missed
(not statistically significant) when a single DNA-based dimension is used. In addition, when we
examined the well-documented EGFR signaling pathway, a pathway known to be involved in
lung adenocarcinoma, the most frequently disrupted gene was signal-regulatory protein alpha
(SIRPA).
SIRPA has been shown to be a direct downstream component of EGFR, and has been shown
to be suppressed in expression by EGFR activation [17, 18]. In the resting lung, SIRPA has
been postulated to control the inflammatory response through SHP-1 and eventually, NFKB
[19]. While there are likely multiple components between SIRPA and NFKB, we wanted to
assess expression of components directly associated with SIRPA. In addition, since this gene
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was identified in a small set of samples, we wanted to see if this prevalence of disruption was
maintained in an additional, larger set of tumors. Hence, we evaluated expression of SHP-1
and SIRPA in a panel of approximately 60 lung adenocarcinoma tumors and found (i) a high
prevalence of underexpression of SIRPA and (ii) a strong correlation between SIRPA and SHP-
1 expression levels. It is interesting to observe this strong relationship between SIRPA and
SHP-1 as most cancer studies have focused on SIRPA’s relationship with SHP-2 instead of
SHP-1.
6.2 Conclusions
I have demonstrated the power of an integrative genetic and epigenetic approach to decipher
resultant gene expression changes in lung adenocarcinoma. The development of an
application such as SIGMA2 was integral as it represented one of the first academic/research
applications with the ability to integrate multiple dimensions of data. To date, there have been a
few other applications that have been developed that can perform similar functionalities but
most of these have been developed by commercial entities. Moreover, the software still is not
out-dated and based on the way it was built, can be extended to handle newer high throughput
platforms including sequence-based platforms.
In terms of what we learn from both the demonstration dataset (Chapter 3) as well as clinical
tumor dataset (Chapter 5), we know that by using an integrative, multi-dimensional approach,
we are detecting genes being disrupted at a much higher frequency when multiple dimensions
are examined as compared to single dimensions alone. Moreover, at a given detection
frequency, a gene may be disrupted by a single dimension at a low frequency but when multiple
dimensions are accounted for, the frequency is in fact high. In Figure 5.5, I illustrate how well
known lung cancer genes such as RRM2 are altered at both the genetic and epigenetic level
and illustrate how more pathways are deemed significant when multiple dimensions are
analyzed. The latter finding is likely a result of the fact that within a given pathway, not only can
different genes be affected in different samples by one mechanism (e.g. DNA copy number
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amplification), but they also can be affected by different, but complementary, mechanisms (e.g.
DNA methylation). These findings validate part A of the hypothesis. In addition, when aligning
genetic and epigenetic profiles, I show that a high proportion of the observed differential
expression can be attributed to genetic and epigenetic changes, validating part B of the
hypothesis (genetic and epigenetic changes resulting in aberrant gene expression). Finally,
when examining the EGFR signaling pathway, we observe that a number of key genes are
frequently altered, while other genes are not altered as often. The most frequently affected
gene, SIRPA, exhibited both genetic and epigenetic alteration and in some cases, this occurred
concurrently within the same sample. Moreover, it was also found from both in the analyses of
chapter 3 (breast cancer) and chapter 5 (lung cancer), that over three times as many genes are
identified as frequently aberrant when using multiple dimensions as compared to any single
dimension. These findings validate part C of the hypothesis, whereby a gene within a
commonly deregulated lung cancer pathway was identified using the integrative genetic and
epigenetic approach.
This finding has potential implications on the sample sizes necessary to discover important
alterations as one can look at a small number of samples in more detail rather than a large
sample set using a single assay. Specifically, these would be the genes that are disrupted at a
low frequency in one dimension but high frequency across multiple dimensions as large sample
sets would be needed to be confident of the low single dimension alteration frequency. Such
considerations exist in situations where large sample sets are not feasible due to rarity and
preciousness of samples.
In terms of the multi-dimensional perspective on lung adenocarcinoma, in addition to finding
additional genes and pathways that are disrupted when we look at multiple dimensions, when
examining known genes and pathways, we see a complex pattern of deregulation with some
components altered more frequently than others. This added complexity highlights how each
tumor is different from one another and the rational approach to identifying therapeutic targets
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will be done at the pathway level as opposed to the gene level. It is clear that within these
pathways, key nodes or “choke points” will likely serve as the best targets for therapeutic
intervention.
6.3 Future directions
There are two key future directions which should be pursued at this point; (i) further evaluation
of SIRPA as a novel tumor suppressor gene in lung adenocarcinoma, (ii) evaluation of the novel
signaling pathways implicated through multi-dimensional analysis, and (iii) incorporation of data
from other dimensions not evaluated in this thesis.
In terms of SIRPA, the first experiments that need to be done are to assess whether the
deregulated mRNA expression of SIRPA is also observed at the protein level. One such
approach would be using immunohistochemistry on a tissue microarray comprised of hundreds
of samples with well annotated clinical information. Subsequently, the frequency of
underexpression, the correlation with overall patient survival, and the overexpression
associated with a subset of tumors from patients with never smoking history could be validated.
In addition, depending on what clinical information is available, one could correlate to other
parameters that were not available to me from the public gene expression microarray datasets
to uncover other interesting clinical associations.
Secondly, with the amount of literature suggesting that SIRPA could be a tumor suppressor
gene in adenocarcinoma, the next set of experiments would be designed to test the tumor
suppressor role of SIRPA. This would require the silencing of the gene in normal cells, using
RNAi based techniques for example, and assess tumorigenic phenotypes such as anchorage
independent growth, reduction of apoptosis, and increase in proliferation. In addition, parallel
gene introduction experiments would also need to be done in cancer cell lines which exhibit little
or no expression of SIRPA and the level of suppression of the above listed tumor phenotypes
would then be assessed.
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One of the canonical signaling pathways that was identified as the most statistically significant
by Ingenuity Pathway Analysis is the Hepatic Fibrosis /Hepatic Stellate Cell Activation pathway.
While the existence and role of stellate cells have been well documented in the liver and
pancreas, there have been a limited number of reports of stellate cells in the lung [20]. From
what is known in the liver and pancreas, stellate cells are involved in tissue fibrosis and
inflammation in chronic diseases such as pancreatitis and hepatitis [21-25]. In pancreatic
tumors, activated stellate cells promote an increase in connective tissue surrounding the tumors
(termed the desmoplastic process) and have been shown to be proliferative in the presence of
tumor secreted factors [25]. In addition, stellate cells also have implications in drug resistance
[26]. In the lung, it is plausible to envision a role of stellate cells in diseases such as chronic
obstructive pulmonary disease (COPD) where tissue fibrosis and inflammation are prominent
[27]. One of the challenges to testing this function in vitro is that it would be important to
recapitulate the tumor microenvironment. Hence, this function would have to be tested in vivo
using inducible mouse models where expression of secreted factors associated with stellate cell
activation, which were identified from our analysis, can be assessed. Phenotypes such as
cellular proliferation, apoptosis, and drug resistance could then be assayed and compared
between pre and post-induction of these secreted factors.
Finally, although multiple DNA dimensions were analyzed in this thesis, recent advances in
technology have allowed for other dimensions that could be incorporated. For example,
genome sequencing technologies allow for the detection of novel somatic mutations in a high
throughput manner. While performing this at the whole genome level is financially and
computationally challenging, this effort can be focused on examining the "exome" (DNA from
gene coding exons only) using sequence capture based techniques [28, 29]. MicroRNAs have
also shown to be important in lung cancer, with specific microRNAs shown to be differentially
expressed [30-36]. MicroRNAs can affect downstream protein expression through a number of
different mechanisms [37-40]. Integration of microRNA and sequence mutation data with the
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previously described genetic and epigenetic data would further increase our understanding of
the biology of lung adenocarcinoma.
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APPENDIX I: List of publications
This appendix details all of the publications that I was a part of that were either published,
accepted, currently in submission, or prepared for submission. In total, 29 publications are
listed below with four of them represented as chapters and an additional nine discussed in
section 1.11. The remaining 16 publications are listed below with a brief description
accompanying each publication.
Publications included as thesis chapters
1. Chari R, Coe BP, Wedseltoft C, Benetti M, Wilson IM, Vucic E, MacAulay C, Ng RT, Lam
WL. (2008) SIGMA2: A system for the integrative genomic multi-dimensional analysis of cancer
genomes, epigenomes, and transcriptomes. BMC Bioinformatics, 9(1):422, 1-12.
This publication is included in the thesis as chapter 2.
2. Chari R, Coe BP, Vucic EA, Lockwood WW, Lam WL. (2010) An integrative multi-
dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer.
BMC Systems Biology. Submitted.
This manuscript submitted for publication is included in the thesis as chapter 3.
3. Chari R, Lockwood WW, Coe BP, Soh J, MacAulay C, Lam S, Gazdar AF, Lam WL. (2010)
UPD is a frequent mechanism of gene disruption in lung adenocarcinoma.
This manuscript in preparation is included in the thesis as chapter 4.
4. Chari R, Thu KL, Wilson IM, Lockwood WW, Lonergan KM, Coe BP, Malloff CA, Gazdar AF,
Lam S, Garnis C, MacAulay CE, Alvarez CE, Lam WL. (2010) Integrating the multiple
dimensions of genomic and epigenomic landscapes of cancer. Cancer and Metastasis
Reviews, 29(1):73-93.
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This publication is included in the thesis as chapter 5.
Publications discussed in section 1.11 (9 listed)
5. Chari R, Lockwood WW, Coe BP, Chu A, Macey D, Thomson A, Davies JJ, MacAulay C,
Lam WL. (2006) SIGMA: A system for the integrative genomic microarray analysis of cancer
genomes. BMC Genomics, 7(1):324, 1-11.
This publication is described in section 1.11.1.
6. Coe BP, Chari R, MacAulay C, Lam WL. (2010) FACADE: A fast and sensitive algorithm for
the segmentation and calling of high resolution array CGH data. Nucleic Acids Research.
Submitted.
This publication is described in section 1.11.1.
7. Lonergan KM, Chari R, deLeeuw RJ, Shadeo A, Chi B, Tsao M, Jones S, Marra M, Ng R,
MacAulay C, Lam S, Lam WL. (2006) Identification of novel lung genes in bronchial epithelium
by serial analysis of gene expression. American Journal of Respiratory Cell and Molecular
Biology, 35(6):651-61.
This publication is described in section 1.11.2.
8. Chari R, Lonergan KM, Ng RT, MacAulay C, Lam S, Lam WL. (2007) Effect of active
smoking on the bronchial epithelial transcriptome. BMC Genomics, 8(1):297, 1-13.
This publication is described in section 1.11.2.
9. Lee EHL*, Chari R*, Lam A, Ng RT, Yee J, English J, Evans KG, MacAulay C, Lam S, Lam
WL. (2008) Disruption of the non-canonical Wnt pathway in lung squamous cell carcinoma.
Clinical Medicine: Oncology, 2:169-179. *These authors contributed equally
This publication is described in section 1.11.3.
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This publication is described in section 1.11.3.
10. Lonergan KM, Chari R, Coe BP, Wilson IM, Tsao M-S, Ng RT, MacAulay C, Lam S, Lam
WL. (2010) Transcriptome profiles of carcinoma-in-situ and invasive non-small cell lung cancer
as revealed by SAGE. PLoS One, 5(2):e9162, 1-22.
This publication is described in section 1.11.3.
11. Chari R, Lonergan KM, Pikor LA, Coe BP, Zhu CQ, Chan THW, MacAulay C, Tsao M-S,
Lam S, Ng RT, Lam WL. (2010) A sequenced-based approach to identify reference genes for
gene expression analysis. BMC Medical Genomics. Submitted.
This publication is described in section 1.11.3.
12. Lockwood WW, Chari R, Coe BP, Girard L, MacAulay C, Lam S, Gazdar AF, Minna JD,
Lam WL. (2008) DNA amplification is a ubiquitous mechanism of oncogene activation in lung
and other cancers. Oncogene, 27(33):4615-4624.
This publication is described in section 1.11.4.
13. Lockwood WW, Chari R, Coe BP, Thu KL, Garnis C, Campbell J, Williams AC, Hwang D,
Zhu CQ, Yee J, English J, Tsao M-S, Gazdar AF, MacAulay C, Minna JD, Lam S, Lam WL.
(2010) BRF2 is a lineage specific oncogene amplified early in squamous cell lung cancer
development. PLoS Medicine. Submitted.
This publication is described in section 1.11.4.
Other publications not discussed in this thesis (16 listed)
Array comparative genomic hybridization and its application to multiple cancer types
14. Garnis C, Chari R, Buys TP, Zhang L, Ng RT, Rosin MP, Lam WL. (2009) Genomic
imbalances in precancerous tissues signal oral cancer risk. Molecular Cancer, 8:50, 1-7.
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The development of oral cancer is thought to occur through the progression of histopathological
stages, from different stages of dyplasia (mild, moderate, and severe) to carcinoma in situ to
invasive disease. Similar to many cancer types, early detection of this disease is critical for
good prognosis. As such, it is important to be able to determine which cases will and will not
progress at early stages of the disease such as mild dysplasia. This manuscript describes the
use of array CGH as a tool to predict progression in genomes of mild dysplasia patients and it
was shown that the level of genomic alteration had high concordance with disease progression.
15. Coe BP, Lockwood WW, Chari R, Lam WL. (2009) Comparative genomic hybridization on
BAC arrays. Methods in Molecular Biology, 556:7-19.
This publication is a chapter in the Methods in Molecular Biology textbook and describes the
process of developing, using and analyzing data from bacterial artificial chromosome CGH
arrays.
16. deLeeuw RJ, Zettl A, Klinker E, Haralambieva E, Trottier M, Chari R, Ge Y, Gascoyne RD,
Chott A, Muller-Hermelink HK, Lam WL. (2007) Whole genome analysis and HLA haplotyping
of enteropathy-type T-cell lymphoma reveals two distinct lymphoma subtypes.
Gastroenterology, 132(5):1902-11.
Enteropathy-type T-cell lypmhoma (ETL) is an aggressive non-Hodgkin lymphoma and the
genetic alterations underlying this disease were not well understood. In this publication, array
CGH was applied to samples from patients with ETL and based on the genetic alterations and
HLA genotyping, it was found that two distinct subytpes of this disease existed, which was
contrary to the clinical classification used at the time.
17. Buys TPH, Wilson IM, Coe BP, Lee EHL, Kennett JY, Lockwood WW, Tsui IFL, Shadeo A,
Chari R, Garnis C, Lam WL. (2006) “Detailed Comparisons of Cancer Genomes” in
Comparative Genomics: Fundamental and Applied Perspectives (Brown JR, ed.), CRC Press /
Taylor & Francis, LLC, Boca Raton, FLA, pp. 245-259.
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This chapter details the technologies used for cancer genome comparisons as well as the
different types of comparisons that are currently undertaken in research today such as the
comparison of cancer subtypes, clonal versus multiple primary tumors, cancer susceptibility and
drug sensitivity.
18. Lockwood WW*, Chari R*, Chi B, Lam WL. (2006) Recent advances in array comparative
genomic hybridization technologies and their applications in human genetics. European Journal
of Human Genetics, 14(2):139-48.
This publication is a review of literature describing the advances in array CGH technology and
its application to many genetic diseases, including cancer.
19. Buys TPH, Wilson IM, Coe BP, Lockwood WW, Davies JJ, Chari R, DeLeeuw RJ, Shadeo
A, MacAulay C, Lam WL. (2005) “Key Features of BAC Array Production and Usage” in DNA
Microarrays (Methods Express Series) (Schena M, ed), Scion Publishing, Ltd., Bloxham, pp.
115-145.
This chapter describes the production and use of bacterial artificial chromosome microarray-
based CGH and the analysis of the data generated by this platform. In addition, protocols for
array CGH experiments are also provided.
Gene expression based studies
20. Coe BP, Chari R, Lockwood WW, Lam WL. (2008) Evolving strategies for global gene
expression analysis of cancer. Journal of Cellular Physiology, 217(3):590-597.
This publication is a review of literature describing the advancement in technology to analyze
gene expression in cancer and the movement of the field towards integrative genomics.
21. Shadeo A, Chari R, Lonergan KM, Pusic A, Miller D, Ehlen T, Van Niekerk D, Matisic J,
Richards-Kortum R, Follen M, Guillaud M, Lam WL, MacAulay C. (2008) Up regulation in gene
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expression of chromatin remodelling factors in cervical intraepithelial neoplasia. BMC
Genomics, 9(1):64, 1-14.
Cervical cancer is a major problem in developing countries. Similar to oral cancer, it is thought
to go through a progression of histopathological stages and thus identifying markers at stages
of intervention are crucial to the prognoses of patients with this disease. In this publication, a
comparison of normal cervical tissue with cervical intraepithelial neoplasia (CIN) was performed
to identify genes upregulated in CIN. It was found that genes involved in chromatin remodelling
were upregulated in CIN.
22. Shadeo A, Chari R, Vatcher G, Campbell J, Lonergan KM, Matisic J, van NieKerk D, Ehlen
T, Miller D, Follen M, Lam WL, MacAulay C. (2007) Comprehensive serial analysis of gene
expression of the cervical transcriptome. BMC Genomics, 8(1):142, 1-11.
This publication describes the transcriptome of normal cervix tissue using serial analysis of
gene expression.
Integrative analysis of multiple DNA and RNA dimensions
23. Wilson IM, Vucic EA, Chari R, Zhang Y-A, Starczynowski DT, Lonergan KM, Enfield KSS,
Buys TPH, Yee J, Laird-Offringa I, Karsan A, Liu P, You M, Anderson M, MacAulay C, Lam S,
Gazdar AF, Lam WL. (2010) EYA4 is a non-small cell lung cancer tumor suppressor located in
the susceptibility locus on chromosome 6q.
Chromosome arm 6q has been shown to harbor a region associated with lung cancer
susceptibility based on the analysis of familial lung cancer datasets. Moreover, this specific
region is also frequently lost in sporadic, non-familial lung cancers as well. Hence, many
studies have been undertaken to identify the gene(s) in this region which may critical to lung
tumorigenesis. In this manuscript, we detail the use of a genetic and epigenetic approach to
identify key genes in this region which are frequently deregulated by concerted genetic and
179

epigenetic alteration. This led to the identification of the gene EYA4, which we further
demonstrate to have tumor suppressive activity.
24. Soh J, Okumura N, Lockwood WW, Yamamoto H, Shigematsu H, Zhang W, Chari R,
Shames D, Tang X, MacAulay C, Varella-Garcia M, Vooder T, Wistuba II, Lam S, Brekken R,
Toyooka S, Minna JD, Lam WL, Gazdar AF. (2009) Oncogene mutations, copy number gains
and mutant allele specific imbalance (MASI) frequently occur together in tumor cells. PLoS
One, 4(10):e7464, 1-13.
Somatic mutation of both oncogenes and tumor suppressor genes have been shown to be
important in cancer. While tumor suppressor genes typically are recessive and require both
alleles to harbor mutation, activating mutations of oncogenes generally only require one of the
alleles to be mutated. However, it has been shown that for specific activating mutations,
multiple mutated copies can exist. In this study, using some of the most commonly mutated
genes in multiple cancer types, the prevalence of this phenomenon was assessed in set of cell
lines and tumors representing lung, pancreatic and colorectal cancers. It was found that for the
EGFR locus, mutation of the gene is accompanied with copy number increase whereby there is
preferential gain of the mutated copy and for KRAS, the event observed is acquired uniparental
disomy where the wild type copy is lost and the mutant copy is duplicated.
25. Campbell JM, Lockwood WW, Buys TP, Chari R, Coe BP, Lam S, Lam WL. (2008)
Integrative genomic and gene expression analysis of chromosome 7 identified novel oncogene
loci in non-small cell lung cancer. Genome, 51(12): 1032–1039.
Genomic alteration of chromosome 7 is a frequent event in non-small cell lung cancer. While
the most commonly known oncogenes on this chromosome include EGFR, MET and BRAF,
there are likely other candidate genes which may have a role in lung tumorigenesis. In this
manuscript, utilizing an integrative genetic and gene expression approach, novel oncogene loci
are identified.
180

26. Buys TPH, Chari R, Lee E, Zhang M, MacAulay C, Lam S, Lam WL, Ling V. (2007)
Genetic changes in the evolution of multidrug resistance for cultured human ovarian cancer
cells. Genes, Chromosomes and Cancer, 46(12):1069-79.
Drug resistance is a common problem for cancer patients treated by chemotherapeutics. One
of the mechanisms of resistance is through the multi-drug resistance phenotype which is often
associated with the activity of ATP-binding cassette (ABC) transporters. In this study, using an
ovarian cancer cell line exposed to increasing concentrations of vincristine to derive drug
resistant derivatives, the genetic and gene expression profiles were compared between these
resistant derivatives and the original cancer cell line. It was found that in while initial resistant
derivatives (lines exposed to lower concentration of drug) harbored copy number and gene
expression increase of ABCC1 and ABCC6, latter resistant derivatives (lines exposed to higher
concentration of drug) did not have the increase in ABCC1 and ABCC6, but had an increase of
ABCB, suggesting the drug resistance phenotype may be a dynamic process.
27. Coe BP, Lockwood WW, Girard L, Chari R, Minna JD, MacAulay C, Lam S, Gazdar AF,
Lam WL. (2006) Differential regulation of cell cycle pathways in small cell and non-small cell
lung cancer. British Journal of Cancer, (12):1927-35.
Small cell lung cancer (SCLC) and non-small cell lung cancer (SCLC) are the two major cell
types of lung cancer. While pathologically they can be distinguished, the molecular basis of
these two cancer types is not well understood. In this study, a whole genome integrative
genetic and gene expression comparison of NSCLC and SCLC was performed and differential
regulation of cell cycle pathways was identified. Specifically, NSCLC is primarily deregulated at
the receptor level while SCLC is primarily deregulated at the nuclear transcription factor level.
Software, analysis approaches, and databases
28. Tsui IFL, Chari R, Buys TPH, Lam WL. (2007) Public databases and software for the
pathway analysis of cancer genomes. Cancer Informatics, 3:389-407.
181

This manuscript describes the currently available computational resources for the analysis of
pathways in cancer. Specifically, the use of these resources to analyze results from high
throughput studies examining genetic, epigenetic or gene expression alterations.
29. Chari R*, Lockwood WW*, Lam WL. (2006) Computational methods for the analysis of
array comparative genomic hybridization. Cancer Informatics, (2):48-58.
This manuscript describes the most commonly used analysis strategies for array CGH data and
compares and contrasts these approaches. In addition, the specific features of currently
available software suites are also compared.
182

APPENDIX II: Description of cell lines
Sample ER Status PR Status HER2 Status TP53 Mutation Status**
HCC38 - - +
HCC1008 - - N/A
HCC1143 - - +
HCC1395 + - +
HCC1599 - - +
HCC1937 - - + (heterozygous mutation)
HCC2218 - + + -
BT474 + - + +
MCF7 + + +
MCF10A N/A N/A N/A N/A
** mutation status obtained from the Sanger Cancer Cell Line Project
(http://www.sanger.ac.uk/genetics/CGP/CellLines/)
183

APPENDIX III: Sources of data
Sample DNA Copy
Number -
Array CGH
Allelic Status
- Affymetrix
SNP 500K
HCC38 GSE21540 https://cabig.n
ci.nih.gov
(GSE21347)
HCC1008 GSE21540 https://cabig.n
ci.nih.gov
(GSE21347)
HCC1143 GSE21540 https://cabig.n
ci.nih.gov
(GSE21347)
HCC1395 GSE21540 https://cabig.n
ci.nih.gov
(GSE21347)
HCC1599 GSE21540 https://cabig.n
ci.nih.gov
(GSE21347)
HCC1937 GSE21540 https://cabig.n
ci.nih.gov
(GSE21347)
HCC2218 GSE21540 https://cabig.n
ci.nih.gov
(GSE21347)
BT474 GSE21540 https://cabig.n
ci.nih.gov
(GSE21347)
MCF7 GSE21540 https://cabig.n
ci.nih.gov
(GSE21347)
184
DNA
Methylation -
Illumina
Infinium
new data for
this publication
(GSE17769)
new data for
this publication
(GSE17769)
new data for
this publication
(GSE17769)
new data for
this publication
(GSE17769)
new data for
this publication
(GSE17769)
new data for
this publication
(GSE17769)
new data for
this publication
(GSE17769)
new data for
this publication
(GSE17769)
new data for
this publication
(GSE17769)
MCF10A N/A N/A new data for
this publication
(GSE17769)
Gene expression -
Affymetrix U133 Plus
2.0 (NCBI GEO
Accession number
provided)
new data for this
publication (GSE17768)
new data for this
publication (GSE17768)
new data for this
publication (GSE17768)
new data for this
publication (GSE17768)
new data for this
publication (GSE17768)
new data for this
publication (GSE17768)
new data for this
publication (GSE17768)
https://cabig.nci.nih.gov
(GSE17768)
https://cabig.nci.nih.gov
(GSE17768)
GSM254525
(GSE17768)

APPENDIX IV: MCD strategy and Kaplan-Meier analysis of
TUSC3
185

APPENDIX V: Kaplan-Meier and Oncomine expression
analysis of frequent MCD genes
Symbol (+) (-) Total
Survival
Associated*
186
**Status in Tumors (p-value)
SH3TC1 0 6 6 No -
CCNA1 0 5 5 Yes -
COL7A1 0 5 5 No -
KCTD4 0 5 5 N/A Not tested
LMCD1 5 0 5 Yes O3(6.3E-4), O5(3.1E-10)
LYAR 0 5 5 Yes U5(3.8E-4), O3(2.6E-4)
MTMR9 0 5 5 No U3(1.8E-7)
SYT8 0 5 5 N/A Not tested
TUSC3 0 5 5 Yes U5(7.4E-5)
ASAM
0 4 4 N/A Not tested
B3GALNT1 4 0 4 No O3(1.1E-7)
COL17A1 0 4 4 No U1(6.8E-5), U3(1.4E-8)
ELK3 0 4 4 Yes -
FGFR1 0 4 4 Yes U1(2.6E-8),U3(4.2E-6),U4(1.3E-7)
KRT17 0 4 4 No U1 (2.3E-11),U2 (1.1E-7), U3(3.9E-7)
LCP1 0 4 4 Yes -
OSBPL5 0 4 4 N/A Not tested
PSD3 0 4 4 Yes -
SFXN3 0 4 4 N/A Not tested
SH3BGRL3 0 4 4 No O2(2.8E-4)
SNRPN 0 4 4 No U3(5.4E-10), U5(2.7E-5)
TNFRSF10D 0 4 4 No O5(5.2E-4), U3(9.7E-4)
TNS4 0 4 4 N/A Not tested
*Survival associated if gene expression was significant associated with survival in at least one
of the two datasets tested (based on p < 0.05 using the log rank test).
**U=underexpressed between tumor and normal, O=overexpressed between tumor and normal
in the particular dataset; The numbers 1-5 indicate the reports from which the data originated,
1= [1], 2= [2], 3=[3], 4=[4], 5=[5], 6=[6]; “-“ indicates gene was either not represented or not
statistically differentially expressed based on group-wise analysis. (+) represents two-fold
overexpression, copy number gain, hypomethylation and allelic imbalance; (-) represents twofold
underexpression, copy number loss, hypermethylation, and LOH in the same sample and
the number of samples in our dataset which met this criteria.

REFERENCES
1. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van
de Rijn M, Jeffrey SS et al: Gene expression patterns of breast carcinomas
distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A
2001, 98(19):10869-10874.
2. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross
DT, Johnsen H, Akslen LA et al: Molecular portraits of human breast tumours.
Nature 2000, 406(6797):747-752.
3. Richardson AL, Wang ZC, De Nicolo A, Lu X, Brown M, Miron A, Liao X, Iglehart JD,
Livingston DM, Ganesan S: X chromosomal abnormalities in basal-like human
breast cancer. Cancer Cell 2006, 9(2):121-132.
4. Radvanyi L, Singh-Sandhu D, Gallichan S, Lovitt C, Pedyczak A, Mallo G, Gish K, Kwok
K, Hanna W, Zubovits J et al: The gene associated with trichorhinophalangeal
syndrome in humans is overexpressed in breast cancer. Proc Natl Acad Sci U S A
2005, 102(31):11005-11010.
5. Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M, Zhao H, Chen H, Omeroglu
G, Meterissian S, Omeroglu A et al: Stromal gene expression predicts clinical
outcome in breast cancer. Nat Med 2008, 14(5):518-527.
6. Karnoub AE, Dash AB, Vo AP, Sullivan A, Brooks MW, Bell GW, Richardson AL, Polyak
K, Tubo R, Weinberg RA: Mesenchymal stem cells within tumour stroma promote
breast cancer metastasis. Nature 2007, 449(7162):557-563.
187

APPENDIX VI: Summary of Kaplan-Meier survival analysis
GeneSymbol
Alternative
Names
van de Vijver - Pvalue
188
Sorlie - Pvalue
SH3TC1 FLJ20356 Fail N/A
CCNA1 0.01484628 N/A
COL7A1 Fail N/A
KCTD4 N/A N/A
LMCD1 Fail 0.00261366
LYAR FLJ20425 0.00551113 Fail
MTMR9 DKFZP434K171 Fail N/A
SYT8 DKFZp434K0322 N/A N/A
TUSC3 N33 0.01696356 Fail
ASAM N/A N/A
B3GALNT1 B3GALT3 Fail N/A
COL17A1 Fail Fail
ELK3 0.04816902 N/A
FGFR1 Fail 0.0147898
KRT17 Fail Fail
LCP1 0.01132949 0.04024164
OSBPL5 N/A N/A
PSD3 DKFZp761K1423 0.00205916 N/A
SFXN3 N/A N/A
SH3BGRL3 N/A Fail
SNRPN Fail Fail
TNFRSF10D Fail N/A
TNS4 N/A N/A
Fail = p-value > 0.05; N/A = not represented on array platform

APPENDIX VII: Copy of UBC Research Ethics Board
certificate of approval
189

University of British Columbia - British Columbia Cancer Agency
Research Ethics Board (UBC BCCA REB)
UBC BCCA Research Ethics Board
Fairmont Medical Building (6th Floor)
614 - 750 West Broadway
Vancouver, BC V5Z 1H5
Tel: (604) 877-6284 Fax: (604) 708-2132
E-mail: reb@bccancer.bc.ca
Website: http://www.bccancer.bc.ca >
Research Ethics
RISe: http://rise.ubc.ca
Certificate of Expedited Approval: Annual
Renewal
PRINCIPAL INVESTIGATOR: INSTITUTION / DEPARTMENT: REB NUMBER:
Wan Lam
BCCA/BCCA/Cancer Genetics &
Development (BCCA)
H08-01392
INSTITUTION(S) WHERE RESEARCH WILL BE CARRIED OUT:
Institution Site
BC Cancer Agency
Other locations where the research will be conducted:
N/A
Vancouver BCCA
PRINCIPAL INVESTIGATOR FOR EACH ADDITIONAL PARTICIPATING BCCA CENTRE:
Vancouver: Wan Lam Vancouver Island: N/A
Fraser Valley: N/A Southern Interior: N/A
Abbotsford Centre: N/A
SPONSORING AGENCIES AND COORDINATING GROUPS:
Canadian Institutes of Health Research (CIHR)
PROJECT TITLE:
Development of a multi-spectral platform for integrated analysis of clinical and research samples.
APPROVAL DATE: EXPIRY DATE OF THIS APPROVAL: PAA#: H08-01392-A003
August 4, 2009 August 4, 2010
CERTIFICATION:
1. The membership of the UBC BCCA REB complies with the membership requirements for research ethics
boards defined in Division 5 of the Food and Drug Regulations of Canada.
2. The UBC BCCA REB carries out its functions in a manner fully consistent with Good Clinical Practices.
3. The UBC BCCA REB has reviewed and approved the research project named on this Certificate of Approval
including any associated consent form and taken the action noted above. This research project is to be
conducted by the provincial investigator named above. This review and the associated minutes of the UBC
BCCA REB have been documented electronically and in writing.
The UBC BCCA Research Ethics Board has reviewed the documentation for the above named project. The research
study as presented in documentation, was found to be acceptable on ethical grounds for research involving human
subjects and was approved for renewal by the UBC BCCA REB.
UBC BCCA Ethics Board Approval of the above has been verified by one of the following:
Dr. George Browman, Chair
Dr. Lynne Nakashima, Second Vice-Chair
If you have any questions, please call:
Bonnie Shields, Manager, BCCA Research Ethics Board: 604-877-6284 or e-mail: reb@bccancer.bc.ca
Dr. George Browman, Chair: 604-877-6284 or e-mail: gbrowman@bccancer.bc.ca
Dr. Lynne Nakashima, Second Vice-Chair: 604-707-5989 or e-mail: lnakas@bccancer.bc.ca
https://rise.ubc.ca/rise/Doc/0/JKQ8088GG9RKN55VLAL9OHM869/fromString.html
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