Haematologica 2003 - Supplements

P2.2
GENETIC PROFILING: FROM GENE EXPRESSION
PROFILES TO SNPs.
Brian Van Ness, Paula Croonquist, Fangyi Zhao, Michael
Linden, Sarah Griffin, Deborah McWilliam, Theresa
Faltesek, Montse Rue, and Martin Oken
The University of Minnesota and the Eastern Cooperative
Oncology Group.
Increasing evidence has demonstrated that genetic factors are
involved in the pathogenesis of multiple myeloma. Genetic
alterations or gene deregulation influences not only the initiation
of disease but disease progression and therapeutic response. One
of the difficulties in predicting disease progression and
therapeutic response is the genetic heterogeneity in malignant
plasma cells – both at the level of deregulated levels of
expression and at the level of genetic variations that alter protein
function. Moreover, reprogramming of the bone marrow
microenvironment has been shown to play an important role in
stimulating plasma cell growth, altering therapeutic response, and
contributing to secondary complications. Among the factors
induced in bone marrow stromal cells, IL-6 has been shown to
play a prominent role in plasma cell proliferation. One of the
most common genetic alterations in myeloma plasma cells (40-
50% of patients) is the activating mutations in the ras family of
oncogenes. Furthermore, cells with mutant ras show resistance
to a variety of therapeutic agents. Recent evidence from gene
expression profiles demonstrates myeloma plasma cells can be
distinguished from their normal plasma cell counterpart.
However, different proliferative signals might be expected to
influence different sets of genes. In order to distinguish the
contributions made by IL-6, stromal cell contact, or mutant ras
activation, we have compared the gene expression profile of
myeloma cell lines grown in IL-6, stromal co-culture and cells
stably transfected with a mutant Nras gene. A simple expectation
was that mutant Nras may induce a subset of genes seen in IL-6
response, and IL-6 response would provide a subset of the pattern
derived from stromal interactions. However, our results show a
much more complex pattern of expression induced by the three
conditions of growth induction.
With support from the Multiple Myeloma Research Foundation
we developed gene expression profiles using the Affymetrix
U95A GeneChip containing 12,626 known genes. Cell cycle
analysis demonstrated that the myeloma derived ANBL-6 cell
line could be significantly induced to proliferate by addition of
IL-6, or by growth in co-culture with bone marrow stromal cells,
or after stable expression of the mutant Nras61 gene. Six
untreated controls, four IL-6 treated cell cultures, three mutant
ras containing cultures, and five co-cultures with bone marrow
stroma (3 normal; 2 patient derived) were analyzed. Hierarchical
clustering was used to visualize groups of genes that showed
common and distinct expression patterns under the four
conditions. From this analysis we were able to identify signature
gene expression patterns that defined each of the proliferative
signals, including sets of genes that were up-regulated as well as
down-regulated. 138 genes were identified that were
significantly differentially expressed when comparing untreated
cells with IL-6 treated cells. Not surprisingly, the highest
percentage represented cell cycle genes (54%). 84 genes were
differentially expressed in comparisons of IL-6 and stromal cocultures;
with a distinct set of genes differentially expressed from
the stromal interactions, that were not identified in IL-6
responses. A high percentage of these were extracellular matrix
associated genes and chemokines. Interestingly, there were 130
genes that distinguish IL-6 and mutant ras responses, with
patterns suggesting that mutant ras does not simply induce a
common subset of IL-6 response genes. Additional comparisons
and specific gene patterns will be presented that demonstrate the
similarities and differences in gene expression among these
common myeloma cell responses.
Notably, of the 30 most differentially expressed genes that
distinguish MM1 and MM4 in the patient expression profiles, we
could account for 24 of these derived from one or more of the
conditions we assayed. Some genes showed induced expression
in all treatments, others were induced specifically by only one of
the conditions examined. RT-PCR confirms common or
differential expression patterns. One gene of interest that was
induced is the EZH2 gene, a polycomb group gene that is
involved in transcriptional repression. EZH2 is not expressed in
normal plasma cells, confers a proliferative phenotype in other
cancer cells, is active in aggressive myeloma (MM4) cells, and is
induced by the conditions we studied. Further analysis of EZH2
and its role in myeloma cell proliferation is underway.
While gene expression profiles can provide important clinical
classifications, it is also important to consider not only the level of
expression, but the functional variation of key genes in the patient
population. Indeed, expression patterns may target further studies
of functional genetic variants. And while genetic mutations in
oncogens or tumor suppressor genes are associated with myeloma,
genetic variants of cytokine or growth factor genes, drug response
genes, and DNA repair genes may contribute to variability in both
the growth and therapeutic response seen in patients, as well as
secondary complications. We have chosen a set of candidate genes
that meet the following criteria for analysis of single nucleotide
polymorphisms (SNPs): 1) each gene has been shown to be
involved in myeloma growth, drug response, or DNA repair; 2)
each gene has polymorphic alleles that exist at frequencies in at
least 5% of the general population; 3) each polymorphism has a
known functional consequence on protein activity. Using this set
of criteria we identified 16 candidate genes that are being analyzed
in three ECOG phase III clinical trials, and correlated to survival,
disease progression, bone disease, toxicity, response, and incidence
of secondary malignancies. This study provides a systematic
analysis of genetic polymorphisms and their effects on critical
disease factors, and serves to compliment the clinical correlations
derived from gene expression profiling. The ultimate goal of
genetic correlations to clinical outcome is the development of
individualized approaches to therapy. This represents the
beginning of international efforts to establish a large DNA bank
and develop a population based study of genetic variants in
myeloma (Bank On A Cure[BOAC TM ]; supported by the
International Myeloma Foundation).
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