Transcript Slides

Genetic Regulators of
Large-scale Transcriptional
Signatures in Cancer
Presented by Mei Liu
September 26, 2007
Introduction
 Over the years
 Global gene expression profiles of thousands of disease
specimens, especially cancer, have been analyzed
 Hundreds of gene expression signatures associated with
disease progression, prognosis, and response to therapy have
been described
 The signatures encompass genes that are associated with
many important parameters of cancer, but their control
mechanisms are still largely unknown
 Limitations
 Each signature contains large number of genes, so it is
technically infeasible to study the function of an expression
signature as a whole
 Forced to study candidate genes individually or a handful of
genes in multiplex fashion
Introduction
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Limited assessment of the functional consequences of a
signature
Hampered development of specific therapies that may target
cancer on the basis of their gene expression signatures
 Gene expression signatures may arise in cancer samples
for many reasons:
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Variations in the composition of cell types
Responses to different host environment
Accumulated effects of aneuploidy and epigenetic changes
(acting in cis)
Response to altered activities of key transcriptional
regulators in cancer (acting in trans)
Introduction
 To experimentally reproduce and functionally assess the
consequences of gene expression signatures
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Regulators offer an efficient approach
Encodes transcriptional factors or signal proteins that
controls hundreds of downstream genes
 Disadvantages:
 A signature may be controlled by one or more regulators that
act in a conditional or combinatorial manner
 Regulator itself may not be part of the expression signature
  Need an unbiased genome-wide method to identify
functional regulators of gene expression signatures
Introduction
 Proposed a general method based on genetic linkage
 Identify functional regulators that drive large-scale
transcriptional signatures in cancer
 Intersect genome-wide DNA copy number and gene
expression data
 Used the method to identify genetic regulators of the
‘wound respond signature’ in human breast cancers
 Based on the concept that
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molecular programs of normal wound healing might be
reactivated in cancer metastasis
The wound signature might be genetically determined
because it is expressed in tumor cells and is a consistent
feature in repeat sampling of tumors
Results – Linkage Analysis
 Genotype – genetic makeup (particular set of genes an
organism possesses)
 Phenotype – actual physical properties (i.e. height, weight,
hair color)
 Linkage analysis aims to associate the pattern of genotype
dist. with the pattern of phenotype dist. in a group of
individuals in order identify the likely genes that control
the phenotype
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In this case, phenotype is the presence or absence of the gene
expression signatures in cancer samples
 Difficulty
 Genes involved in linkage analyses << # of samples
 ~ 10,000 genes vs. ~50 samples in typical microarray studies
of cancer
 Insufficient statistical power to map the linkage to each gene
Results – Linkage Analysis
 SLAMS (Stepwise Linkage Analysis of Microarray
Signatures)
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Initially map linkage of prospective regulator genes to large
chromosomal regions
Then refine and validate the list of candidate regulators
within the linked region using additional sources of data
 Overcome inherent noise in gene expression and DNA
copy number data
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Define phenotype in the linkage analysis by the coordinate
behavior of many genes within a gene expression signature
Establish linkage to chromosomal regions by coordinate
amplification or deletion of several neighboring loci
Results – Linkage Analysis
 SLAMS (four-step strategy)
 #1: Sort tumors into two groups by
presence or absence of the signature
 #2: Rank the change in DNA copy
number of each gene by association
with the signature
 #3: Filter candidate genes encoded
within the linked chromosomal locus
by their transcriptional regulation
 #4: Validate based on ability of their
expression levels to predict the
signature in additional tumor samples
Results – Linkage Analysis
 SLAMS Application
 Analyzed 37 breast tumors for gene expression patterns
and mapped for DNA copy number change at 6,692 loci
 Observed amplification of 57 DNA probes in
association with the wound signature
 32 probes represent chromosome 8q
 132 probes representing 8q out of total 6,692 probes
 Probability of encountering 32 of 132 probes from one
chromosomal arm in 57 random trials is 3.4 x 10-41
 Strong linkage between amplification of a large region
of 8q with the wound signature
Results – Linkage Analysis
 Filtered the 32 amplified genes in 8q based on their mRNA
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expression patterns in 85 breast tumors
Tested association between level of mRNA expression of
candidate genes and the wound signature
CSN5 showed the strongest positive correlation with the
wound signature among tumor samples
Pairwise and iterative analysis of CSN5 with candidate
regulators suggested the combination of CSN5 with MYC
mRNA was significantly associated with the wound
signature (P = 6.6 x 10-6)
Predict CSN5 and MYC function together to activate the
wound signature
Results – Linkage Analysis
 Identify the optimal
regulatory model of the
wound signature in tumor
samples
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mRNA expression levels of
MYC and CSN5
Two-tiered DT assigned
tumor samples to 2 groups
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Group 1: low CSN5 or
MYC mRNA level
Group 2: moderate or high
levels of CSN5 & MYC
Results – Linkage Analysis
 Substantial different wound score
 80% samples with an activated wound signature (wound
score  0.2) are captured in group 2
 High expression level of CSN5 & MYC is a significant
predictor of poor patient survival in breast tumors
 CSN5 & MYC function together to induce poor-prognosis
program in human breast cancers
Results – Linkage Analysis
 Verify the association between wound signature
and amplification of CSN5 and MYC
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Quantified DNA copy number of CSN5 & MYC
loci using an independent set of 41 early breast
tumor samples
Tumors with the wound signature had significantly
higher copy number of CSN5 & MYC
 MYC & CSN5 as candidate regulators of the
wound signature is further supported by additional
sources of information
Results – Linkage Analysis
 Would signature is based on the sustained transcriptional
response of fibroblasts to serum stimulation
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MYC was strongly induced during serum response, as were
CSN5 and other CSN components
 CSN6 is a bona fide member of wound signature
 MYC & CSN5 can activate a subset of normally serum
responsive genes
 MYC is required for transcriptional response of fibroblast
in response to serum
 Although wound signature genes are not enriched for
chromosome 8q localization, they overlapped significantly
with MYC target genes (P < 10-8)
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Suggest direct regulation of wound signature by MYC
Results – Regulator Validation
 Validation of wound signature regulation by MYC and
CSN5
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Experimentally validated roles of MYC & CSN5 in wound
signature and cancer progression
 Induced 201 of 255 genes representing ‘activated’ wound
signature
 Repressed 114 of 257 genes representing ‘quiescent’ wound
signature
Magnitude of wound signature activation induced by MYC
& CSN5 co-expression corresponds to
 7.3-fold increased risk of death
 5.2-fold increased risk of metastatis
Confirmed that MYC & CSN5 are causative genetic lesions
in breast cancers with the wound signature
Results – Functional Consequences
 Co-expression of MYC & CSN5
 Increased cell proliferation
compared with either gene alone
 Altered cell shape:
 appeared round
 less polarized
 loss of actin stress fibers and focal
adhesion contacts
 Increased the ability of the cells to
invade
 MYC & CSN5 cooperate
functionally to confer several
properties associated with invasive
tumor cells
Results – Regulation Mechanisms
 Mechanisms of gene regulation via interplay of MYC and
CSN5
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Over-expression of CSN5 increased the rate of MYC
ubiquitination by 3-fold
CSN5 strongly increased turnover of MYC protein
No MYC target genes were repressed by CSN5
coexpression, suggesting that CSN5 specifically promotes
transcription of select MYC target genes
All results together show that CSN5 is an essential activator
of MYC transcriptional activity
CSN5 increases the transcriptional potency of MYC toward
select target genes to promote proliferation, survival, and
invasion
Conclusion
 Developed an integrated genomic approach to
identify genetic regulators of large-scale
transcriptional signatures in human cancers
 Method is general and may be used to identify
linkage between gene expression signatures and
other types of genetic data
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SNPs or DNA methylation maps
 Limitation: requires human interpretation, which
may introduce subjective bias
Conclusion
 The wound signature application illustrates several
advantages of finding genetic regulators
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Simplify the application of diagnostic signatures in
the clinical setting
Knowledge of the regulators allowed us to activate
the wound signature in untransformed breast
epithelial cells to an extent seen in cancer samples
SLAMS method and functional validation can
clarify the regulatory architecture of expression
signatures and resolves signatures that are causally
related vs. those merely occur at the same time
Conclusion
 The method may be generally useful as a starting
point in understanding the regulation and
functions of gene expression signatures in cancer
 Inhibition of CSN5-mediated regulation of MYC
may be a useful therapeutic strategy for high-risk
breast cancers