Title: Topics in Cancer Genomics
Abstract: My talk will focus on how to leverage thousands of cancer
genomes and functional genomics datasets to discover
disease-associated regulators and variations. First, I will go over
the ENCODE annotation related to the cancer genome. I will show how
extended gene annotation allows us to place oncogenic transformations
in the context of a large cell space; here, many normal-to-tumor
transitions move towards a stem-like state, while oncogene knockdowns
show an opposing trend. Second, I will look at how these can be recast
into a comprehensive regulatory network of both transcription factors
and RNA-binding proteins (TFs and RBPs). I will showcase their value
by highlighting how SUB1, a previously uncharacterized RBP, drives
aberrant tumor expression and amplifies the effect of the well-known
oncogenic TF MYC. Third, I will describe a workflow to prioritize key
elements and variants. I will showcase the application of this
prioritization to somatic burdening, cancer differential expression
and GWAS (LARVA, MOAT & uORF tools). Finally, I will put all these
methods together through application to the PCAWG dataset. In this
analysis, we integrate genomic annotations and predicted functional
impact scores to quantify the overall burdening of various elements in
cancer genomes. We also show how the overall functional burdening of
various genomic elements correlates with patient survival time and
tumor clonality. Finally, we adapted an additive-effects model from
complex-trait studies to show that the aggregated effect of putative
passengers, including undetected weak drivers, provides significant
additional power (~12% additive variance) for predicting cancerous
phenotypes, beyond identified driver mutations. Furthermore, this
framework allowed us to estimate potential weak-driver mutations in
samples lacking any well-characterized driver alterations.
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i0ucb19
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