Thursday, November 5, 2020

Fwd: CSHL Virtual Meeting: Biological Data Science

Topics in Precision Oncology

My talk will focus on leveraging thousands of cancer genomes and
functional genomics datasets to discover disease-associated regulators
and variations. First, I will go over the ENCODE annotations related
to the cancer genome. I will show these can be recast into a
comprehensive regulatory network of transcription factors and
RNA-binding proteins (TFs and RBPs). I will showcase the value of this
joint regulatory network by highlighting how SUB1, a previously
uncharacterized RBP, drives aberrant tumor expression and amplifies
the effect of the well-known oncogenic TF MYC. Next, I will describe
how ENCODE non-coding annotations can be applied to the comprehensive
PCAWG mutation dataset. The goal is to determine the overall burdening
of various elements in cancer genomes. I will show how this correlates
with patient survival time and tumor clonality. Finally, I adapt 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 allows one to estimate
potential weak-driver mutations in samples lacking any
well-characterized driver alterations.


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