Thursday, January 14, 2021

Re: [EXT] Abstract for MDACC Hogg seminar series


Analyzing the non-coding part of the cancer genome


My talk will focus on leveraging thousands of functional genomics
datasets to annotate the cancer genome and perform data mining to
discover cancer-associated regulators and variations.

First, I will go over the ENCODE annotations related to the cancer
genome. I will introduce our computational efforts to perform
large-scale integration to accurately define distal and proximal
regulatory elements (i.e., the MatchedFilter tool). Then I will show
how this extended gene annotation allows us to place oncogenic
transformations in the context of a broad cell space; here, many
normal-to-tumor transitions move towards a stem-like state, while
oncogene knockdowns show an opposing trend.

Next, I will look at our comprehensive regulatory networks of
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 well-known oncogenic TF MYC.

Third, I will introduce a workflow to prioritize key elements and
variants. I will showcase the application of this prioritization to
somatic burdening, cancer differential expression, and GWAS (the
LARVA, MOAT & uORF tools). Targeted validations of the prioritized
regulators, elements, and variants demonstrate the value of our
annotation resource.

Finally, I will describe how ENCODE 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. I
adapt an additive-effects model from complex-trait studies to show
that putative passengers' aggregated effect, 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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