Ben Raphael, PhD, Brown University
Advances in DNA sequencing technology have enabled large-scale measurement of the molecular alterations that occur in cancer cells. Translating this information into deeper insights about processes that drive cancer development demands novel computational approaches.
In this talk, I will describe algorithms to address two key problems in cancer genomics. First, I will describe techniques to identify combinations of mutations that perturb cellular signaling and regulatory networks. One algorithm employs a heat diffusion process to identify subnetworks of a genome-scale interaction network that are recurrently altered across samples. A second algorithm finds combinations of mutations that optimize a statistical measure of mutual exclusivity. Next, I will discuss approaches to deconvolve DNA sequencing data from bulk tumor samples and to derive a phylogenetic tree that relates subpopulations of tumor cells within these samples. I will illustrate applications of these approaches to multiple cancer types in The Cancer Genome Atlas (TCGA), including a recent Pan-Cancer study of >3000 samples from 12 cancer types.