[[{"fid":"503","view_mode":"embedded_left","fields":{"format":"embedded_left","field_file_image_alt_text[und][0][value]":"Ben Raphael","field_file_image_title_text[und][0][value]":"","field_file_caption_credit[und][0][value]":"%3Cp%3EBen%20Raphael%3C%2Fp%3E%0A","field_file_caption_credit[und][0][format]":"full_html"},"type":"media","attributes":{"alt":"Ben Raphael","height":367,"width":250,"class":"media-element file-embedded-left"},"link_text":null}]]Understanding the molecular alterations that cause cancer is one of the greatest challenges in biology and medicine. Recent advances in DNA sequencing technology now enable large-scale measurement of mutations in cancer genomes and provide an unprecedented opportunity to address this challenge. However, translating this information into deeper insight about the mutational process that drives cancer demands novel computational approaches.
In this talk, I will describe algorithms that we have developed to address key problems in cancer genomics. First, I will discuss algorithms that deconvolve DNA sequencing data from a single tumor and determine subpopulations of cancer cells harboring different mutations. These algorithms exploit integrality constraints on copy number aberrations and phylogenetic tree constraints that relate subpopulations. Next, I will describe algorithms 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 mutated across samples. A second algorithm finds combinations of mutations that optimize a measure of mutual exclusivity across 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.
Ben Raphael is an Associate Professor in the Department of Computer Science and Director of the Center for Computational Molecular Biology (CCMB) at Brown University. His research focuses on the design of algorithms for genome sequencing and interpretation. Recent interests include structural variation in human and cancer genomes, and network/pathway analysis of genetic variants. He received an S.B. in Mathematics from MIT, a Ph.D. in Mathematics from the University of California, San Diego (UCSD), and completed postdoctoral training in Bioinformatics and Computer Science at UCSD. He is the recipient of an NSF CAREER award, a Career Award from the Burroughs Wellcome Fund, and a Sloan Research Fellowship.