Computational Analysis of Mutational Heterogeneity in Cancer
Recent cancer sequencing projects have demonstrated that somatic mutations in tumors are highly heterogeneous. This mutational heterogeneity is apparent both across tumors -- where different individuals with the same tumor type exhibit different combinations of driver mutations -- and within a tumor, where individual cells in a tumor may possess different complements of somatic mutations. We describe algorithms to address both sources of heterogeneity. In the case of inter-tumor heterogeneity, we describe two algorithms to identify driver pathways, groups of genes containing driver mutations, in a large cohort of cancer samples. The first algorithm, HotNet2, uses a heat diffusion process to identify subnetworks of a genome- scale interaction network that are recurrently mutated. The second algorithms, Dendrix and Multi-Dendrix, optimize a measure derived from the statistical property of mutual exclusivity that is satisfied by mutations on driver pathways. In the case of intra-tumor heterogeneity, we present THetA, an algorithm that uses convex optimization techniques to infer tumor composition, including the proportions of normal (non-cancerous) cells and one or more populations of tumor cells, in a single sample. We apply these algorithms to genome/exome sequencing and array copy number data from several cancer types in The Cancer Genome Atlas (TCGA).
Bio: 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 combinatorial and statistical algorithms for the interpretation of genomes. Particular areas of emphasis include analysis of structural variation in human and cancer genomes, and network/pathway analysis of genetic variants. Dr. Raphael 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 a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, an Alfred P. Sloan Research Fellowship, and a National Science Foundation CAREER award.