IPP Symposium
Identifying Significant Mutations in Large Cohorts of Cancer Genomes
Fabio Vandin, Brown CS Research Faculty (Computational Bio)
Cancer is a disease that is driven by somatic mutations that accumulate in the genome during an individual's lifetime. Recent advances in DNA sequencing technology are enabling genome-wide measurements of these mutations in large cohorts of cancer patients. A major challenge in analyzing this massive amount of data is to distinguish "driver" mutations responsible for cancer from "passenger" mutations not related to the disease. I will present two algorithms we develop to address this challenge. Our algorithms rely on the observation that driver mutations target a relatively small number of pathways, or groups of interacting genes, in the cell. The first algorithm uses a heat diffusion process on graphs and a novel statistical test to identify subnetworks of a large gene interaction network that are mutated in a significant number of cancer samples. The second algorithm requires no prior information about the interactions between genes, and uses a Markov Chain Monte Carlo approach to identify groups of genes whose mutations are mutually exclusive (or nearly so) in a large number of cancer samples. I will illustrate applications of these algorithms to data from The Cancer Genome Atlas, a project that is characterizing the genomes of thousands of samples from dozens of cancer types. This is joint work with Ben Raphael and Eli Upfal.
Fabio Vandin is a Research Assistant Professor in the Department of Computer Science at Brown University. His research focuses on the design of algorithms for cancer genomics, structural proteomics, and biological sequence analysis. He received his PhD in Information Engineering from the University of Padova, Italy. He is a member of the International Society for Computational Biology.
