Science / Technology - Lecture/Discussion - WPI Only
Friday, February 1, 2013
11:00 AM-12:00 PM
Worcester, MA 01609
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Algorithms for identifying significant mutations in cancer genomes
BCB Faculty Candidate
Fabio Vandin, Ph.D.
Abstract: 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 data is to distinguish functional "driver" mutations responsible for cancer progression from "passenger" mutations not related to the disease. Recent cancer sequencing studies have shown that somatic mutations are distributed over a large number of genes. This mutational heterogeneity is due in part to the fact that somatic mutations target cellular signaling and regulatory pathways, or groups of genes, and that a mutation in dozens of possible genes might be sufficient to perturb a pathway. While some of these driver pathways are well characterized, many others are only approximately known.
I will describe two efficient algorithms for the problem of discovering driver pathways using data from large cohorts of cancer samples. The first algorithm identifies subnetworks of a protein-protein interaction network that are mutated in a significant number of samples, and uses a heat diffusion process on graphs and a novel statistical test to identify significant subnetworks. The second algorithm requires no prior information about sets of genes or 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.
Suggested Audiences: College, Adult
Last Modified: January 30, 2013 at 10:55 AM