COMPUTATIONAL METHODS TO DISCOVER SIGNIFICANT MUTATIONS IN CANCER GENOMES

Cancer is a disease that is mostly driven by somatic mutations accumulating in the genome during an individual’s lifetime. Recent advances in DNA sequencing technology have enabled genome-wide measurements of these mutations in large cohorts of cancer patients. A major challenge in analyzing these data is to distinguish functional "driver" mutations responsible for cancer progression from “passenger”, random 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 pathways, or groups of genes, and that a mutation in any of dozens possible genes might be sufficient to perturb a pathway. While some of the cancer driver pathways are well characterized, many others are only approximately known.

I will describe algorithms for discovering cancer driver pathways using DNA mutation data from large cohorts of cancer samples. 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 identifies subnetworks that have mutations associated with clinical variables, in particular survival time. I will illustrate applications of these algorithms to data from The Cancer Genome Atlas, a project that has characterized the genomes of thousands of samples from dozens of cancer types.