Identifying significant mutations in large cohorts of cancer genomes

Cancer is a disease that is driven by somatic mutations that accumulate in the genome during an individuals 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 that contribute to cancer progression from random, passenger mutations not related to the disease. One of the standard approaches to identify driver mutations is to identify genes that are recurrently mutated in a large cohort of cancer patients. However, the large mutational heterogeneity of cancer reduces the ability of this approach to identify driver mutations. This mutational heterogeneity is due in part to the fact that somatic mutations target pathways, or sets of interacting genes, and that a mutation in dozens of possible genes might be sufficient to perturb a pathway. Standard methods for pathways analysis focus only on known pathways, reducing the ability to discover novel driver genes and sets.