Abstract 1936: CoMEt: A statistical approach to identify combinations of mutually exclusive alterations in cancer

Abstract

Abstract Identifying driver mutations in cancer genomes is a significant challenge due to the mutational heterogeneity of tumors: different combinations of somatic mutations drive different tumors, even those of the same cancer type. This mutational heterogeneity arises because driver mutations target genes in signaling and regulatory pathways, each of which can be perturbed in numerous ways. We introduce CoMEt, an algorithm to identify combinations of candidate driver mutations de novo, without any prior biological knowledge (e.g. pathways or protein interactions). CoMEt searches for combinations of mutations that exhibit mutual exclusivity, a pattern expected for mutations in pathways. CoMEt uses an exact statistical test for mutual exclusivity that is less biased toward high frequency alterations than previous approaches and more sensitive in detecting combinations of lower frequency alterations. We compute the exact test using a novel tail enumeration procedure and also derive a binomial approximation. CoMEt simultaneously identifies collections of one or more combinations of mutually exclusive alterations, consistent with the observation of multiple hallmarks of cancer, and also performs simultaneous analysis of subtype-specific mutations. Finally, CoMEt uses an MCMC algorithm to sample from collections in proportion to their significance, summarizing the distribution in a marginal probability graph. We show that CoMEt outperforms other mutual exclusivity approaches on simulated and real data. We apply CoMEt to hundreds of samples from four different TCGA cancer types: gastric cancer (STAD), glioblastoma (GBM) and acute myeloid leukemia (AML), and breast cancer (BRCA). We identify multiple mutually exclusive sets within each cancer type. These include the RTK/RAS pathway in gastric cancer, the Rb and p53 signaling pathways in glioblastoma, and a collection containing multiple kinases, including FLT3, as well as the RAS genes in AML. In addition, we analyze subtype-specific mutations using CoMEt by encoding the subtype of each sample and computing exclusivity between and within subtypes. We apply this approach using four gene expression subtypes of breast cancer and identify several pathways that are enriched for mutations in specific subtypes including the PI(3)K/AKT signaling pathway in the Luminal A subtype. Many of these overlap known pathways, but others reveal novel putative cancer genes. These findings provide testable hypotheses for experimental validation. Citation Format: Hsin-Ta Wu, Mark D.M. Leiserson, Fabio Vandin, Benjamin J. Raphael. CoMEt: A statistical approach to identify combinations of mutually exclusive alterations in cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1936. doi:10.1158/1538-7445.AM2015-1936