Abstract 3971: DrGaP: A powerful tool for identifying driver genes and pathways in cancer sequencing studies

Abstract

Abstract Recent next-generation sequencing experiments have brought success in identifying cancer genes that were highly frequently mutated in tumors. As genomic sequencing experiments continue to identify large numbers of novel cancer mutations, one big challenge will be distinguishing driver mutations from passenger mutations. Driver mutations are the ones that have a role in oncogenesis or in the cancer phenotype, whereas passenger mutations are co-traveler that accumulate through DNA replication but are irrelevant to tumor development. From statistical point of view, driver genes are defined as those for which the non-silent mutation rate is significantly greater than a background (or passenger) mutation rate estimated from silent mutations. Statistical methods are now actively being developed to attempt to assess functional significance of a mutated gene in cancer sequencing studies. However, embedding biological knowledge on mutational process in tumors into statistical models is not trivial. These biological considerations include length of protein coding regions, gene structure, transcript isoforms, difference in mutation types, variation in background mutation rates, redundancy of the genetic code, multiple mutations in one gene, and functional consequence of different tumor types. They have not yet been fully addressed by current methods and tools for identifying driver genes. Driver mutations could be either common or rare among tumors. Identification of rare driver mutations may pose a potential challenge. In addition, mutation in one member of a collection of functionally related genes may result in the same net effect, and/or mutations in certain genes may be observed less frequently if they play functional roles in later stages of tumor development. At the pathway level, the frequency of rare driver mutations is accumulated in that pathway which warrants sufficient detection power. Thus, there is an increasing interest in the identification of driver pathways in tumor formation and progression. To meet these challenges, we developed a tool-DrGaP for identify driver genes and pathways in cancer sequencing studies. We introduced the Poisson processes to model the random nature of somatic mutation under no selection pressure. The new model can handle the identification of both driver genes and pathways or any gene sets in the same statistical framework. The new tool provides several significant improvements and increased power over current existing methods through simulations. To illustrate its utility, we applied the DrGaP to analysis of a recent lung cancer sequencing study. The DrGaP identified a few novel driver genes that were missed by previous methods, demonstrating its high accuracy and sensitivity. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3971. doi:1538-7445.AM2012-3971