Abstract 2372: Detecting driver genes based on tumor whole-exome sequencing studies

Abstract

Abstract Driver somatic mutations confer a selective growth advantage to tumors while passenger mutations do not. Genes carrying driver mutations are detected in whole-exome sequencing (WES) studies if their non-synonymous mutations are significantly more frequent than their silent mutations, adjusting for the heterogeneous mutation rates across genomic locations, subjects and mutation types. Typical tumor WES studies (∼100-300 tumor samples) lack statistical power, and thus can identify only small numbers of candidate driver genes and possibly miss the well-established ones. The best algorithm to detect driver genes to date is MutSigCV. For each target gene, MutSigCV identifies its “bagel”, a set of genes predicted to have similar background mutation rates to the target gene and use the “bagels” to estimate the background mutation rate. Based on the identified “bagels”, we developed a novel statistical method implementing a series of algorithms to improve the statistical power while maintaining the type-I error rate. First, we developed a statistical framework for testing the selective growth advantage of each mutation type and appropriately integrating evidence of growth advantage across mutation types to achieve robust power. We then extended the algorithm to consider spatial clustering information. This substantially improves the statistical power when non-silent mutations are clustered into a few short regions or nucleotides. We applied this novel method to TCGA melanoma WES including 271 tumor samples. Without modeling spatial clustering information, over 30 candidate driver genes, including BRAF, NRAS, PTEN, TP53, PPP6C and CDKN2A were detected controlling FDR=0.01. Modeling spatial information of somatic mutations detected additional rarely mutated genes, including DISP1 (P=1.3×10-20, involved in cellular proliferation and differentiation that leads to normal development of embryonic structures), IDH1 (P=1.0×10-8, previously identified in relation to gliobalstoma and acute myeloid leukemia), MAP2K1 (P=5×10-18, a member of the mitogen-activated protein kinases that also include BRAF, MEK and other important players in melanoma), INTS8 (P=7.7×10-15, an integrator of small nuclear RNA processing), and STK19 (P=1.2×10-10, encoding a serine/threonine kinase, previously identified in melanoma). Finally, we developed methods to simultaneously analyze multiple cancers to improve the power for genes mutated in more than one cancer type, which will be illustrated by analyzing 12 major cancers in TCGA. Our method will provide an important tool for improving driver gene mutation detection in cancer. Citation Format: Xing Hua, Teresa Maria Landi, Jianxin Shi. Detecting driver genes based on tumor whole-exome sequencing studies. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2372. doi:10.1158/1538-7445.AM2014-2372