Abstract B11: The MuSiC2 system for discovery and visualization of coding and noncoding cancer drivers

Abstract

Abstract The MuSiC system has been widely used over the last 5 years by the scientific community for analyzing genomic data across the cancer spectrum. However, the desired scope of analysis has now increased dramatically, due to advances in instrumentation and experimental methods, vastly expanding sample availability, and the general appreciation of many newfound subtleties of the cancer initiation and progression processes themselves. We have extended MuSiC in several ways to address both new requirements and several long-standing issues that have not been manageable with any current analysis system. Here, we focus on three aspects. First, to answer the growing need for noncoding analysis, MuSiC2 extends the common idea of a “significantly mutated gene” model to one of “significantly mutated regions,” including the attendant tasks of interfacing to ENCODE, calculating region-appropriate background mutation rates (BMRs) for hypothesis testing, etc. Second, to address the statistical artifacts realized with most analysis software, we have designed two new filters: one for longer-than-average genes and another for cancer types having elevated BMRs, both scenarios of which typically show appreciably inflated false-positive rates because of upwardly biased passenger mutation counts. Finally, we describe a number of new visualization capabilities designed to give the investigator new interpretive capabilities, including for complex genomic rearrangements that may stem from viral integration. Taken together with improved programmatic efficiencies, the new MuSiC2 system should be useful for discovering and visualizing coding and noncoding cancer drivers in pediatric and adult cancers. Citation Format: Matthew A. Wyczalkowski, Matthew H. Bailey, Carolyn Lou, Felix Hu, Justin Y. Chen, Prag Batra, Michael D. McLellan, Li Ding, Michael C. Wendl. The MuSiC2 system for discovery and visualization of coding and noncoding cancer drivers [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr B11.