MutSigCVsyn: Identification of Thirty Synonymous Cancer Drivers

Abstract

Abstract Synonymous mutations, which change only the DNA sequence but not the encoded protein sequence, can affect protein structure and function, mRNA maturation, and mRNA half-lives. The possibility that synonymous mutations can act as cancer drivers has been explored in several recent studies. However, none of these studies control for all three levels (patient, histology, and gene) of mutational heterogeneity that are known to affect the accurate identification of non-synonymous cancer drivers. Here, we create an algorithm, MutSigCVsyn, an adaptation of MutSigCV, to identify synonymous cancer drivers based on a novel non-coding background model that takes into account the mutational heterogeneity across these levels. Examining 2,572 PCAWG cancer whole-genome sequences, MutSigCVsyn identifies 30 novel synonymous drivers that include mutations in promising candidates like BCL-2. By bringing the best practices in non-synonymous driver identification to the analysis of synonymous drivers, these are promising candidates for future experimental study.