Abstract LB-280: The landscape of somatic microsatellite indels across cancer: detection and identification of driver events

Abstract

Abstract Microsatellites (MSs) are tracts of variable-length repeats of short DNA motifs that are abundant in the human genome and exhibit high rates of mutations in the form of insertions or deletions of the repeated motif (MS indels). Despite their prevalence, the contribution of somatic MS indels to cancer is largely unexplored due to difficulties in detecting them and assessing their significance. Here, we present a comprehensive analysis of MS indels across 20 tumor types. We characterize the overall MS indel landscape and detect genes with candidate driver MS indel events. We present two novel tools: MSMuTect for accurate detection of somatic MS indels and MSMutSig for identifying candidate cancer genes containing events at higher frequency than expected by chance. We observe high variability of the frequency of MS indels across tumors and demonstrate that the number and pattern of MS indels can accurately distinguish microsatellite stable (MSS) tumors from tumors with microsatellite instability (MSI). Applying MSMutSig across 6,788 tumors from 20 different tumor types identified 7 genes with significant MS indel hotspots: ACVR2A, RNF43, DOCK3, MSH3, ESRP1, PRDM2 and JAK1. In the four genes that have been previously implicated in cancer (ACVR2A, RNF43, JAK1 and MSH3), we identified previously unreported MS indels events. Three of the genes with significant loci - DOCK3, PRDM2 and ESRP1- had not been previously listed as cancer genes. MS indels in DOCK3, a negative regulator of the WNT pathway, were mutually exclusive with mutations in CTNNB1. MS indels in ESRP1, an RNA processing gene, correlated with alternative splicing of FGFR2, an event associated with the epithelial-to-mesenchymal transition. Overall, our comprehensive analysis of somatic MS indels across cancer highlights their importance, particularly in MSI tumors, significantly contributes to the ongoing global efforts to detect cancer genes, and may improve classification of patients into clinically-relevant subgroups. Citation Format: Yosef E. Maruvka, Kent W. Mouw, Rosa Karlic, Rasanna Parasuraman, Atanas Kamburov, Paz Polak, Nicholas J. Haradhvala, Julian M. Hess, Esther Rheinbay, Yehuda Brody, Lior Z. Braunstein, Alan D’Andrea, Michael S. Lawrence, Adam Bass, Andre Bernards, Franziska Michor, Gad Getz. The landscape of somatic microsatellite indels across cancer: detection and identification of driver events [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-280. doi:10.1158/1538-7445.AM2017-LB-280