Abstract 395: Identifying the functional drivers of noncoding somatic mutations in ovarian cancer

Abstract

Abstract Epithelial ovarian cancer (OC) is a heterogeneous disease that has been stratified into different histologic subtypes: high-grade serous (HGSOC), clear cell (CCOC), endometrioid (EnOC) and mucinous (MOC). HGSOC is the most common, but every histotype is characterized by largely distinct germline genetics, somatic alterations and clinical biomarkers. Recently, whole-genome sequencing (WGS) studies have catalogued genome-wide somatic variation for most OC histotypes. These data demonstrate that OC histotypes harbor thousands of noncoding somatic mutations and our next major challenge is to distinguish the few important noncoding somatic drivers from the thousands of passenger mutations. We hypothesized that driver noncoding somatic mutations impact disease development and progression through altering the sequence of regulatory elements (REs), such as enhancers and promoters, eventually resulting in perturbation of the expression of target genes. To systematically address this hypothesis, we established genome-wide H3K27ac epigenomic profiles, annotating active REs for the different ovarian cancer histotypes using chromatin immunoprecipitation sequencing (ChIP-seq) in 20 fresh frozen primary OC tissue samples—five tumors for each major histotype. In parallel, we performed transcriptional profiling using RNA sequencing (RNA-seq). Together, these two datasets enabled us to evaluate epigenetic alterations and the transcriptome. We identified histotype-specific active REs, and common active REs across all histotypes. We used the RNA-seq data to assess the effect of the histotype-specific REs in gene expression, and to find target genes of cis-REs and novel histotype-specific biomarkers. Next, we integrated these unique profiles with WGS data from 232 OCs (169 HGSOCs, 35 CCOCs and 28 EnOCs). The number of somatic single-nucleotide variants per sample range from 481 to 40,764 (mean=7199, sd = 5751). Of these, 9.4% were noncoding and overlapped active REs in OC. Using a Poisson binomial distribution, we tested the significance of the observed number of mutated samples for any given active RE. Importantly, the method adjusted for interpatient mutation rate heterogeneity. Using the Benjamini-Hochberg procedure for p-value correction, we identified several significantly mutated active REs, including the promoters of POLR3E and WDR74. In conclusion, we have used an integrative method to identify functional, driver noncoding somatic mutations for ovarian cancer based on their interaction with disease-specific regulatory elements, and their putative target genes. This represents a powerful way to distinguish important noncoding somatic drivers from a much larger number of passenger mutations that accumulate during tumor development. The approach can be applied to other cancer types if sufficient depth and quality of genetic, transcriptomic and epigenomic datasets are available. Citation Format: Rosario I. Corona, Ji-Heui Seo, Dennis J. Hazelett, Xianzhi Lin, Paulette Y. Mhawech-Fauceglia, Jenny Lester, Sohrab Shah, David G. Huntsman, Beth Y. Karlan, Benjamin P. Berman, Matthew L. Freedman, Simon A. Gayther, Kate Lawrenson. Identifying the functional drivers of noncoding somatic mutations in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 395.