Abstract A2-42: Identifying clinically important somatic mutations through a knowledge-based approach

Abstract

Abstract Large-scale tumor sequencing projects, like The Cancer Genome Atlas (TCGA), have implicated thousands of somatic mutations in cancer. These initiatives have incentivized many improvements in somatic variant detection. However, we have observed that important pathogenic variants are often missed due to stringent filtering, tumor heterogeneity, tumor contamination of normal, low tumor purity, alignment challenges, and other issues. These idiosyncrasies can impede variant detection algorithms from reliably calling even the most clinically relevant variants. To rescue this missed variation we devised a knowledge based variant identification strategy. We mined the literature and other variant databases for pathogenic variation and assembled them into an integrated Database of Curated Mutations(DoCM - www.docm.info). The DoCM contains 488 variants across 63 genes implicated in 34 cancer types. We developed an algorithm to identify any pathogenic variant signal, for all variants in the DoCM, in aligned sequence data. As a proof of principle, we applied this approach to four cancer types sequenced by TCGA: acute myeloid leukemia (AML), breast cancer, ovarian carcinoma, and uterine corpus endometrial carcinoma. Obvious sequencing and alignment errors, like variants in homopolymer runs, were excluded from subsequent analysis by manual review. Across these four TCGA projects, which includes 1,840 individuals, 1,757 clinically relevant variants were identified, 1,223 of which had not been previously reported in TCGA studies. To validate this approach, custom capture probes were designed for all of the DoCM variants, new libraries constructed and deep sequencing performed on 96 tumor and matched normal samples from the AML and breast cancer TCGA projects. Following this strategy, we were able to confirm the rescue of clinically relevant somatic mutations that were missed in the original TCGA analysis. We propose a knowledge-driven variant detection approach be considered as standard practice to avoid false-negative calls of events likely to be clinically relevant. Citation Format: Benjamin J. Ainscough, Malachi Griffith, Jason Kunisaki, Adam Coffman, Joshua F. McMichael, James M. Eldred, Jason R. Walker, Robert S. Fulton, Richard K. Wilson, Obi L. Griffith, Elaine R. Mardis. Identifying clinically important somatic mutations through a knowledge-based approach. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-42.