Abstract 5004: Integrative quality control of cancer somatic mutations with CaMutQC

Abstract

Abstract The quality control of cancer somatic mutations is an essential step for eliminating false positive mutations from technical bias, and selecting key mutation candidates plays a crucial role in both downstream translation research and personal medical decisions. Existing tools, with complicated parameters and changeable filtering standards, may not be suitable specifically for cancer somatic mutations. In addition, previous genomic studies usually adopted different filtering criteria for processing raw sequencing datasets, which greatly restricts the efficiency of integrative exploration for cancer genomics. In this study, we presented CaMutQC, a heuristic cancer somatic mutations (CAMs) quality control and filtration package for cancer genomic studies. CaMutQC provides two schemes, including filtration of false positive mutations generated by technical issues, and the screening of candidate cancer somatic mutations from single or multiple VEP-annotated files in VCF format. Specifically, for quality control, instead of directly discarding CAMs that failed to meet the thresholds of parameters collected from classic studies, CaMutQC labels them with customized tags to represent the filtering type, enabling portable subsequent analyses. And a vivid and well-structured filter report is generated after filtration or selection. In addition, we proved by applying an updated mutation QC strategy, which takes the union of CaMutQC-filtered cancer variants from multiple variant callers (eg. MuTect, MuSE and VarScan2) on published datasets that, CaMutQC can not only reduce the false positive CAMS, but also greatly rescue the false negative CAMS from each single caller or the overlap of multiple tools. In summary, in this study, for the first time, we systematically implemented the parameters and criteria of CAMs quality control from published studies into the CaMutQC package, which we believe it would be a valuable tool for cancer genomic research. CaMutQC is implemented in R and is available at https://github.com/likelet/CaMutQC under the GPL-v3 license. Citation Format: Xin Wang, Jian Ren, Qi Zhao. Integrative quality control of cancer somatic mutations with CaMutQC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5004.