Abstract 1288: A comprehensive investigation of factors impacting the accuracy of mutation detection using next generation sequencing technology

Abstract

Abstract Next generation sequencing (NGS) allows researchers to survey DNA mutations in tumor cells at very large scales with affordable cost. However, distinguishing tumor cell sub-population mutations from errors due to NGS platform biases, sample preservation effects, assay protocols and suboptimal or incongruous bioinformatics analysis methods is currently complex and difficult. For NGS to be successfully leveraged in a clinical translational manner, increased ability to discriminate between true mutations and artifacts is paramount. To address this urgent unmet clinical need, we systematically investigated somatic mutation events in paired cell lines (breast cancer and normal) with multiple NGS platforms and protocols to fully characterize factors that affect accuracy, specificity and sensitivity of mutation detection. We applied machine learning algorithms to define high confidence mutation calls with data sets from 8 NGS platforms combined with 9 bioinformatic analysis pipelines. With “ground truth” established using multiple orthogonal sequencing platforms, we evaluated the performance of mutation detection under various circumstances, including: fresh cell vs FFPE, amount of DNA, library preparation, sequencing platform, tumor purity, and analysis algorithm. Moreover, we performed whole exome sequencing (WES) and whole genome sequencing (WGS) at 6 sequencing sites in parallel, which allowed us to assess the reproducibility of NGS runs on the same biological samples. By analyzing DNA variant detection recall and precision from data sets derived from various conditions, we identified statistical variance components in overall DNA variant detection framework as well as specific practices that will likely lead to the false identification of variants. By concomitantly examining the effects between sample processing and bioinformatics pipelines, here we recommend best practices for mutation detection using NGS technology and establish quality metrics for cancer studies involving NGS technology. Citation Format: Wenming Xiao, Yongmei Zhao, Somatic Mutation Working Group, SEQC2 Consortium. A comprehensive investigation of factors impacting the accuracy of mutation detection using next generation sequencing technology [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 1288.