Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing - Xiao, Wenming; Ren, Luyao; Chen, Zhong; Fang, Li Tai; Zhao, Yongmei; Lack, Justin; Guan, Meijian; Zhu, Bin; Jaeger, Erich; Kerrigan, Liz; Blomquist, Thomas M; Hung, Tiffany; et al. (Nature biotechnology, 2021)
Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.
Keywords: sekvenování celého genomu; benchmarking; Cell Line; mutace; Neoplasms; Cell Line, Tumor; High-Throughput Nucleotide Sequencing; Sequence Analysis, DNA; sekvenování exomu; sekvenční analýza DNA; reprodukovatelnost výsledků; Reproducibility of Results; lidé; Humans; vysoce účinné nukleotidové sekvenování; Benchmarking; buněčné linie; nádory; Mutation; nádorové buněčné linie; Exome Sequencing; Whole Genome Sequencing