Accuracy and Reproducibility of Somatic Point Mutation Calling in Clinical-Type Targeted Sequencing Data
Gareth A Palidwor,
David J Stewart,
Pearl A Campbell,
Theodore J. Perkins
Posted 31 Dec 2019
bioRxiv DOI: 10.1101/2019.12.31.891952 (published DOI: 10.1186/s12920-020-00803-z)
Posted 31 Dec 2019
Background Treating cancer depends in part on identifying the mutations driving each patient’s disease. Many clinical laboratories are adopting high-throughput sequencing for assaying patients’ tumours, applying targeted panels to formalin-fixed paraffin-embedded tumour tissues to detect clinically-relevant mutations. While there have been some benchmarking and best practices studies of this scenario, much variant-calling work focuses on whole-genome or whole-exome studies, with fresh or fresh-frozen tissue. Thus, definitive guidance on best choices for sequencing platforms, sequencing strategies, and variant calling for clinical variant detection is still being developed. Results Because ground truth for clinical specimens is rarely known, we used the well-characterized Coriell cell lines GM12878 and GM12877 to generate data. We prepared samples to mimic as closely as possible clinical biopsies, including formalin fixation and paraffin embedding. We evaluated two well-known targeted sequencing panels, Illumina’s TruSight 170 panel and the Oncomine Focus panel. Sequencing was performed on an Illumina NextSeq500 and an Ion Torrent PGM respectively. We performed multiple biological replicates of each assay, to test reproducibility. Finally, we applied five different public and freely-available somatic single-nucleotide variant (SNV) callers to the data, MuTect2, SAMtools, VarScan2, Pisces and VarDict. Although the TruSight 170 and Oncomine Focus panels cover different amounts of the genome, we did not observe major differences in variant calling success within the regions that each covers. We observed substantial discrepancies between the five variant callers. All had high sensitivity, detecting known SNVs, but highly varying and non-overlapping false positive detections. Harmonizing variant caller parameters or intersecting the results of multiple variant callers reduced disagreements. However, intersecting results from biological replicates was even better at eliminating false positives. Conclusions Reproducibility and accuracy of targeted clinical sequencing results depends less on sequencing platform and panel than on downstream bioinformatics and biological variability. Differences in variant callers’ default parameters are a greater influence on algorithm disagreement than other differences between the algorithms. Contrary to typical clinical practice, we recommend analyzing replicate samples, as this greatly decreases false positive calls.
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