A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing

Abstract

Cancer is a global health issue that places enormous demands on healthcare systems. Basic research, the development of targeted treatments, and the utility of DNA sequencing in clinical settings, have been significantly improved with the introduction of whole genome sequencing. However the broad applications of this technology come with complications. To date there has been very little standardisation in how data quality is assessed, leading to inconsistencies in analyses and disparate conclusions. Manual checking and complex consensus calling strategies often do not scale to large sample numbers, which leads to procedural bottlenecks. To address this issue, we present a quality control method that integrates point mutations, copy numbers, and other metrics into a single quantitative score. We demonstrate its power on 1,065 whole-genomes from a large-scale pan-cancer cohort, and on multi-region data of two colorectal cancer patients. We highlight how our approach significantly improves the generation of cancer mutation data, providing visualisations for cross-referencing with other analyses. Our approach is fully automated, designed to work downstream of any bioinformatic pipeline, and can automatise tool parameterization paving the way for fast computational assessment of data quality in the era of whole genome sequencing.