期刊
NUCLEIC ACIDS RESEARCH
卷 47, 期 15, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkz474
关键词
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资金
- Canadian Cancer Society
- Lotte & John Hecht Memorial Foundation [704762]
- Cancer Research Society [21282]
- Conquer Cancer Foundation of ASCO Career Development Award
- Gattuso-Slaight Personalized Cancer Medicine Fund at Princess Margaret Cancer Centre
- Canada Research Chairs program
- Canada Foundation for Innovation, Leaders Opportunity Fund (CFI) [32383]
- Ontario Ministry of Research and Innovation
- Ontario Research Fund Small Infrastructure Program
- Canadian Cancer Society Research Institute
- Princess Margaret Cancer Foundation
- Joe and Cara Finley Centre for Head & Neck Translational Research
Detection of cancer-associated somatic mutations has broad applications for oncology and precision medicine. However, this becomes challenging when cancer-derived DNA is in low abundance, such as in impure tissue specimens or in circulating cell-free DNA. Next-generation sequencing (NGS) is particularly prone to technical artefacts that can limit the accuracy for calling low-allele-frequency mutations. State-of-the-art methods to improve detection of low-frequency mutations often employ unique molecular identifiers (UMIs) for error suppression; however, these methods are highly inefficient as they depend on redundant sequencing to assemble consensus sequences. Here, we present a novel strategy to enhance the efficiency of UMI-based error suppression by retaining single reads (singletons) that can participate in consensus assembly. This 'Singleton Correction' methodology outperformed other UMI-based strategies in efficiency, leading to greater sensitivity with high specificity in a cell line dilution series. Significant benefits were seen with Singleton Correction at sequencing depths <= 16 000x. We validated the utility and generalizability of this approach in a cohort of >300 individuals whose peripheral blood DNA was subjected to hybrid capture sequencing at similar to 5000x depth. Singleton Correction can be incorporated into existing UMI-based error suppression workflows to boost mutation detection accuracy, thus improving the cost-effectiveness and clinical impact of NGS.
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