Abstract
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 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.
Original language | English |
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Article number | 87 |
Number of pages | 11 |
Journal | Nucleic Acids Research |
Volume | 47 |
Issue number | 15 |
DOIs | |
Publication status | Published - 5 Sept 2019 |
Funding
We thank the Genome Technologies and Genome Sequence Informatics teams at Ontario Institute for Cancer Research, the staff of the Princess Margaret Genomics Centre (www.pmgenomics.ca, Troy Ketela, Neil Winegarden, Julissa Tsao and Nick Khuu), and the Bioinformatics and High-Performance Computing Core (Carl Virtanen and Zhibin Lu) for their expertise in generating the sequencing data used in this study. We thank Marco Di Grappa for technical support and evaluation of the software. The authors gratefully acknowledge the support from the Princess Margaret Cancer Foundation. Princess Margaret Cancer Foundation, Joe and Cara Finley Centre for Head & Neck Translational Research, a Canadian Cancer Society grant generously supported by the Lotte & John Hecht Memorial Foundation [704762]; Cancer Research Society [21282]; Conquer Cancer Foundation of ASCO Career Development Award (SVB). Any opinions, findings, and conclusions expressed in this material are those of the author(s) and do not necessarily reflect those of the American Society of Clinical Oncology or the Conquer Cancer Foundation. S.V.B. and T.J.P. are supported by the Gattuso-Slaight Personalized Cancer Medicine Fund at Princess Margaret Cancer Centre. Additional grant support to TJP from the 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. Funding for open access charge: Canadian Cancer Society Research Institute.
All Science Journal Classification (ASJC) codes
- Genetics