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 ≤16 000×. 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 ∼5000× 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.
Highlights
High-throughput sequencing technologies have revolutionized genetic and biomedical research by uncovering alterations responsible for the development of disease
With two or more redundant reads required to construct a consensus sequence, only two-thirds of all reads in LargeMid qualified for traditional error suppression; this corresponded to a 25% single-strand consensus sequence (SSCS) efficiency rate and 2% duplex consensus sequences (DCSs) efficiency rate (Figure 1B)
Only 15% of the expected DCSs were observed in LargeMid, and the more deeply sequenced libraries had only modest gains in DCS recovery (SmallDeep and [8,9,10])
Summary
High-throughput sequencing technologies have revolutionized genetic and biomedical research by uncovering alterations responsible for the development of disease. Considerable progress has been made toward germline and somatic variant detection, identification of variants at lower allele frequencies remains hindered by sequencing errors and technical artefacts. This has numerous implications in oncology, in liquid biopsy applications, where tumour DNA fragments may be present at frequencies
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