Accurately identifying low-allelic fraction variants in single samples with next-generation sequencing: applications in tumor subclone resolution.

Current methods for resolving genetically distinct subclones in tumor samples require somatic mutations to be clustered by allelic frequencies, which are determined by applying a variant calling program to next-generation sequencing data. Such programs were developed to accurately distinguish true polymorphisms and somatic mutations from the artifactual nonreference alleles introduced during library preparation and sequencing. However, numerous variant callers exist with no clear indication of the best performer for subclonal analysis, in which the accuracy of the assigned variant frequency is as important as correctly indicating whether the variant is present or not. Furthermore, sequencing depth (the number of times that a genomic position is sequenced) affects the ability to detect low-allelic fraction variants and accurately assign their allele frequencies. We created two synthetic sequencing datasets, and sequenced real KRAS amplicons, with variants spiked in at specific ratios, to assess which caller performs best in terms of both variant detection and assignment of allelic frequencies. We also assessed the sequencing depths required to detect low-allelic fraction variants. We found that VarScan2 performed best overall with sequencing depths of 100×, 250×, 500×, and 1,000× required to accurately identify variants present at 10%, 5%, 2.5%, and 1%, respectively.