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Comprehensive benchmarking of SNV callers for highly admixed tumor data.

Abstract

Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.

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  • Authors+Show Affiliations

    ,

    Molecular Health GmbH, Heidelberg, Germany.

    ,

    Molecular Health GmbH, Heidelberg, Germany.

    Molecular Health GmbH, Heidelberg, Germany.

    Source

    PloS one 12:10 2017 pg e0186175

    MeSH

    Algorithms
    Benchmarking
    Computer Simulation
    Databases, Genetic
    Exome
    Gene Frequency
    Germ Cells
    Humans
    Neoplasms
    Polymorphism, Single Nucleotide
    Reference Standards
    Reproducibility of Results
    Sequence Alignment

    Pub Type(s)

    Journal Article

    Language

    eng

    PubMed ID

    29020110