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Assessing Limit of Detection in Clinical Sequencing

  • Elizabeth R. Starks
    Correspondence
    Address correspondence to Elizabeth R. Starks, M.Sc., Invitae, 1400 16th St., San Francisco, CA 94103; or Aly Karsan, M.D., Canada’s Michael Smith Genome Sciences Centre, BC Cancer, 570 W 7th Ave., Vancouver, BC V5Z 4S6, Canada.
    Affiliations
    Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
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  • Lucas Swanson
    Affiliations
    Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
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  • T. Roderick Docking
    Affiliations
    Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
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  • Ian Bosdet
    Affiliations
    Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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  • Sarah Munro
    Affiliations
    Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
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  • Richard A. Moore
    Affiliations
    Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
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  • Aly Karsan
    Correspondence
    Address correspondence to Elizabeth R. Starks, M.Sc., Invitae, 1400 16th St., San Francisco, CA 94103; or Aly Karsan, M.D., Canada’s Michael Smith Genome Sciences Centre, BC Cancer, 570 W 7th Ave., Vancouver, BC V5Z 4S6, Canada.
    Affiliations
    Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada

    Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Published:January 21, 2021DOI:https://doi.org/10.1016/j.jmoldx.2020.12.010
      Clinical reporting of solid tumor sequencing requires reliable assessment of the accuracy and reproducibility of each assay. Somatic mutation variant allele fractions may be below 10% in many samples due to sample heterogeneity, tumor clonality, and/or sample degradation in fixatives such as formalin. The toolkits available to the clinical sequencing community for correlating assay design parameters with assay sensitivity remain limited, and large-scale empirical assessments are often relied upon due to the lack of clear theoretical grounding. To address this uncertainty, a theoretical model was developed for predicting the expected variant calling sensitivity for a given library complexity and sequencing depth. Binomial models were found to be appropriate when assay sensitivity was only limited by library complexity or sequencing depth, but functional scaling for library complexity was necessary when both library complexity and sequencing depth were co-limiting. This model was empirically validated with sequencing experiments by using a series of DNA input amounts and sequencing depths. Based on these findings, a workflow is proposed for determining the limiting factors to sensitivity in different assay designs, and the formulas for these scenarios are presented. The approach described here provides designers of clinical assays with the methods to theoretically predict assay design outcomes a priori, potentially reducing burden in clinical tumor assay design and validation efforts.
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