Assessing Limit of Detection in Clinical Sequencing

Clinical reporting of solid tumor sequencing requires accurate 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, we developed a theoretical model for predicting the expected variant calling sensitivity for a given library complexity and sequencing depth. We found that binomial models were appropriate when assay sensitivity was only limited by library complexity or sequencing depth, but that functional scaling for library complexity was necessary when both library complexity and sequencing depth were co-limiting. We empirically validated this model with sequencing experiments using a series of DNA input amounts and sequencing depths. Based on these findings, we propose a workflow for determining the limiting factors to sensitivity in different assay designs, and present the formulas for these scenarios. 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.