A unique molecular identifier-based and clonal hematopoiesis-aware approach for accurate mutation calling in cell-free DNA assays.

e22515 Background: Given peripheral blood cells (PBCs) matched cell-free DNA (cfDNA), accurate mutation calling in next generation sequencing (NGS)-based assays relies on discriminating artifacts and clonal hematopoiesis mutations from tissue originated somatic mutations. Although clonal hematopoiesis has been considered in previous overall error modeling, it has not been adapted to PBCs without using unique molecular identifiers (UMIs). Moreover, previous studies on background error profiling were mainly based on healthy controls without matched PBC gDNA, which may lead to potential overestimation of the error rates on those sites with clonal hematopoiesis mutations. Additionally, the fraction of tissue cells is also an important influencing factor but is usually ignored. Methods: We performed UMI-assisted capture-based DNA assays on cfDNA samples, matched PBCs, and oral epithelium cells from 150 healthy donors. A site-specific and subtype-specific background error model was first built for PBCs using the SNVs called from PBCs with matched oral epithelium cells to exclude potential clonal hematopoiesis mutations. Then a similar background model was established for cfDNA with the SNVs inferred from cfDNA to exclude clonal hematopoiesis. The SNVs identified in cfDNA and matched PBCs were separately filtered with the cfDNA and PBC background error models. In this study, we used the ultrasensitive liquid biopsy approach to evaluate paired with tissue and blood samples from 56 early-stage NSCLC patients. All samples are sequenced using NGS target-capture panels covering 29 genes. Results: The mutations were detected in 91.1% of tissue and 67.9% were discovered in plasma. The coincidence rate between tissue and plasma of the 56 early-stage NSCLC patients was 66.1%. Conclusions: We have developed a novel method tailored for UMI-assisted capture-based targeted DNA assays, which outperforms currently available methods in terms of modeling background errors and filtering clonal hematopoietic mutations.