Optimization of Population Frequency Cut-offs for Filtering Common Germline Polymorphisms from Tumor-Only Next-Generation Sequencing Data

In this study, five population databases plus the Catalog of Somatic Mutations in Cancer was employed to demonstrate the impact of changing the PF cut-off on assignment of variants as germline versus somatic. The 1% to 2% PF cut-offs widely used in bioinformatic pipelines result in high sensitivity for classification of somatic variants, but unnecessarily reduced sensitivity for germline variants. Using optimized PF cut-offs, the source of variants in TCGA data could be predicted with>95% accuracy. Further exploration of four TCGA cancer datasets indicated that the optimal cut-off is influenced by both cancer type and the assay region of interest. Comparing TCGA data to data generated from a clinical, hybridization capture test (∼615kb capture space) showed that PF cut-offs may not be transferable between assays, even when the gene set is held constant. Thus, filtering approaches need to be carefully designed and optimized, and should be assay-specific to support tumor-only testing until tumor-normal testing becomes routine in the clinical setting.
Source: The Journal of Molecular Diagnostics - Category: Pathology Source Type: research