TY - JOUR
T1 - Optimization of Population Frequency Cutoffs for Filtering Common Germline Polymorphisms from Tumor-Only Next-Generation Sequencing Data
AU - McNulty, Samantha N.
AU - Parikh, Bijal A.
AU - Duncavage, Eric J.
AU - Heusel, Jonathan W.
AU - Pfeifer, John D.
N1 - Funding Information:
Supported by the Department of Pathology and Immunology, Washington University School of Medicine. We thank the Genomics and Pathology Services at Washington University School of Medicine team who performed initial analyses of the clinical cases described in this study, and Michael D. McLellan of the McDonnell Genome Institute (MGI) for facilitating access to The Cancer Genome Atlas (TCGA) data sets generated at MGI.
Publisher Copyright:
© 2019 American Society for Investigative Pathology and the Association for Molecular Pathology
PY - 2019/9
Y1 - 2019/9
N2 - Clinical next-generation sequencing assays are often run on tumor specimens without a matched normal specimen, which complicates the differentiation of germline from somatic variants. In tumor-only testing, population data are often used to infer germline status, though no consensus exists on the exact population frequency (PF) cutoff above which a variant should be considered likely germline. In this study, five population databases plus the Catalog of Somatic Mutations in Cancer were used to demonstrate the impact of changing the PF cutoff on assignment of variants as germline versus somatic. The 1% to 2% PF cutoffs widely used in bioinformatic pipelines resulted in high sensitivity for classification of somatic variants, but unnecessarily reduced sensitivity for germline variants. Using optimized PF cutoffs, the source of variants in The Cancer Genome Atlas (TCGA) data could be predicted with >95% accuracy. Further exploration of four TCGA cancer data sets indicated that the optimal cutoff is influenced by both cancer type and the assay region of interest. Comparing TCGA data to data generated from a clinical, hybridization capture test (approximately 615 kb capture space) showed that PF cutoffs 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.
AB - Clinical next-generation sequencing assays are often run on tumor specimens without a matched normal specimen, which complicates the differentiation of germline from somatic variants. In tumor-only testing, population data are often used to infer germline status, though no consensus exists on the exact population frequency (PF) cutoff above which a variant should be considered likely germline. In this study, five population databases plus the Catalog of Somatic Mutations in Cancer were used to demonstrate the impact of changing the PF cutoff on assignment of variants as germline versus somatic. The 1% to 2% PF cutoffs widely used in bioinformatic pipelines resulted in high sensitivity for classification of somatic variants, but unnecessarily reduced sensitivity for germline variants. Using optimized PF cutoffs, the source of variants in The Cancer Genome Atlas (TCGA) data could be predicted with >95% accuracy. Further exploration of four TCGA cancer data sets indicated that the optimal cutoff is influenced by both cancer type and the assay region of interest. Comparing TCGA data to data generated from a clinical, hybridization capture test (approximately 615 kb capture space) showed that PF cutoffs 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.
UR - http://www.scopus.com/inward/record.url?scp=85070794965&partnerID=8YFLogxK
U2 - 10.1016/j.jmoldx.2019.05.005
DO - 10.1016/j.jmoldx.2019.05.005
M3 - Article
C2 - 31251990
AN - SCOPUS:85070794965
SN - 1525-1578
VL - 21
SP - 903
EP - 912
JO - Journal of Molecular Diagnostics
JF - Journal of Molecular Diagnostics
IS - 5
ER -