Toward automation of germline variant curation in clinical cancer genetics.

TitleToward automation of germline variant curation in clinical cancer genetics.
Publication TypeJournal Article
Year of Publication2019
AuthorsRavichandran, V, Shameer, Z, Kemel, Y, Walsh, M, Cadoo, K, Lipkin, S, Mandelker, D, Zhang, L, Stadler, Z, Robson, M, Offit, K, Vijai, J
JournalGenet Med
Volume21
Issue9
Pagination2116-2125
Date Published2019 09
ISSN1530-0366
KeywordsAlgorithms, Automation, Computational Biology, Genetic Testing, Genetic Variation, Genome, Human, Genomics, Germ Cells, High-Throughput Nucleotide Sequencing, Humans, Mutation, Neoplasms, Software
Abstract

PURPOSE: Cancer care professionals are confronted with interpreting results from multiplexed gene sequencing of patients at hereditary risk for cancer. Assessments for variant classification now require orthogonal data searches and aggregation of multiple lines of evidence from diverse resources. The clinical genetics community needs a fast algorithm that automates American College of Medical Genetics and Genomics (ACMG) based variant classification and provides uniform results.

METHODS: Pathogenicity of Mutation Analyzer (PathoMAN) automates germline genomic variant curation from clinical sequencing based on ACMG guidelines. PathoMAN aggregates multiple tracks of genomic, protein, and disease specific information from public sources. We compared expertly curated variant data from clinical laboratories to assess performance.

RESULTS: PathoMAN achieved a high overall concordance of 94.4% for pathogenic and 81.1% for benign variants. We observed negligible discordance (0.3% pathogenic, 0% benign) when contrasted against expert curated variants. Some loss of resolution (5.3% pathogenic, 18.9% benign) and gain of resolution (1.6% pathogenic, 3.8% benign) were also observed.

CONCLUSION: Automation of variant curation enables unbiased, fast, efficient delivery of results in both clinical and laboratory research. We highlight the advantages and weaknesses related to the programmable automation of variant classification. PathoMAN will aid in rapid variant classification by generating robust models using a knowledgebase of diverse genetic data ( https://pathoman.mskcc.org).

DOI10.1038/s41436-019-0463-8
Alternate JournalGenet. Med.
PubMed ID30787465
PubMed Central IDPMC6703969
Grant ListP30 CA008748 / CA / NCI NIH HHS / United States
P50 CA221745 / CA / NCI NIH HHS / United States
R21 CA209533 / CA / NCI NIH HHS / United States