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.
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.
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.
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).
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We thank Sabine Topka, Semanti Mukherjee, Maria Carlo, and Zoe Steinsnyder for helpful suggestions to improve the manuscript. Research reported in this paper was supported by National Cancer Institute of the National Institutes of Health under award numbers R21CA029533, P50CA221745, as well as Cycle for Survival, the Breast Cancer Research Foundation, and the V Foundation for Cancer Research. It is also supported by the Cancer Center core grant P30CA008748 and the Robert and Kate Niehaus Center for Inherited Cancer Genomics. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funding agencies.
L.Z. received compensation from Future Technology Research LLC (seminar on precision medicine), Roche Diagnostics Asia Pacific, BGI, Illumina (speaking activities at conferences/workshop). L.Z.'s family member has a leadership position and ownership interest of Shanghai Genome Center. Y.K. was a past employee of Bioreference Laboratories, a subsidiary of OPKO Health, with employment ending in January 2016. K.C. declares institutional support for therapeutic clinical trial from AstraZeneca and Syndax Pharmaceuticals outside the submitted work. Z.S. declares her immediate family member works at the department of Ophthalmology at MSKCC. She also holds consulting/advisory roles with Allergan, Adverum Biotechnologies, Alimera Sciences, Biomarin, Fortress Biotech, Genentech, Novartis, Optos, Regeneron, Regenxbio, Spark Therapeutics. M.R. declares grants, personal fees and nonfinancial support from AstraZeneca, personal fees from McKesson, grants and personal fees from Pfizer, nonfinancial support from Myriad, nonfinancial support from Invitae, grants from AbbVie, grants from Tesar, grants from Medivation outside the submitted work. V.J. and K.O. declare that they hold patent on the “Diagnosis and Treatment of ERCC3 mutant cancer” PCT/US18/22588. The other authors declare no conflicts of interest.
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