ArticleGenetics in medicine : official journal of the American College of Medical Genetics2019
Toward automation of germline variant curation in clinical cancer genetics.
Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Comprehensive evaluation of ACMG/AMP-based variant classification tools.Bioinformatics (Oxford, England) · 2026Article
- Multiple myeloma risk linked to DNA damage response genes.Journal of hematology & oncology · 2026Article
- A machine learning framework for predictive interpretation of variants of uncertain significance in hereditary cancer.Frontiers in systems biology · 2026Article
- Automating ACMG variant classifications with BIAS-2015 v2.1.1: algorithm analysis and benchmark against the FDA-approved eRepo dataset.Genome medicine · 2025Article
- Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- The role of multi-organ cancer predisposition genes in the risk of inherited and histologically diverse gastric cancer.EBioMedicine · 2025Article
- An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.Human genetics · 2025Article
- Germline DNA Damage Repair Variants and Prognosis of Patients with High-Risk or Metastatic Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Design and validation of a clinical whole genome sequencing-based assay for patient screening in a large healthcare system.Frontiers in molecular biosciences · 2025Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Comprehensive genomic profiling of breast cancers characterizes germline-somatic mutation interactions mediating therapeutic vulnerabilities.Cell discovery · 2023Article
- Variant curation and interpretation in hereditary cancer genes: An institutional experience in Latin America.Molecular genetics & genomic medicine · 2023Article
- vaRHC: an R package for semi-automation of variant classification in hereditary cancer genes according to ACMG/AMP and gene-specific ClinGen guidelines.Bioinformatics (Oxford, England) · 2023Article
- NBN Pathogenic Germline Variants are Associated with Pan-Cancer Susceptibility and In Vitro DNA Damage Response Defects.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Assessment of an automated approach for variant interpretation in screening for monogenic disorders: A single-center study.Molecular genetics & genomic medicine · 2022Review
- MARGINAL: An Automatic Classification of Variants inBiomolecules · 2022Article
- Inherited Germline Cancer Susceptibility Gene Variants in Individuals with Non-Muscle-Invasive Bladder Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022Article
- A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization.Scientific reports · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
purposeCancer 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.
methodsPathogenicity 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.
resultsPathoMAN 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.
conclusionAutomation 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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