Kathryn Stahl, Wesley Goar, Kori Kuzma, Alex Wagner
{"title":"72. What's under VarCat's hat: Modeling variant oncogenicity classifications with GA4GH Standards","authors":"Kathryn Stahl, Wesley Goar, Kori Kuzma, Alex Wagner","doi":"10.1016/j.cancergen.2024.08.074","DOIUrl":null,"url":null,"abstract":"<div><div>The Variation Categorizer (VarCat) is a tool for classifying variant oncogenicity for variant-disease pairings in a clinical laboratory workflow. VarCat implements the ClinGen/CGC/VICC oncogenicity guidelines to assist in the classification of a variant's capability for driving cancer formation and growth. VarCat provides an intuitive interface for structured data sharing and produces classification assessments compliant with genomic knowledge standards specified by the Global Alliance for Genomics and Health (GA4GH).</div><div>Here, we present the models, structures, and capabilities provided by VarCat's API and demonstrate its ability to create standardized assessments. VarCat leverages harmonized data from several genomic knowledge sources collated by the VICC MetaKB service. VarCat ensures comprehensive analysis by incorporating standardized gene, variant, therapeutic, disease, and evidence data, and it is driving the development of GA4GH genomic knowledge formats for oncogenicity data. We also describe the suite of normalization microservices used by MetaKB and VarCat to harmonize genomic knowledge concepts. We illustrate how VarCat reduces barriers to interoperable variant-associated evidence through the adoption of the GA4GH Variation Representation Specification (VRS). We also present standardized evidence data using the AMP/ASCO/CAP guidelines for clinical actionability. Overall, our work illustrates how GA4GH Genomic Knowledge Standards drive data interoperability and successful knowledge exchange, ultimately enhancing genetic disease comprehension and advancing patient care.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Page S23"},"PeriodicalIF":1.4000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Genetics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210776224001121","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
The Variation Categorizer (VarCat) is a tool for classifying variant oncogenicity for variant-disease pairings in a clinical laboratory workflow. VarCat implements the ClinGen/CGC/VICC oncogenicity guidelines to assist in the classification of a variant's capability for driving cancer formation and growth. VarCat provides an intuitive interface for structured data sharing and produces classification assessments compliant with genomic knowledge standards specified by the Global Alliance for Genomics and Health (GA4GH).
Here, we present the models, structures, and capabilities provided by VarCat's API and demonstrate its ability to create standardized assessments. VarCat leverages harmonized data from several genomic knowledge sources collated by the VICC MetaKB service. VarCat ensures comprehensive analysis by incorporating standardized gene, variant, therapeutic, disease, and evidence data, and it is driving the development of GA4GH genomic knowledge formats for oncogenicity data. We also describe the suite of normalization microservices used by MetaKB and VarCat to harmonize genomic knowledge concepts. We illustrate how VarCat reduces barriers to interoperable variant-associated evidence through the adoption of the GA4GH Variation Representation Specification (VRS). We also present standardized evidence data using the AMP/ASCO/CAP guidelines for clinical actionability. Overall, our work illustrates how GA4GH Genomic Knowledge Standards drive data interoperability and successful knowledge exchange, ultimately enhancing genetic disease comprehension and advancing patient care.
期刊介绍:
The aim of Cancer Genetics is to publish high quality scientific papers on the cellular, genetic and molecular aspects of cancer, including cancer predisposition and clinical diagnostic applications. Specific areas of interest include descriptions of new chromosomal, molecular or epigenetic alterations in benign and malignant diseases; novel laboratory approaches for identification and characterization of chromosomal rearrangements or genomic alterations in cancer cells; correlation of genetic changes with pathology and clinical presentation; and the molecular genetics of cancer predisposition. To reach a basic science and clinical multidisciplinary audience, we welcome original full-length articles, reviews, meeting summaries, brief reports, and letters to the editor.