Cline, Melissa

Abstract The success of precision cancer prevention and treatment hinges on accurate discrimination between benign and pathogenic germline alleles in cancer susceptibility genes. Variants of uncertain clinical significance (VUS) present a challenge for cancer risk assessment and functional data will be essential to resolve many VUS. To address this, we developed a cloud-based environment to collate, curate, integrate and analyze all published functional data related to BRCA1 and BRCA2 missense VUS. Then, functional evidence criteria for pathogenicity were determined for each assay using the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) classification guidelines and ClinGen criteria. Our approach involved retrieving published articles reporting one or more functional assays of BRCA1/2 missense variants reporting variant impact on various biochemical and cell biological functions. Functional results were recorded using original authors' thresholds and categories, then converted to ordinal variables to harmonize: [0 = no functional impact], [1 = intermediate impact], and [2 = functional impact]. For BRCA1, we updated our previous published integration (Genet Med. 2021;23:306-315) with 14 additional publications, collating results from 53 instances of functional assays reporting functional data on 3,219 unique missense variants. For BRCA2, 28 publications were identified reporting data from 141 instances of functional assays on 4,759 unique missense variants. Utilizing a panel of 542 and 396 known pathogenic and benign reference variants for BRCA1 and BRCA2, respectively, we determined the sensitivity, specificity, and ACMG/AMP odds of pathogenicity for each instance of the assay. The remaining variants were assigned a relevant functional assay code weight for each assay instance. Our study successfully derived ACMG/AMP evidence strength from functional data for 3,040 BRCA1 (94% of 3,219) and 2,704 BRCA2 (57% of 4,759) missense variants. This comprehensive work underscores the utility of functional data in informing classification of BRCA1 and BRCA2 missense VUS. Citation Format: Paulo Lyra, Lucas Dalcolmo, Michael Parsons, Thales Nepomuceno, Samuel Brito, Nam Phuong N. Nguyen, Geise de Oliveira, Joao Paulo da Siva, Laura Caleca, Tanisha Taneja, Chunling Hu, Marcy Richardson, Maria Rossing, Aditi Hazra, Alexandra Martins, Sandrine Caputo, Gael A. Millot, Joanne NgeowYuen Yie, Marcelo A. Carvalho, Melissa Cline, Paolo Radice, Rachael Carlsen, Romy Mesman, Valentina Zampiga, Rehan Villani, Vijay Josef, Shyam Sharan, Kyriaki Michailidou, Amanda B. Spurdle, Fergus Couch, Maaike P.G. Vreeswijk, Alvaro N. Monteiro. Integration of functional data to classify BRCA1/2 missense variants: An ENIGMA project [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7325.

Abstract Precision oncology is the practice of interpreting the clinical significance of observed molecular changes in patient neoplasms, potentially impacting medical decision making and care. This process is labor-intensive and (among other challenges) involves accurately translating between variation representation conventions from one resource to the next. For example, differences in representations of Copy Number Variation (CNV) from genomic regions, cytogenomic bands, or gene features create challenges in knowledge matching due to lack of standards covering all of these modalities of observed variation.The Global Alliance for Genomics and Health (GA4GH; ga4gh.org) is an international collaborative of genomic data sharing initiatives (Driver Projects) developing genomic data sharing standards within a human rights framework. GA4GH recently published the Variation Representation Specification (VRS; pronounced “verse”), a standard for the computational representation of biomolecular variation. VRS is a terminology, schema, and associated conventions for creating uniquely identifiable and federatable representations of molecular variation. VRS has formal data classes well-suited to differentiating between variation on a single molecule (e.g. tandem duplications) from variation measured at a systemic level (e.g. genome-wide copy number variation). In addition to molecular sequence variation, VRS also supports variation on cytogenetic coordinate systems and genes, making it well-suited to representing variation associated with cancer biomarkers.We demonstrate the use of VRS to model reported gene-associated CNVs from the AACR Project GENIE cohort, to aid in the computational discovery of evidence from clinico-genomic knowledgebases with genomic or cytogenomic CNV representations. We highlight the use case of knowledge matching to the Atlas of Genetics and Cytogenetics in Oncology and Haematology (“the Atlas”; atlasgeneticsoncology.org), a cytogenetics resource historically driven by user website navigation. Using VRS search tools we developed for the Variant Interpretation for Cancer Consortium (VICC; cancervariants.org) GA4GH Driver Project, we found that 64% of GENIE samples with reported CNVs matched clinically relevant knowledge in the Atlas. This work was enabled by programmatic search tools leveraging standard VRS object structures, demonstrating how VRS enables collection of real-world evidence across more resources without manual interpretation or custom normalization methods. We conclude with a survey of open-source tools supporting this analysis as well as search of other clinico-genomic knowledgebases with VRS, including CIViC (civicdb.org), BRCA Exchange (brcaexchange.org), and the Molecular Oncology Almanac (moalmanac.org). Citation Format: Matthew Cannon, Kori Kuzma, James Stevenson, Jiachen Liu, Colin O'Sullivan, Bimal P. Chaudhari, Matthew Brush, Robert R. Freimuth, Tristan Nelson, Michael Baudis, Obi L. Griffith, Malachi Griffith, Lawrence Babb, Melissa S. Cline, Xuelu Liu, Brian Walsh, Alex H. Wagner. Introduction of the GA4GH Variation Representation Specification (VRS) and supporting tools for discovery and exchange of clinical genomic and cytogenomic knowledge in cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1177.