Nepomuceno, Thales C.

PURPOSE The emergence of large real-world clinical databases and tools to mine electronic medical records has allowed for an unprecedented look at large data sets with clinical and epidemiologic correlates. In clinical cancer genetics, real-world databases allow for the investigation of prevalence and effectiveness of prevention strategies and targeted treatments and for the identification of barriers to better outcomes. However, real-world data sets have inherent biases and problems (eg, selection bias, incomplete data, measurement error) that may hamper adequate analysis and affect statistical power. METHODS Here, we leverage a real-world clinical data set from a large health network for patients with breast cancer tested for variants in BRCA1 and BRCA2 (N = 12,423). We conducted data cleaning and harmonization, cross-referenced with publicly available databases, performed variant reassessment and functional assays, and used functional data to inform a variant's clinical significance applying American College of Medical Geneticists and the Association of Molecular Pathology guidelines. RESULTS In the cohort, White and Black patients were over-represented, whereas non-White Hispanic and Asian patients were under-represented. Incorrect or missing variant designations were the most significant contributor to data loss. While manual curation corrected many incorrect designations, a sizable fraction of patient carriers remained with incorrect or missing variant designations. Despite the large number of patients with clinical significance not reported, original reported clinical significance assessments were accurate. Reassessment of variants in which clinical significance was not reported led to a marked improvement in data quality. CONCLUSION We identify the most common issues with BRCA1 and BRCA2 testing data entry and suggest approaches to minimize data loss and keep interpretation of clinical significance of variants up to date.

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.