Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

• Autor(a) principal: Parsons, Michael T.
• Data de Publicação: 2019
• Outros Autores: Tudini, Emma, Li, Hongyan, Hahnen, Eric, Wappenschmidt, Barbara, Feliubadaló, Lidia, Aalfs, Cora M., Agata, Simona, Aittomäki, Kristiina, Reis, R. M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/1822/67336
• topic: Alternative Splicing; BRCA1; BRCA2; Computational Biology; Early Detection of Cancer; Female; Genetic Predisposition to Disease; Humans; Likelihood Functions; Male; Multifactorial; Neoplasms; Mutation, Missense; Classification; Clinical; Quantitative; Uncertain significance; Variant; Ciências Médicas::Medicina Básica; Science & Technology

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification.