Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification
- PMID: 31131967
- PMCID: PMC6772163
- DOI: 10.1002/humu.23818
Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification
Abstract
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.
Keywords: BRCA1; BRCA2; classification; clinical; multifactorial; quantitative; uncertain significance; variant.
© 2019 Wiley Periodicals, Inc.
Conflict of interest statement
The following authors declare conflicts as stated below.
Sandrine M. Caputo: Astra Zeneca contributes financially to the COVAR study.
Laura Cortesi: Astra Zeneca (honoraria), Pfizer (honoraria and advisory role), Amgen (advisory role), and Novartis (advisory role).
Fergus Couch: Steering committee and diagnostics committee for the Astra Zeneca LUCY study.
Anne‐Marie Gerdes: February 2016, advisory Board meeting about BRCA1/2 testing in ovarian cancer, sponsored by Astra Zenica.
Paolo Radice: Scientific coordinator of a course on the classification of BRCA gene allele variants sponsored by Astra Zeneca (Milan, June 2018).
Angela Toss: Lilly (advisory role), Roche (advisory role).
Lisa Wiesmuller is an inventor of a patent on a test system for determining genotoxicities, which is owned by LW.
Ana Vega: October 2015, advisory Board meeting about BRCA1/2 testing in ovarian cancer, sponsored by Astra Zeneca.
All other authors declare that they have no conflict of interests.
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