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.
Highlights
BRCA1 and BRCA2 variants resulting in abrogated function of the encoded proteins confer a high risk of breast and ovarian cancer (Antoniou et al, 2003; Kuchenbaecker et al, 2017), and have been reported to increase risk of several other cancer types (Breast Cancer Linkage C, 1999; Ford, Easton, Bishop, Narod, & Goldgar, 1994; Moran et al, 2012; Thompson, Easton, & Breast Cancer Linkage C, 2002)
Despite the high prior probability of 0.97 based on bioinformatic prediction, the clinical information included in this study provided sufficient evidence against pathogenicity that the posterior probability fell below 0.95 (0.81 for BRCA2 c.516+1G>T, 0.78 for BRCA2 c.7007+1G>C)
Vreeswijk indicate that BRCA2 c.7007+1G>C has a severe impact on function as measured by failure to complement the lethal cell phenotype, whereas BRCA2 c.516+1G>T does not have a severe impact on function
Summary
BRCA1 and BRCA2 variants resulting in abrogated function of the encoded proteins confer a high risk of breast and ovarian cancer (Antoniou et al, 2003; Kuchenbaecker et al, 2017), and have been reported to increase risk of several other cancer types (Breast Cancer Linkage C, 1999; Ford, Easton, Bishop, Narod, & Goldgar, 1994; Moran et al, 2012; Thompson, Easton, & Breast Cancer Linkage C, 2002). The BRCA1/2 model components include likelihood ratios (LRs) for pathogenicity estimated from clinical data, such as co‐segregation with disease, co‐occurrence with a pathogenic variant in the same gene, reported family history, breast tumor pathology, and more recently, case‐control data (de la Hoya et al, 2016; Easton et al, 2007; Goldgar et al, 2008; Spurdle et al, 2014; Thompson, Easton, & Goldgar, 2003) This information is combined with a prior probability of pathogenicity based on bioinformatic predictions of variant effect on protein sequence or messenger RNA (mRNA) splicing (Tavtigian, Byrnes, Goldgar, & Thomas, 2008; Vallee et al, 2016), probabilities that have been calibrated against clinical information, to produce a quantitative classification applicable across many variant types. We have demonstrated the value of this dataset as a resource for calibrating qualitative information for application to BRCA1/2 variant classification, and deriving BRCA1/ 2‐appropriate rule strengths for several ACMG/AMP evidence codes
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