Development and validation of a new algorithm for the reclassification of genetic variants identified in the BRCA1 and BRCA2 genes.

BRCA1 and BRCA2 sequencing analysis detects variants of uncertain clinical significance in approximately 2 % of patients undergoing clinical diagnostic testing in our laboratory. The reclassification of these variants into either a pathogenic or benign clinical interpretation is critical for improved patient management. We developed a statistical variant reclassification tool based on the premise that probands with disease-causing mutations are expected to have more severe personal and family histories than those having benign variants. The algorithm was validated using simulated variants based on approximately 145,000 probands, as well as 286 BRCA1 and 303 BRCA2 true variants. Positive and negative predictive values of ≥99 % were obtained for each gene. Although the history weighting algorithm was not designed to detect alleles of lower penetrance, analysis of the hypomorphic mutations c.5096G>A (p.Arg1699Gln; BRCA1) and c.7878G>C (p.Trp2626Cys; BRCA2) indicated that the history weighting algorithm is able to identify some lower penetrance alleles. The history weighting algorithm is a powerful tool that accurately assigns actionable clinical classifications to variants of uncertain clinical significance. While being developed for reclassification of BRCA1 and BRCA2 variants, the history weighting algorithm is expected to be applicable to other cancer- and non-cancer-related genes.

Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes.

Missense variants represent a significant proportion of variants identified in clinical genetic testing. In the absence of strong clinical or functional evidence, the American College of Medical Genetics recommends that these findings be classified as variants of uncertain significance (VUS). VUSs may be reclassified to better inform patient care when new evidence is available. It is critical that the methods used for reclassification are robust in order to prevent inappropriate medical management strategies and unnecessary, life-altering surgeries. In an effort to provide evidence for classification, several in silico algorithms have been developed that attempt to predict the functional impact of missense variants through amino acid sequence conservation analysis. We report an analysis comparing internally derived, evidence-based classifications with the results obtained from six commonly used algorithms. We compiled a dataset of 1118 variants in BRCA1, BRCA2, MLH1, and MSH2 previously classified by our laboratory's evidence-based variant classification program. We compared internally derived classifications with those obtained from the following in silico tools: Align-GVGD, CONDEL, Grantham Analysis, MAPP-MMR, PolyPhen-2, and SIFT. Despite being based on similar underlying principles, all algorithms displayed marked divergence in accuracy, specificity, and sensitivity. Overall, accuracy ranged from 58.7 to 90.8% while the Matthews Correlation Coefficient ranged from 0.26-0.65. CONDEL, a weighted average of multiple algorithms, did not perform significantly better than its individual components evaluated here. These results suggest that the in silico algorithms evaluated here do not provide reliable evidence regarding the clinical significance of missense variants in genes associated with hereditary cancer.

Comparison of locus-specific databases for BRCA1 and BRCA2 variants reveals disparity in variant classification within and among databases.

Genetic variants of uncertain clinical significance (VUSs) are a common outcome of clinical genetic testing. Locus-specific variant databases (LSDBs) have been established for numerous disease-associated genes as a research tool for the interpretation of genetic sequence variants to facilitate variant interpretation via aggregated data. If LSDBs are to be used for clinical practice, consistent and transparent criteria regarding the deposition and interpretation of variants are vital, as variant classifications are often used to make important and irreversible clinical decisions. In this study, we performed a retrospective analysis of 2017 consecutive BRCA1 and BRCA2 genetic variants identified from 24,650 consecutive patient samples referred to our laboratory to establish an unbiased dataset representative of the types of variants seen in the US patient population, submitted by clinicians and researchers for BRCA1 and BRCA2 testing. We compared the clinical classifications of these variants among five publicly accessible BRCA1 and BRCA2 variant databases: BIC, ClinVar, HGMD (paid version), LOVD, and the UMD databases. Our results show substantial disparity of variant classifications among publicly accessible databases. Furthermore, it appears that discrepant classifications are not the result of a single outlier but widespread disagreement among databases. This study also shows that databases sometimes favor a clinical classification when current best practice guidelines (ACMG/AMP/CAP) would suggest an uncertain classification. Although LSDBs have been well established for research applications, our results suggest several challenges preclude their wider use in clinical practice.