Variants of uncertain clinical significance in hereditary breast and ovarian cancer genes: best practices in functional analysis for clinical annotation

Since the identification and cloning of BRCA1 in 1994,1 and shortly thereafter of BRCA2 ,2 genetic tests of germline DNA to identify pathogenic variants in genes linked to hereditary breast and ovarian cancer (HBOC) have become mainstream.3 These tests are critical to identify women at increased risk relative to the general population. Women at moderate risk (2≤relative risk (RR) 4), including those with BRCA1 and BRCA2 pathogenic variants, may also benefit from preventive surgery. Germline mutation testing is also becoming increasingly relevant in the cancer treatment setting because carriers of pathogenic variants in BRCA1/2 may benefit from poly-ADP ribose polymerase (PARP) inhibitors.4 5 Importantly, genetic tests can identify individuals in HBOC families who do not carry the relevant predisposing allele and are not at elevated risk of cancer.6 A significant fraction of documented variants in BRCA1 and BRCA2 are considered variants of uncertain clinical significance (VUS), for which cancer association has not been assessed or could not be determined due to insufficient information (table 1). In ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), a clinically oriented database, currently ~37% of BRCA1 and ~45% of BRCA2 unique variants recorded are VUS. Thus, there is a critical need to classify variants according to their pathogenicity. View this table: Table 1 Fraction of VUS in BRCA1 and BRCA2 Over the past decade, functional assays have emerged that can be included as a source of evidence to classify variants according to their pathogenicity, with the potential to greatly accelerate classification.7 Here, we discuss several technical and conceptual aspects relevant for the use of functional assays in the classification of variants. We present best practice recommendations to improve annotation quality and accuracy, and to provide a basis for the comparison and integration of functional data …