PT - JOURNAL ARTICLE AU - Amanda Ewart Toland AU - Paul R Andreassen TI - DNA repair-related functional assays for the classification of BRCA1 and BRCA2 variants: a critical review and needs assessment AID - 10.1136/jmedgenet-2017-104707 DP - 2017 Nov 01 TA - Journal of Medical Genetics PG - 721--731 VI - 54 IP - 11 4099 - http://jmg.bmj.com/content/54/11/721.short 4100 - http://jmg.bmj.com/content/54/11/721.full SO - J Med Genet2017 Nov 01; 54 AB - Mutation of BRCA1 and BRCA2 is the most common cause of inherited breast and ovarian cancer. Genetic screens to detect carriers of variants can aid in cancer prevention by identifying individuals with a greater cancer risk and can potentially be used to predict the responsiveness of tumours to therapy. Frequently, classification cannot be performed based on traditional approaches such as segregation analyses, including for many missense variants, which are therefore referred to as variants of uncertain significance (VUS). Functional assays provide an important alternative for classification of BRCA1 and BRCA2 VUS. As reviewed here, both of these tumour suppressors promote the maintenance of genome stability via homologous recombination. Thus, related assays may be particularly relevant to cancer risk. Progress in implementing functional assays to assess missense variants of BRCA1 and BRCA2 is considered here, along with current limitations and the path to more impactful assay systems. While functional assays have been developed to independently evaluate BRCA1 and BRCA2 VUS, high-throughput assays with sufficient sensitivity to characterise the large number of identified variants are lacking. Additionally, because of relatively low conservation of certain domains of BRCA1, and of BRCA2, between humans and rodents, heterologous expression in rodent cells may have limited reliability or capacity to assess variants present throughout either protein. Moving forward, it will be important to perform assays in human cell lines with relevance to particular tumour types, and to strengthen risk predictions based on multifactorial statistical analyses that also include available data on cosegregation and tumour pathology.