A computational method to classify variants of uncertain significance using functional assay data with application to BRCA1

Background: Besides revealing cancer predisposition variants or the absence of any changes, genetic testing for cancer predisposition genes can also identify variants of uncertain clinical significance (VUS). Classifying VUSs is a pressing problem, as ever more patients seek genetic testing for disease syndromes and receive noninformative results from those tests. In cases such as the breast and ovarian cancer syndrome in which prophylactic options can be severe and life changing, having information on the disease relevance of the VUS that a patient harbors can be critical. Methods: We describe a computational approach for inferring the disease relevance of VUSs in disease genes from data derived from an in vitro functional assay. It is based on a Bayesian hierarchical model that accounts for sources of experimental heterogeneity. Results: The functional data correlate well with the pathogenicity of BRCA1 BRCT VUSs, thus providing evidence regarding pathogenicity when family and genetic data are absent or uninformative. Conclusions: We show the utility of the model by using it to classify 76 VUSs located in the BRCT region of BRCA1. The approach is both sensitive and specific when evaluated on variants previously classified using independent sources of data. Although the functional data are very informative, they will need to be combined with other forms of data to meet the more stringent requirements of clinical application. Impact: Our work will lead to improved classification of VUSs and will aid in the clinical decision making of their carriers.