In the last few years, several studies have focused on the interpretation of unclassified variants (UVs) of BRCA1 and BRCA2 genes. Analysis of UVs through a unique approach is not sufficient to understand their role in the development of tumors. Thus, it is clear that assembling results from different sources (genetic and epidemiological data, histopathological features, and in vitro and in silico analyses) represents a powerful way to classify such variants. Building reliable integrated models for UV classification requires the joining of many working groups to collaborative consortia, allowing data exchange and improvements of methods. This will lead to improvement in the predictivity of gene testing in BRCA1 and BRCA2 and, consequently, to an increase in the number of families that can be correctly classified as linked or unlinked to these genes, allowing more accurate genetic counseling and clinical management.