Assessment of blind predictions of the clinical significance of BRCA1 and BRCA2 variants. Issue 9 (23rd August 2019) - Cline, Melissa S.; Babbi, Giulia; Bonache, Sandra; Cao, Yue; Casadio, Rita; de la Cruz, Xavier; Díez, Orland; Gutiérrez‐Enríquez, Sara; Katsonis, Panagiotis; Lai, Carmen; Lichtarge, Olivier; Martelli, Pier L.; et al.
Abstract: Testing for variation in BRCA1 and BRCA2 (commonly referred to as BRCA1/2 ), has emerged as a standard clinical practice and is helping countless women better understand and manage their heritable risk of breast and ovarian cancer. Yet the increased rate of BRCA1/2 testing has led to an increasing number of Variants of Uncertain Significance (VUS), and the rate of VUS discovery currently outpaces the rate of clinical variant interpretation. Computational prediction is a key component of the variant interpretation pipeline. In the CAGI5 ENIGMA Challenge, six prediction teams submitted predictions on 326 newly‐interpreted variants from the ENIGMA Consortium. By evaluating these predictions against the new interpretations, we have gained a number of insights on the state of the art of variant prediction and specific steps to further advance this state of the art. Abstract : Variation in BRCA1 and BRCA2 can greatly increase the risk of breast, ovarian and other cancers. Growing awareness of this fact is leading to an increased rate of genetic testing, which in turn has led to the discovery of thousands of Variants of Uncertain Significance (VUS). The rate of VUS discovery has outstripped the rate of variant interpretation, which has led for a growing need for accurate, high‐throughput methods for variant analysis. In the CAG5 ENIGMA challenge, participants predicted the clinical significance of 326 variants, for which expert interpretations had been completed but not published. Six teams submitted blind predictions on these variants, with fourteen methods collectively. The best performance was achieved by the LEAP methods by Color Genomics, which successfully leveraged private data including an HGMD subscription and an internal library of genetic testing results. This emphasizes that there are still private data that could inform variant prediction. To the extent that such data can be made publicly available, science will benefit, and genetic testing patients by extension.