In Silico Prediction of BRCA1 and BRCA2 Variants with Conflicting Clinical Interpretation in a Cohort of Breast Cancer Patients

Germline <i>BRCA1/2</i> alteration has been linked to an increased risk of hereditary breast and ovarian cancer syndromes. As a result, genetic testing, based on NGS, allows us to identify a high number of variants of uncertain significance (VUS) or conflicting interpretation of pathogenicity (CIP) variants. The identification of CIP/VUS is often considered inconclusive and clinically not actionable for the patients' and unaffected carriers' management. In this context, their assessment and classification remain a significant challenge. The aim of the study was to investigate whether the <i>in silico</i> prediction tools (PolyPhen-2, SIFT, Mutation Taster and PROVEAN) could predict the potential clinical impact and significance of <i>BRCA1/2</i> CIP/VUS alterations, eventually impacting the clinical management of Breast Cancer subjects. In a cohort of 860 BC patients, 10.6% harbored <i>BRCA1</i> or <i>BRCA2</i> CIP/VUS alterations, mostly observed in <i>BRCA2</i> sequences (85%). Among them, forty-two out of fifty-five alterations were predicted as damaging, with at least one <i>in silico</i> that used tools. Prediction agreement of the four tools was achieved in 45.5% of patients. Moreover, the highest consensus was obtained in twelve out of forty-two (28.6%) mutations by considering three out of four <i>in silico</i> algorithms. The use of prediction tools may help to identify variants with a potentially damaging effect. The lack of substantial agreement between the different algorithms suggests that the bioinformatic approaches should be combined with the personal and family history of the cancer patients.