A study on patients' selection for BRCA1 and BRCA2 mutations testing by different models in Libyan women with breast cancer

Introduction: The BRCA mutation spectrum of familial breast cancer in Libya remains unknown. Several genetic models developed to predict the probability of BRCA1/2 mutations have not been applied in Libya, where the NCCN criteria are used for highly penetrating breast cancer susceptibility genes. We aimed to predict BRCA1/2 mutation probability in familial breast cancer and genetic testing eligibility using BOADICEA and BRCAPRO models and NCCN criteria. Methods: BRCA1/2 mutations were retrospectively predicted in 62 women with familial breast cancer between 2018 and 2021. Logistic regression, ROC analysis, and area under the curve were used to compare NCCN referral criteria with the BRCAPRO and BOADICEA scores. Results: Thirty-two out of 62 breast cancer patients (51.6%), with a mean age of 43.5 +/- 8 years, were predicted as BRCA mutation carriers by both models. BRCAPRO predicted BRCA1 and BRCA2 mutations in 27.4% and 41.9% of the women, respectively. BOADICEA predicted 8% for BRCA1 and 29% for BRCA2. At least one NCCN criterion was met by 50/62 women (80.6%). Three criteria were statistically significant predictors in BRCAPRO and BOADICEA: breast cancer at age <= 50 years with one or more close blood relatives with breast cancer, breast cancer patient with a close relative of male breast cancer, and triple-negative breast cancer. For the three respective criteria, sensitivity was 0.78, 0.89, and 0.75, specificity was 0.33, 0.39, and 0.22, area under curve was 0.72, 0.75, and 0.76, positive predictive value was 55%, 61%, and 51%, and negative predictive value was 58.5%, 77%, and 45%. Conclusions: Our study highlights that certain aspects of the NCCN criteria demonstrate variations in significance when compared to the BRCAPR and BOADICEA models. For the first time, these models were used to predict BRCA mutations in Libyan women, and our finding indicates that these models are promising for improving genetic testing decision-making.