Comprehensive Proteomic Profiling-derived Immunohistochemistry-based Prediction Models for BRCA1 and BRCA2 Germline Mutation-related Breast Carcinomas

Abstract

Heredity, mostly due to BRCA germline mutations, is involved in 5% to 10% of all breast cancer cases. Potential BRCA germline mutation carriers may be missed following the current eligibility criteria for BRCA genetic testing. The purpose of this study was to, therefore, develop an immunohistochemistry-based model to predict likelihood of underlying BRCA1 and BRCA2 germline mutations in unselected female breast cancer patients. The study group consisted of 100 BRCA1-related, 46 BRCA2-related, and 94 sporadic breast carcinomas. Tumor expression of 44 proteins involved in (BRCA-related) breast carcinogenesis was assessed by immunohistochemistry. A prediction model for BRCA-related versus non-BRCA-related breast cancer was developed using Lasso logistic regression analysis with cross-validation. The model was assessed for its discriminative value and clinical usefulness. The optimal prediction model included 14 predictors (age, cyclinD1, ERα, ERβ, FGFR2, FGFR3, FGFR4, GLUT1, IGFR, Ki67, mitotic activity index, MLH1, p120, and TOP2A), showed excellent discriminative performance (area under the receiving operating characteristic curve=0.943; 95% confidence interval=0.909-0.978), and reasonable calibration. To enhance possible implementation, we developed an alternative model only considering more widely available immunostains. This model included 15 predictors (age, BCL2, CK5/6, CK8/18, cyclinD1, E-cadherin, ERα, HER2, Ki67, mitotic activity index , MLH1, p16, PMS2, PR, and vimentin), and still showed very good discriminative performance (area under the receiving operating characteristic curve=0.853; 95% confidence interval=0.795-0.911). We present a well-applicable and accurate tool to predict which breast cancer patients may have an underlying BRCA germline mutation, largely consisting of immunohistochemical markers independent of clinical characteristics. This may improve identification of potential BRCA germline mutation carriers and optimize referral for germline mutation testing.