Functional proteomics characterization of residual triple-negative breast cancer after standard neoadjuvant chemotherapy

Background: In this study, we used functional proteomics to determine the molecular characteristics of residual triple receptor-negative breast cancer (TNBC) patients after neoadjuvant systemic chemotherapy (NCT) and their relationship with patient outcomes in order to identify potential targets for therapy. Patients and methods: Protein was extracted from 54 residual TNBCs, and 76 proteins related to breast cancer signaling were measured by reverse phase protein arrays (RPPAs). Univariable and multivariable Cox proportional hazard models were fitted for each protein. Survival outcomes were estimated by the Kaplan-Meier product limit method. Training and cross validation were carried out. The coefficients estimated from the multivariable Cox model were used to calculate a risk score (RS) for each sample. Results: Multivariable analysis using the top 25 proteins from univariable analysis at a false discovery rate (FDR) of 0.3 showed that AKT, IGFBP2, LKB1, S6 and Stathmin were predictors of recurrence-free survival (RFS). The crossvalidation model was reproducible. The RS model calculated based on the multivariable analysis was -1.1086 × AKT + 0.2501 × IGFBP2-0.6745 × LKB1+1.0692 × S6 + 1.4086 × stathmin with a corresponding area under the curve, AUC = 0.856. The RS was an independent predictor of RFS (HR = 3.28, 95%CI = 2.07-5.20, P < 0.001). Conclusions:We found a five-protein model that independently predicted RFS risk in patients with residual TNBC disease. The PI3 K pathway may represent potential therapeutic targets in this resistant disease.