Prediction of neoadjuvant chemotherapy efficacy and prognostic biomarker analysis in patients with triple-negative breast cancer - Liao, Xiao-Wen; Gao, Jia-Bin; Sun, Hong; Chen, Hong-Dan; Zheng, Min-Hui; Han, Lei; Chen, Xiao-Geng; Su, Yu-Nan; Pan, Ding-Long; Wu, Min; Cai, Shuang-Long; Lin, Xiuquan; et al. (Frontiers in Pharmacology, 2025)
BackgroundNeoadjuvant chemotherapy has become a common and effective treatment modality for triple-negative breast cancer (TNBC). The primary goal is to reduce the size of the primary tumor, enabling breast-conserving surgery, axillary preservation, and a transition to operability, thereby providing patients with more therapeutic options. Although neoadjuvant chemotherapy (NAC) has demonstrated favorable outcomes in clinical practice, predicting its efficacy and prognostic value in TNBC remains a key challenge in current clinical research.MethodsThis study included 248 TNBC patients who received NAC at two breast cancer treatment centers. By employing a modeling validation approach, we aim to explore predictors of treatment efficacy and potential prognostic biomarkers associated with NAC.ResultsIn the multivariable analysis of the training set, the factors predicting the pathological complete response (pCR) to NAC in TNBC patients include high biopsy-sTILs expression, biopsy-Ki67 > 20%, and positive expression of biopsy-androgen receptor (AR). The factors predicting disease-free survival (DFS) are ypN3, high postoperative sTIL expression, receipt of postoperative radiotherapy, and effective NAC. The factors predicting overall survival (OS) include ypN2, ypN3, high postoperative sTIL expression, postoperative Ki67 > 20%, receipt of postoperative radiotherapy, and effective NAC. The C-indices in the training and validation sets for the prediction of pCR using the nomogram were 0.729 and 0.816, respectively. The C-indices for predicting DFS were 0.895 and 0.865, respectively. The C-indices for predicting OS were 0.899 and 0.860, respectively.ConclusionThis study established and validated a nomogram model predicting the pCR, DFS, and OS in TNBC patients undergoing NAC. This model demonstrates good discrimination and accuracy.