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, Chen Guo-Zhong
Department of Radiation Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
General Surgery Department of Pengyang County People's Hospital, Guyuan, Ningxia, China.
Front Pharmacol. 2025 Mar 12;16:1553831. doi: 10.3389/fphar.2025.1553831. eCollection 2025.
Neoadjuvant 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.
This 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.
In 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.
This 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.
新辅助化疗已成为三阴性乳腺癌（TNBC）常见且有效的治疗方式。其主要目标是缩小原发肿瘤大小，从而能够进行保乳手术、保留腋窝，并实现可手术切除，进而为患者提供更多治疗选择。尽管新辅助化疗（NAC）在临床实践中已显示出良好疗效，但预测其在TNBC中的疗效和预后价值仍是当前临床研究的一项关键挑战。
本研究纳入了在两个乳腺癌治疗中心接受NAC的248例TNBC患者。通过采用建模验证方法，我们旨在探索治疗疗效的预测因素以及与NAC相关的潜在预后生物标志物。
在训练集的多变量分析中，预测TNBC患者对NAC的病理完全缓解（pCR）的因素包括活检sTILs高表达、活检Ki67>20%以及活检雄激素受体（AR）阳性表达。预测无病生存期（DFS）的因素为ypN3、术后sTIL高表达、接受术后放疗以及有效的NAC。预测总生存期（OS）的因素包括ypN2、ypN3、术后sTIL高表达、术后Ki67>20%、接受术后放疗以及有效的NAC。使用列线图预测pCR时，训练集和验证集的C指数分别为0.729和0.816。预测DFS的C指数分别为0.895和0.865。预测OS的C指数分别为0.899和0.860。
本研究建立并验证了一个预测接受NAC的TNBC患者pCR、DFS和OS的列线图模型。该模型显示出良好的区分度和准确性。
