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This study developed and validated a predictive model for pathological complete response (pCR) in 233 breast cancer patients undergoing neoadjuvant immunotherapy at a single institution. The model, incorporating pretreatment clinical T stage, Ki67 expression, neutrophil-to-lymphocyte ratio, chemotherapy regimens, and immunotherapy cycles, demonstrated good discrimination (AUC 0.818 in validation set) and clinical benefit. The authors suggest this model may help individualize treatment strategies.
A predictive model incorporating readily available clinical and laboratory variables can estimate the likelihood of pathological complete response to neoadjuvant immunotherapy in breast cancer patients.
Neoadjuvant immunotherapy combined with chemotherapy has shown promising potential in breast cancer management, but predicting pathological complete response (pCR) remains challenging. This study aimed to develop and validate a predictive model for pCR in breast cancer patients receiving neoadjuvant immunotherapy. We retrospectively analyzed breast cancer patients treated with neoadjuvant immunotherapy at Sun Yat-sen University Cancer Center (SYSUCC) between November 2019 and April 2025. pCR was defined as Miller-Payne grade 5, while grades 1-4 were classified as non-pCR. Clinical, pathological, and laboratory variables—including age, body mass index (BMI), menopausal status, TNM stage, histological type, Ki67 expression, chemotherapy regimen, immune checkpoint inhibitor type, immunotherapy cycles, treatment pattern, neutrophil-to-lymphocyte ratio (NLR), albumin, C-reactive protein, and serum amyloid A—were analyzed. Univariate logistic regression identified variables significantly associated with pCR (p < 0.05), which were further analyzed by multivariate logistic regression. Variables with p < 0.1 in the multivariate model were included in the final predictive model. The cohort was randomly divided (2:1) into a training and validation set. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). A total of 233 patients were included, and 52.8% achieved pCR. The final model incorporated pretreatment clinical T stage, pretreatment Ki67 expression, pretreatment NLR, chemotherapy regimens, and number of immunotherapy cycles. The model demonstrated good discrimination, with an AUC of 0.782 in the training set and 0.818 in the validation set. Calibration curves indicated good agreement between predicted and observed pCR probabilities. DCA showed the model provided a net clinical benefit over “treat all” and “treat none” strategies across a range of threshold probabilities. We developed and validated a practical predictive model for pCR in breast cancer patients receiving neoadjuvant immunotherapy. The model may assist clinicians in individualizing treatment strategies and optimizing patient outcomes. Prospective validation is warranted. M. Lin, T. Du, J. Tang. Construction and validation of a clinical model predicting pathological complete response after neoadjuvant immunotherapy in breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS4-04-05.
