Background: Pathological complete response (pCR) is an established surrogate marker for prognosis in patients with breast cancer (BC) after neoadjuvant chemotherapy. Individualized pCR prediction based on clinical information available at biopsy, particularly immunohistochemical (IHC) markers, may help identify patients who could benefit from preoperative chemotherapy.
Methods: Data from patients with HER2-negative BC who underwent neoadjuvant chemotherapy from 2002 to 2020 (n = 1166) were used to develop multivariable prediction models to estimate the probability of pCR (pCR-prob). The most precise model identified using cross-validation was implemented in an online calculator and a nomogram. Associations among pCR-prob, prognostic IHC3 distant recurrence and disease-free survival were studied using Cox regression and Kaplan-Meier analyses. The model's utility was further evaluated in independent external validation cohorts.
Results: 273 patients (23.4%) achieved a pCR. The most precise model had across-validated area under the curve (AUC) of 0.84, sensitivity of 0.82, and specificity of 0.71. External validation yielded AUCs between 0.75 (95% CI, 0.70-0.81) and 0.83 (95% CI, 0.78-0.87). The higher the pCR-prob, the greater the prognostic impact of pCR status (presence/absence): hazard ratios decreased from 0.55 (95% central range, 0.07-1.77) at 0% to 0.20 (0.11-0.31) at 50% pCR-prob. Combining pCR-prob and IHC3 score further improved the precision of disease-free survival prognosis.
Conclusions: A pCR prediction model for neoadjuvant therapy decision-making was established. Combining pCR and recurrence prediction allows identification of not only patients who benefit most from neoadjuvant chemotherapy, but also patients with a very unfavorable prognosis for whom alternative treatment strategies should be considered.
Keywords: Breast cancer; Clinical data; Immunohistochemistry; Neoadjuvant chemotherapy; Prediction model; pCR.
© 2025. The Author(s).