Prognostic analysis of three forms of Ki-67 in patients with breast cancer with non-pathological complete response before and after neoadjuvant systemic treatment

Background: Patients who do not achieve a pathological complete response (pCR) after neoadjuvant systemic treatment (NST) have a significantly worse prognosis. A reliable predictor of prognosis is required to further subdivide non-pCR patients. To date, the prognostic role in terms of disease-free survival (DFS) between the terminal index of Ki-67 after surgery (Ki-67(T)) and the combination of the baseline Ki-67 at biopsy before NST (Ki-67(B)) and the percentage change in Ki-67 before and after NST (Ki-67(C)) has not been compared.Aim: This study aimed to explore the most useful form or combination of Ki-67 that can provide prognostic information to non-pCR patients.Patients and Methods: We retrospectively reviewed 499 patients who were diagnosed with inoperable breast cancer between August 2013 and December 2020 and received NST with anthracycline plus taxane.Results: Among all the patients, 335 did not achieve pCR (with a follow-up period of >= 1 year). The median follow-up duration was 36 months. The optimal cutoff value of Ki-67(C) to predict a DFS was 30%. A significantly worse DFS was observed in patients with a low Ki-67(C) (p < 0.001). In addition, the exploratory subgroup analysis showed relatively good internal consistency. Ki-67(C) and Ki-67(T) were considered as independent risk factors for DFS (both p < 0.001). The forecasting model combining Ki-67(B) and Ki-67(C) showed a significantly higher area under the curve at years 3 and 5 than Ki-67(T) (p = 0.029 and p = 0.022, respectively).Conclusions: Ki-67(C) and Ki-67(T) were good independent predictors of DFS, whereas Ki-67(B) was a slightly inferior predictor. The combination of Ki-67(B) and Ki-67(C) is superior to Ki-67(T) for predicting DFS, especially at longer follow-ups. Regarding clinical application, this combination could be used as a novel indicator for predicting DFS to more clearly identify high-risk patients.