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.