Stathmin and phospho-stathmin protein signature is associated with survival outcomes of breast cancer patients

Abstract

Currently, Stathmin1 (STMN1) and phospho-STMN1 levels in breast cancers and their clinical implications are unknown. We examined the expression of STMN1 and its serine phospho-site (Ser16, Ser25, Ser38, and Ser63) status by immunohistochemistry. Using Cox regression analysis, a STMN1 expression signature and phosphorylation profile plus clinicopathological characteristics (STMN1-E/P/C) was developed in the training set (n = 204) and applied to the validation set (n = 106). This tool enabled us to separate breast cancer patients into high- and low-risk groups with significantly different disease-free survival (DFS) rates (P < 0.001). Importantly, this STMN1-E/P/C model had a greater prognostic value than the traditional TNM classifier, especially in luminal subtype breast cancer (P = 0.002). Further analysis showed that patients in the low-risk group would benefit more from adjuvant paclitaxel-based chemotherapy (P = 0.002). In conclusion, the STMN1-E/P/C signature is a reliable prognostic indicator for luminal subtype breast cancer and may predict the therapeutic response to paclitaxel-based treatments, potentially facilitating individualized management.

Keywords: breast cancer; paclitaxel; phosphorylation; prognostic model; stathmin.

Figure 1. Identification by immunohistochemistry (IHC) of STMN1 and its multiple phosphor-sites in the primary tumor and Kaplan-Meier analysis of DFS in breast cancer patients with high or low STMN1 expression and the expression of its multiple serine phospho-sites

A. Representative IHC staining of high and low expression of STMN1 in the large (400×) and small images (100×). B. Representative IHC staining of high and low expression of multiple phosphor-sites (Ser-16, Ser-25, Ser38, Ser63) in the large (400×) and small images (100×). C. Kaplan-Meier analysis of DFS in the training set. D. Kaplan-Meier analysis of DFS in the validation set.

Figure 2. Time-dependent ROC curves for the prognosis of breast cancer by the STMN1-E/P model and Kaplan-Meier survivals in the training and validation sets

Data are shown as AUC (95% CI) or hazard ratios (95% CI). ROC = receiver operator characteristic. AUC = area under the curve. A. Comparisons of the prognostic accuracy by the STMN1-E/P model and TNM stage in the training set. B. DFS of patients with high- or low-risk scores in the training set. C. Comparisons of the prognostic accuracy by the STMN1-E/P model and TNM stage in the validation set. D. DFS of patients with high- or low-risk scores in the validation set. P values were calculated using the log-rank test.

Figure 3. Time-dependent ROC curves for prognosis of breast cancer by the STMN1-E/P/C model and Kaplan-Meier survivals in patients of two sets and different subtypes of breast cancer with high- or low-risk according to the STMN1-E/P/C model

A. Comparisons of the prognostic accuracy by the STMN1-E/P/C model, STMN1-E/P model and TNM stage in the training set. B. DFS of patients with high- or low-risk scores according to the STMN1-E/P/C model in the training set. C. Comparisons of the prognostic accuracy by the STMN1-E/P/C model, STMN1-E/P model and TNM stage in the validation set. D. DFS of patients with high- or low-risk scores according to the STMN1-E/P/C model in the validation set. P values were calculated using the log-rank test. E. DFS of patients with luminal breast cancer. F. DFS of patients with HER2/neu subtype breast cancer. G. DFS of patients with TNBC subtype breast cancer.