Chemoradiotherapy response prediction model by proteomic expressional profiling in patients with locally advanced cervical cancer

Abstract

Objective: Resistance to chemo-radiation therapy is a substantial obstacle that compromises treatment of advanced cervical cancer. The objective of this study was to investigate if a proteomic panel associated with radioresistance could predict survival of patients with locally advanced cervical cancer.

Methods: A total of 181 frozen tissue samples were prospectively obtained from patients with locally advanced cervical cancer before chemoradiation. Expression levels of 22 total and phosphorylated proteins were evaluated using well-based reverse phase protein arrays. Selected proteins were validated with western blotting analysis and immunohistochemistry. Performances of models were internally and externally validated.

Results: Unsupervised clustering stratified patients into three major groups with different overall survival (OS, P = 0.001) and progression-free survival (PFS, P = 0.003) based on detection of BCL2, HER2, CD133, CAIX, and ERCC1. Reverse-phase protein array results significantly correlated with western blotting results (R² = 0.856). The C-index of model was higher than clinical model in the prediction of OS (C-index: 0.86 and 0.62, respectively) and PFS (C-index: 0.82 and 0.64, respectively). The Kaplan-Meier survival curve showed a dose-dependent prognostic significance of risk score for PFS and OS. Multivariable Cox proportional hazard model confirmed that the risk score was an independent predictor of PFS (HR: 1.6; 95% CI: 1.4-1.9; P < 0.001) and OS (HR: 2.1; 95% CI: 1.7-2.5; P < 0.001).

Conclusion: A proteomic panel of BCL2, HER2, CD133, CAIX, and ERCC1 independently predicted survival in locally advanced cervical cancer patients. This prediction model can help identify chemoradiation responsive tumors and improve prediction for clinical outcome of cervical cancer patients.

Keywords: Chemoradiotherapy; Cluster analysis; Prognosis; Proportional hazards models; Protein array analysis; Uterine cervical neoplasms.

Fig. 1.

Unsupervised hierarchical clustering. (A) Hierarchical clustering analysis of 22-protein expressional data by well-based RPPA divided patients into three groups. Adenocarcinoma was more frequent in groups 2 and 3. Proteins indicated by red arrow head are tested with western blotting analysis. Progression-free survival (B) and overall survival (C) according to groups. We subsequently validated well-based RPPA results using western blotting analysis and locally advanced cervical cancer samples. (D) Representative western blot images in 10 locally advanced cervical cancer specimens. Protein expression levels of HER2, CD133, CAIX, ERCC1, and BCL2 are confirmed by western blot analysis. All of the samples were squamous cell carcinomas cases. (E) Comparison of relative signal by western blotting and expressional values by well-based RPPA. Protein expressional levels are normalized against β-actin expression. There was a significant correlation between expression signal from western blotting and well-based RPPA score (r = 0.879, P < 0.001). r, correlation coefficients.

Fig. 2.

Model predicting overall survival. (A) Minus log p-value of log rank test for each cut-off point of risk score of a combined model. Area above the red line means statistical significance. (B) C-index of a random survival forest model. Combined clinical and protein-variable model showed better performance than the model only based on clinical variables in predicting death (P = 0.017). (C-F) Kaplan-Meier survival curve according to risk score of each model in each set. C-index of combined model was higher than that of clinical model (C-index: 0.62 and 0.86, respectively). C-index, concordance-index.