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

Date 29 September 2019
Event ESMO 2019 Congress
Session Poster Display session 2
Topics Cervical Cancer
Presenter Chel Hun Choi
Citation Annals of Oncology (2019) 30 (suppl_5): v403-v434. 10.1093/annonc/mdz250
Authors C.H. Choi
  • Obstetrics And Gynecology, Samsung Medical Center (SMC)-Sungkyunkwan University School of Medicine, 135-710 - Seoul/KR



Resistance to chemo-radiation therapy is a substantial obstacle compromising treatment of advanced cervical cancer. We investigated if proteomic panel associated with radioresistance can predict the survival in locally advanced cervical cancer.


One hundred and eighty-one frozen tissue samples were prospectively obtained from patients with locally advanced cervical cancer before chemoradiation. To develop survival prediction model, expression of 22 total and phosphorylated proteins was evaluated by well-based reverse phase protein arrays. and selected proteins were validated by western blotting analysis and immunohistochemistry. The performance of models was internally and externally validated.


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 of 0.86, and 0.62, respectively), and also in the prediction of PFS (C-index of 0.82, and 0.64, respectively). The Kaplan-Meier survival curve shows the dose-dependent prognostic significance of the risk score for PFS and OS. The 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).


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 clinical outcome prediction in cervical cancer patients.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Chel Hun Choi.


Samsung Medical Center.


The author has declared no conflicts of interest.