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

Date

29 Sep 2019

Session

Poster Display session 2

Presenters

Chel Hun Choi

Citation

Annals of Oncology (2019) 30 (suppl_5): v403-v434. 10.1093/annonc/mdz250

Authors

C.H. Choi

Author affiliations

  • Obstetrics And Gynecology, Samsung Medical Center (SMC)-Sungkyunkwan University School of Medicine, 135-710 - Seoul/KR
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Resources

Background

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.

Methods

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.

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 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).

Conclusions

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.

Funding

Samsung Medical Center.

Disclosure

The author has declared no conflicts of interest.

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