Chel Hun Choi1, Joon-Yong Chung2, Jun Hyeok Kang3, E Sun Paik3, Yoo-Young Lee3, Won Park4, Sun-Ju Byeon5, Eun Joo Chung6, Byoung-Gie Kim3, Stephen M Hewitt7, Duk-Soo Bae8. 1. Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, USA. 2. Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, USA. 3. Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 4. Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 5. Department of Pathology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea. 6. Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, USA. 7. Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, USA. Electronic address: genejock@helix.nih.gov. 8. Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. Electronic address: ds123.bae@samsung.com.
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. Published by Elsevier Inc.
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 cancerpatients. This prediction model can help identify chemoradiation responsive tumors and improve prediction for clinical outcome of cervical cancerpatients. Published by Elsevier Inc.
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