Prediction of recurrence free survival for esophageal cancer patients using a protein signature based risk model

使用基于蛋白质特征的风险模型预测食管癌患者的无复发生存期

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作者:Raghibul Hasan, Gunjan Srivastava, Akram Alyass, Rinu Sharma, Anoop Saraya, Tushar K Chattopadhyay, Siddartha DattaGupta, Paul G Walfish, Shyam S Chauhan, Ranju Ralhan

Background

Biomarkers to predict the risk of disease recurrence in Esophageal squamous cell carcinoma (ESCC) patients are urgently needed to improve treatment. We developed proteins expression-based risk model to predict recurrence free survival for ESCC patients.

Conclusions

Our comprehensive risk model predictive for recurrence allowed us to determine the robustness of our biomarker panel in stratification of ESCC patients at high or low risk of disease recurrence; high risk patients are stratified for more rigorous personalized treatment while the low risk patients may be spared from harmful side effects of toxic therapy.

Methods

Alterations in Wnt pathway components expression and subcellular localization were analyzed by immunohistochemistry in 80 ESCCs, 61 esophageal dysplastic and 47 normal tissues; correlated with clinicopathological parameters and clinical outcome over 86 months by survival analysis. Significant prognostic factors were identified by multivariable Cox regression analysis.

Results

Biomarker signature score based on cytoplasmic β-catenin, nuclear c-Myc, nuclear DVL and membrane α-catenin was associated with recurrence free survival [Hazard ratio = 1.11 (95% CI = 1.05, 1.17), p < 0.001, C-index = 0.68] and added significant prognostic value over clinical parameters (p < 0.001). The inclusion of Slug further improved prognostic utility (p < 0.001, C-index = 0.71). Biomarker Signature Scoreslug improved risk classification abilities for clinical outcomes at 3 years, accurately predicting recurrence in 79% patients in 1 year and 97% in 3 years in high risk group; 73% patients within low risk group did not have recurrence in 1 year, with AUC of 0.76. Conclusions: Our comprehensive risk model predictive for recurrence allowed us to determine the robustness of our biomarker panel in stratification of ESCC patients at high or low risk of disease recurrence; high risk patients are stratified for more rigorous personalized treatment while the low risk patients may be spared from harmful side effects of toxic therapy.

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