INDIVIDUAL PROGNOSTIC ALGORITHM FOR ESTIMATING THE RISK OF ESOPHAGEAL CANCER PROGRESSION AFTER SURGICAL TREATMENT - O. I. Kit; E. Yu. Zlatnik; A. L. Bazaev; A. A. Demidova; I. A. Novikova (Сибирский онкологический журнал)
The development of laboratory criteria for predicting esophageal cancer (EC) prognosis is of great importance due to the need to achieve personalized approach to cancer treatment. Since the role of lymphocytic infiltration in EC remains controversial, our goal was to develop a prognostic algorithm for estimating the risk of esophageal squamous cell carcinoma progression, considering its lymphocytic microenvironment.Material and Methods. Tumor tissues were obtained from 40 EC patients during surgery; the tissues were homogenized, and lymphocyte subsets (Т-В-NK, T-reg) were determined by flow cytometry. A prognostic algorithm for calculating the risk of EC progression within 3 years was developed using discriminant analysis with the calculation of the three F functions: F0 , F6–12, F12–24, corresponding to the absence of the risk of EC progression during 3 years (F0 ); a high risk of EC progression during 6–12 months (F6–12); a high risk of EC progression during 12–24 months (F12–24) after surgery.Results. Only two factors showed the highest discriminant power, allowing us to consider the differences as statistically significant – CD3+CD4+ and T-reg cells in tumors. When dividing EC patients into groups based on the prediction of time to disease progression, coefficients were calculated and mathematical functions were determined for three discriminant functions (F0 , F6–12, F12–24) organized into a model. The F coefficient calculated for each patient allowed us to predict the risk of EC progression 6–12 and 12–24 months after surgery or the absence of disease progression within 3 years after surgery.Conclusions. The development of EC progression after surgery is apparently influenced by the lymphocytic microenvironment, predominantly by CD3+CD4+ and T-regs; their determination and inclusion in the prognostic algorithm can be important for personalized approach to the treatment of EC patients.