PD-1 monoclonal antibody has been used as a first-line drug for the treatment of advanced esophageal cancer, however, the ideal predictor of efficacy has not been established. The peripheral immune system plays an important role in driving anti-tumor effects, and the characteristics of peripheral immune cells can predict the effect of PD-1 monoclonal antibody therapy. However, the value of a single indicator for predicting the effect of PD-1 monoclonal antibody therapy is greatly limited. 200 patients with advanced esophageal cancer who received PD-1 monoclonal antibody treatment would be enrolled. Changes in peripheral blood immune cells before and after treatment would be recorded and used for machine learning to establish a prediction model for the efficacy of PD-1 monoclonal antibody treatment. The application between the indicator model and the expression level of PD-L1 in tumor tissue in predicting the efficacy of PD-1 monoclonal antibody in advanced esophageal cancer will be also study. This study aims to establish a peripheral blood immune cell-based models to predict the efficacy of PD-1 monoclonal antibody therapy for advanced esophageal cancer, provide a basis for guiding the selection of treatment options for advanced esophageal cancer, and improve the efficacy of PD-1 monoclonal antibody therapy.
200 patients with advanced esophageal cancer who received PD-1 monoclonal antibody treatment would be enrolled in this study. Changes in peripheral blood immune cells before and after treatment would be recorded and used for machine learning to establish a prediction model for the efficacy of PD-1 monoclonal antibody treatment.