[Construction of a prognosis forecasting model for immuno-therapy response in cancer patients by integrating routine clinical parameters and tumor mutational burden].

objectivesTo develop a machine-learning model that integrates routine clinical parameters with tumor mutational burden (TMB) and to evaluate its performance in predicting responses to programmed death-1 (PD-1)/programmed death-ligand 1(PD-L1) inhibitors across various cancer types.

methodsWe conducted a retrospective study of 146 patients with advanced solid tumors who were treated with PD-1/PD-L1 inhibitors. The cohort was randomly divided into a training set (

resultsNNT9 was identified as the optimal model, and the history of systemic therapy, TMB, platelet count, and BMI were the four most important predictive features. NNT9 achieved AUCs of 0.949 and 0.851 in the training and validation sets, respectively, outperforming TMB alone (AUCs: 0.747 and 0.720). In the validation set, NNT9 also demonstrated superior sensitivity (0.571), accuracy (0.867), F1 score (0.667), positive predictive value (0.800), and negative predictive value (0.880). The confusion matrix revealed that NNT9 misclassified only half as many patients as TMB alone in the validation set. Kaplan-Meier analysis showed that patients predicted to be responders by NNT9 had significantly longer PFS than non-responders in both training and validation sets (both

conclusionsThe NNT9 model, which integrates readily available clinical parameters with TMB, represents an accurate and clinically feasible tool for predicting immunotherapy benefit in a pan-cancer cohort, and shows promise for clinical translation.