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

[OBJECTIVES] To 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.

[METHODS] We 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 (=116) and a validation set (=30) at a 4:1 ratio. Using the PyTorch framework, we constructed a neural network model (designated NNT9) incorporating age, sex, body mass index (BMI), TMB, history of systemic therapy, neutrophil-to-lymphocyte ratio (NLR), and other routine blood parameters. The model employed a multilayer perceptron architecture. Hyperparameters were automatically optimized using AutoGluon, and the model was refined via 5-fold cross-validation. SHapley Additive exPlanations (SHAP) was used to perform feature importance analysis on the optimal model in the training set. Predictive performance was compared against TMB alone using metrics including the area under the receiver operating characteristic curve (AUC), accuracy, F1 score, sensitivity, and specificity. Confusion matrices were generated, and the association between model-predicted response groups and progress free survive (PFS) was analyzed.

[RESULTS] NNT9 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 <0.01).

[CONCLUSIONS] The 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.