DNA methylation profiles predicting response to anti-PD-1-based treatment in patients with advanced gastric cancer

BACKGROUND: Immunotherapy has shown promise in treating gastric cancer (GC), yet predicting its efficacy remains challenging. Here we investigated DNA methylation as a predictive marker for response of anti-PD-1-based treatment in GC. METHODS: A total of 99 GC patients treated with first-line anti-PD-1-based treatment were enrolled. In the model construction phase, 30 samples were analyzed using the Infinium MethylationEPIC BeadChip (850 K array) and 41 samples using Targeted Bisulfite Sequencing (TBS). Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Least Absolute Shrinkage and Selector Operation (LASSO) were applied to identify differential CpG methylation probes (DMPs). Seven machine learning models were developed, and their performance was assessed by the area under the curve (AUC) of receiver operating characteristic and survival analysis. SHapley Additive exPlanations (SHAP) analysis provided interpretability of the model. In the model validation phase, a temporally independent cohort of 28 samples underwent TBS for external validation. RESULTS: The 850 K array identified 523 DMPs, of which 20 were selected as most significant for treatment response. The iMETH model, based on the k-nearest neighbors (KNN) algorithm, showed optimal predictive value in both training (AUC = 0.99) and testing (AUC = 0.96) sets. Progression-free survival (PFS) and overall survival (OS) were significantly longer in responders predicted by iMETH (all log-rank test p < 0.05...