Personalized immune subtypes based on machine learning predict response to checkpoint blockade in gastric cancer

Immune checkpoint inhibitors (ICI) show high efficiency in a small fraction of advanced gastric cancer (GC). However, personalized immune subtypes have not been developed for the prediction of ICI efficiency in GC. Herein, we identified Pan -Immune Activation Module (PIAM), a curated gene expression profile (GEP) representing the co -infiltration of multiple immune cell types in tumor microenvironment of GC, which was associated with high expression of immunosuppressive molecules such as PD -1 and CTLA-4. We also identified Pan -Immune Dysfunction Genes (PIDG), a conservative PIAM-derivated GEP indicating the dysfunction of immune cell cooperation, which was associated with upregulation of metastatic programs (extracellular matrix receptor interaction, TGF-beta signaling, epithelial-mesenchymal transition and calcium signaling) but downregulation of proliferative signalings (MYC targets, E2F targets, mTORC1 signaling, and DNA replication and repair). Moreover, we developed `GSClassifier', an ensemble toolkit based on top scoring pairs and extreme gradient boosting, for population -based modeling and personalized identification of GEP subtypes. With PIAM and PIDG, we developed four Pan -immune Activation and Dysfunction (PAD) subtypes and a GSClassifier model 'PAD for individual' with high accuracy in predicting response to pembrolizumab (anti -PD -1) in advance GC (AUC = 0.833). Intriguingly,. PAD -II (PIAM(high)PIDG(low)) displayed the highest objective response rate (60.0%) compared with other subtypes (PAD -I, PIAM(high)PIDG(high) 0%; PAD -III, PIAM(low)PIDG(high) 0%; PAD -IV, PIAM(low)PIDG(low), 17.6%; P = 0.003), which was further validated in the metastatic urothelial cancer cohort treated with atezolizumab (anti -PD -L1) (P = 0.018). In all, we provided `GSClassifier' as a refined computational framework for GEP-based stratification and PAD subtypes as a promising strategy for exploring ICI responders in GC. Metastatic pathways could be potential targets for GC patients with high immune infiltration but resistance to ICI therapy.