CELL INTELLIGENCE · 肿瘤细胞治疗研究

Immune checkpoint inhibitor (ICI)-based immunotherapy brought new hope for gastric cancer (GC) treatment. However, due to the lack of proper biomarkers, patient selection and outcome prediction for GC's immunotherapy remain unsatisfying. In this study, through applying an extracellular vesicle (EV) protein expression array, we assessed the correlation of plasma EV-derived protein spectrum with outcomes of ICI-related therapeutic combinations. Plasma from 112 GC patients received ICI-related therapies were investigated retrospectively/prospectively as three cohorts. We identified four plasma EV-derived proteins (ARG1/CD3/PD-L1/PD-L2) from 42 crucial candidate proteins and combined them as an EV-score that robustly predicting immunotherapeutic outcomes at baseline and dynamically monitoring disease progression along with treatment. High EV-score reflected microenvironmental features of stronger antitumour immunity, characterized by more activated CD8 + T/NK cells, higher TH1/TH2 ratio and higher expressions of IFN-γ/perforin/granzymes in paired peripheral blood, which were verified by dataset analysis and in vivo experiments. EV-score≥1 GC received more therapeutic benefits from ICIs, while EV-score < 1 GC potentially benefited more from ICIs combining HER2-targeted therapies. Collectively, through proposing a plasma EV-score on protein level that powerfully predicting and monitoring GC's immunotherapeutic outcomes, our work facilitated clinical patient selection and decision-makings, and provided mechanistical insights for immunotherapy-related microenvironmental changes and improvements for current ICI-regimens.