Metabolomic and Lipidomic Analysis Predicts Immunotherapy-related Adverse Events in Gastric Cancer Patients

This study is designed as a prospective clinical trial employing advanced metabolomics and lipidomics methodologies to comprehensively analyze alterations in metabolites and lipid molecules within plasma samples. The primary objective is to investigate the differential profiles between gastric cancer patients who induce immune-related adverse events (irAEs) and who not, following Programmed Cell Death Protein 1/Programmed Death-Ligand 1 inhibitor(PD-1/PD-L1 inhibitor) therapy. Through this approach, the investigators aim to establish predictive biomarkers for irAEs occurrence and subsequently develop a robust prognostic model to enhance clinical management and therapeutic outcomes. Patients who meet the inclusion and exclusion criteria will be formally enrolled after screening and signing an informed consent form. Patients pathologically confirmed of gastric cancer who received anti-PD-1/anti-PD-L1 blockade therapy alone or with combined with chemotherapy. Baseline plasma samples were collected before immune checkpoint inhibitors(ICIs) treatment for all patients. Patients with irAEs collected plasma samples at the onset of irAEs, and patients without irAEs collected samples according to the treatment cycles to onset of irAEs patients. Patients with irAEs and without irAEs were matched by 1:1 or 1:2 for consideration of age, sex and stage to confirm the sample of which cycle should be chosen for patients without irAEs. Comprehensive metabolomics and lipidomics analyses were performed on the collected plasma samples,Differential metabolites and lipid molecules related to immune-related adverse events were screened out.By leveraging the identified key metabolites and lipid molecules, a robust predictive model has been developed to evaluate the risk of immune-related adverse events in patients. Comprehensive data on quality-of-life metrics and adverse event severity grading were systematically collected for patients experiencing immune-related adverse events. Extended analyses were carried out to evaluate potential links between the identified metabolic/lipidomic signatures and long-term patient prognosis.

• Develop a predictive model for immune-related adverse events [Time frame: 2 years]
• Investigating the correlation between metabolomic and lipidomic profiles and patient outcomes to inform evidence-based clinical decision-making. [Time frame: 2 years]