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

Incidence of immune-related adverse events

* For gastric cancer patients receiving immunotherapy for the first time, immune-related adverse events should be monitored during treatment, with a follow-up period of 1 year. * The immune checkpoint inhibitors include pembrolizumab, nivolumab, sintilimab, tislelizumab, sugemalimab and camrelizumab. * NCCN Guidelines for Management of Immunotherapy-Related Toxicity are adopted as the gold standard for assessing immune-related adverse events. * The severity of immune-related adverse events was evaluated based on the Common Terminology Criteria for Adverse Events version 5.0 (CTCAE v5.0).

Changes in Plasma Metabolite Levels

* A comprehensive metabolomic profiling of plasma in treatment-naïve gastric cancer patients undergoing immunotherapy: Investigating metabolic disparities between responders with immune-related adverse events and those without. * The measurement methods include gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). * The measured metabolites include amino acids, fatty acids, glucose, lactate, nucleotides, organic acids, vitamins and small-molecule metabolites. * All metabolomic markers in plasma will be reported as relative quantitative values in arbitrary units. * Baseline plasma samples were collected from all patients prior to immunotherapy, and follow-up samples were obtained every 2 treatment cycles until 1 year after treatment initiation.

Changes in Plasma Lipid Levels

* Comprehensive lipidomic profiling of plasma from gastric cancer patients undergoing initial immunotherapy was performed to investigate differential plasma lipid signatures between those who developed immune-related adverse events and those who did not. * The measurement methods include gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). * The measured lipids include triglycerides, cholesterol, phospholipids, fatty acids, ceramides, steroids, fatty acid derivatives, and other sphingolipids. * All plasma lipidomic markers will be reported as relative quantitative values in arbitrary units. * Baseline plasma samples were collected from all patients prior to immunotherapy, with subsequent samples obtained every 2 treatment cycles until 1 year of therapy.

Develop a predictive model for immune-related adverse events

* This predictive model incorporates input variables including lipid molecules, metabolites, and baseline clinical characteristics of patients, with the output being an adverse event risk score (low, intermediate, or high risk). * Employing machine learning algorithms (e.g., logistic regression, random forests, support vector machines) to construct predictive models. * The clinical characteristics include age (reported in years), height and weight (reported as BMI in kg/m²), gender (recorded as male or female), tumor stage (classified by TNM staging system). * The measured metabolites include amino acids, fatty acids, glucose, lactate, nucleotides, organic acids, vitamins and small-molecule metabolites. * The measured lipids include triglycerides, cholesterol, phospholipids, fatty acids, ceramides, steroids, fatty acid derivatives, and other sphingolipids. * All metabolomic and lipidomics markers in plasma will be reported as relative quantitative values in arbitrary units.

Investigating the correlation between metabolomic and lipidomic profiles and patient outcomes to inform evidence-based clinical decision-making.

Using survival analysis (e.g., Kaplan-Meier curves, Cox regression models) to assess the association between Immune-related adverse events and patient survival rates or recurrence rates