This study comprehensively examines metabolic and lipidomic dynamics in gastric cancer patients initiating PD-1/PD-L1 inhibitor therapy, employing a longitudinal design with pre- and post-treatment patients. The primary objectives include identifying irAE-associated metabolic and lipid biomarkers, developing predictive risk models, and evaluating the prognostic value of these molecular profiles. The findings are expected to contribute significantly to personalized treatment strategies and improved clinical decision-making in immunooncology.
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.
Study Type
OBSERVATIONAL
Enrollment
100
Qinghai Red Cross Hospital
Xining, Qinghai, China
RECRUITINGIncidence 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).
Time frame: 1 year
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.
Time frame: 1 year
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.
Time frame: 1 year
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.
Time frame: 2 years
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
Time frame: 2 years
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