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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.
Full description
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.
Enrollment
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Inclusion criteria
Age≥ 18 years
ECOG PS 0-2
Gastric cancer diagnosed by histology or cytology
Untreatment with PD-1/PD-L1 inhibitors
Expected survival≥3 months
Exclusion criteria
Unable to obtain an organization or due to insufficient organizational material, unable to diagnose gastric cancer
Refusal to receive PD-1/PD-L1 inhibitor treatment
Baseline (before immunotherapy) plasma samples are unavailable
Combined with autoimmune diseases
Baseline (before immunotherapy) there are severe diseases in the heart, lungs, thyroid gland and other organs
Baseline (before immunotherapy) there are severe abnormalities in liver and kidney functions, pancreatic enzymes and other indicators
⑦ Researchers posit that any condition deemed potentially harmful to the subjects or that might prevent subjects from meeting or adhering to the research requirements shall not be permissible for inclusion in this study
100 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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