ArticleJournal for immunotherapy of cancer2024
Plasma metabolomics of immune-related adverse events for patients with lung cancer treated with PD-1/PD-L1 inhibitors.
Article in Journal for immunotherapy of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Omics-based biomarkers of immune-related organ toxicities associated with immune checkpoint inhibitors: a scoping review and evidence map.Frontiers in immunology · 2026Article
- Integrated cellular, proteomic and metabolomic profiling of immune-related adverse events in non-small cell lung cancer.Frontiers in immunology · 2026Article
- Serum metabolic characteristics of interstitial lung disease: a potential link between endocrine aging and gut-derived metabolites.Frontiers in medicine · 2026Article
- Dynamic changes in gut microbiota and metabolites in advanced lung cancer patients with immune-related adverse events.Frontiers in immunology · 2026Article
- Discovery of plasma metabolomic biomarkers and a predictive model for immune checkpoint inhibitor-associated myocarditis: a pilot case-control study.Frontiers in immunology · 2026Article
- The role of metabolites in the causal effect of immune cell phenotypes on immunotherapy toxicity risk.Discover oncology · 2025Article
- Immunometabolism: The role of gut-derived microbial metabolites in optimising immune response during checkpoint inhibitor therapy.Clinical and translational medicine · 2025Review
- Application of Immune Checkpoint Inhibitors in Cancer.MedComm · 2025Review
- Metabolomics- and Proteomics-Based Disease Diagnostic Classifier Model for the Prediction and Diagnosis of Colorectal Carcinoma.Journal of proteome research · 2025Article
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9 authors.
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Abstract
backgroundMetabolomics has the characteristics of terminal effects and reflects the physiological state of biological diseases more directly. Several current biomarkers of multiple omics were revealed to be associated with immune-related adverse events (irAEs) occurrence. However, there is a lack of reliable metabolic biomarkers to predict irAEs. This study aims to explore the potential metabolic biomarkers to predict risk of irAEs and to investigate the association of plasma metabolites level with survival in patients with lung cancer receiving PD-1/PD-L1 inhibitor treatment.
methodsThe study collected 170 plasmas of 85 patients with lung cancer who received immune checkpoint inhibitors (ICIs) treatment. 58 plasma samples of 29 patients with irAEs were collected before ICIs treatment and at the onset of irAEs. 112 plasma samples of 56 patients who did not develop irAEs were collected before ICIs treatment and plasma matched by treatment cycles to onset of irAEs patients. Untargeted metabolomics analysis was used to identify the differential metabolites before initiating ICIs treatment and during the process that development of irAEs. Kaplan-Meier curves analysis was used to detect the associations of plasma metabolites level with survival of patients with lung cancer.
resultsA total of 24 differential metabolites were identified to predict the occurrence of irAEs. Baseline acylcarnitines and steroids levels are significantly higher in patients with irAEs, and the model of eight acylcarnitine and six steroid metabolites baseline level predicts irAEs occurrence with area under the curve of 0.91. Patients with lower concentration of baseline decenoylcarnitine(AcCa(10:1) 2, decenoylcarnitine(AcCa(10:1) 3 and hexanoylcarnitine(AcCa(6:0) in plasma would have better overall survival (OS). Moreover, 52 differential metabolites were identified related to irAEs during ICIs treatment, dehydroepiandrosterone sulfate, corticoserone, cortisol, thyroxine and sphinganine 1-phaosphate were significantly decreased in irAEs group while oxoglutaric acid and taurocholic acid were significantly increased in irAEs group.
conclusionsHigh levels of acylcarnitines and steroid hormone metabolites might be risk factor to development of irAEs, and levels of decenoylcarnitine (AcCa(10:1) 2, decenoylcarnitine (AcCa(10:1) 3 and hexanoylcarnitine (AcCa(6:0) could be used to predict OS for patients with lung cancer received ICIs treatment.
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