Comprehensive metabolomic analysis identifies key biomarkers and modulators of immunotherapy response in NSCLC patients

Although immune checkpoint inhibitors (ICIs) have revolutionized immuno-oncology with effective clinical responses, only 30 to 40 % of patients respond to ICIs, highlighting the need for reliable biomarkers to predict and enhance therapeutic outcomes. This study investigated how amino acid, glycolysis, and bile acid metabolism affect ICI efficacy in non-small cell lung cancer (NSCLC) patients. Through targeted metabolomic profiling and machine learning analysis, we identified amino acid metabolism as a key factor, with histidine (His) linked to favorable outcomes and homocysteine (HCys), phenylalanine (Phe), and sarcosine (Sar) linked to poor outcomes. Importantly, the His/HCys+Phe+Sar ratio emerges as a robust biomarker. Furthermore, we emphasize the role of glycolysis-related metabolites, particularly lactate. Elevated lactate levels post-immunotherapy treatment correlate with poorer outcomes, underscoring lactate as a potential indicator of treatment efficacy. Moreover, specific bile acids, glycochenodeoxycholic acid (GCDCA) and taurolithocholic acid (TLCA), are associated with better survival and therapeutic response. Particularly, TLCA enhances T cell activation and anti-tumor immunity, suggesting its utility as a predictive biomarker and therapeutic agent. We also suggest a connection between gut microbiota and TLCA levels, with the Eubacterium genus modulating this relationship. Therefore, modulating specific metabolic pathways—particularly amino acid, glycolysis, and bile acid metabolism—could predict and enhance the efficacy of ICI therapy in NSCLC patients, with potential implications for personalized treatment strategies in immuno-oncology. Our study identifies metabolic biomarkers and pathways that could predict and enhance the outcomes of immune checkpoint inhibitor therapy in NSCLC patients