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Updated: Jun 10, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Comprehensive metabolomic analysis identifies key biomarkers and modulators of immunotherapy response in NSCLC
Se-Hoon Lee1, Sujeong Kim2, Jueun Lee3
1Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea.
Abstract:
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. ONE SENTENCE SUMMARY: Our study identifies metabolic biomarkers and pathways that could predict and enhance the outcomes of immune checkpoint inhibitor therapy in NSCLC patients.
Insights
Metabolic biomarkers, including amino acids like histidine and lactate, can predict immune checkpoint inhibitor (ICI) therapy outcomes in non-small cell lung cancer (NSCLC). Specific bile acids and gut bacteria also influence treatment response, offering personalized strategies.
Area of Science:
- Metabolomics and Immuno-oncology
- Biomarker Discovery in Oncology
Background:
- Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, but response rates remain limited (30-40%).
- Predictive biomarkers are crucial for optimizing ICI therapy efficacy in non-small cell lung cancer (NSCLC).
Purpose of the Study:
- To investigate the impact of amino acid, glycolysis, and bile acid metabolism on ICI efficacy in NSCLC patients.
- To identify novel metabolic biomarkers for predicting and enhancing ICI therapeutic outcomes.
Main Methods:
- Targeted metabolomic profiling of NSCLC patients undergoing ICI therapy.
- Machine learning analysis to identify key metabolic pathways and biomarkers.
- Correlation of metabolite levels with clinical response and survival data.
Main Results:
- Amino acid metabolism is critical; histidine (His) correlates with favorable outcomes, while homocysteine (HCys), phenylalanine (Phe), and sarcosine (Sar) indicate poor response.
- The ratio His/(HCys+Phe+Sar) serves as a robust predictive biomarker.
- Elevated lactate levels and specific bile acids (GCDCA, TLCA) are associated with treatment response; TLCA enhances T cell activation and anti-tumor immunity.
- Gut microbiota, particularly the Eubacterium genus, may influence TLCA levels and ICI response.
Conclusions:
- Metabolic profiling reveals key biomarkers in amino acid, glycolysis, and bile acid pathways for ICI therapy in NSCLC.
- Modulating these metabolic pathways holds potential for predicting and enhancing ICI efficacy.
- Findings support personalized treatment strategies in immuno-oncology based on metabolic profiles.
