Metabolic determinants of cancer immunotherapy outcomes identified by plasma profiling

Area of Science:

• Oncology
• Metabolomics
• Immunotherapy

Background:

• Immune-checkpoint inhibitors (ICIs) offer benefits to only a subset of advanced cancer patients.
• The metabolic factors influencing ICI response are not well understood.
• Identifying predictive biomarkers is crucial for optimizing cancer therapy.

Purpose of the Study:

• To identify metabolic and clinical predictors of progression-free survival in patients receiving ICIs.
• To develop a predictive model for treatment response using multimodal data.
• To explore the role of specific metabolites in antitumor immunity.

Main Methods:

• Analysis of 4,336 plasma samples from 1,714 patients across five cancer types and 16 cohorts.
• Utilized targeted metabolomics, metagenomics, and machine learning.
• Integrated 154 metabolites with clinical variables (age, BMI, renal function).

Main Results:

• A multimodal model identified five metabolites, age, BMI, and renal function as predictors of 12-month progression-free survival (AUC 0.88 in training, 0.73 in validation).
• Histidine was associated with favorable prognosis, while long-chain fatty acids and succinate were negatively associated with outcomes.
• Histidine supplementation enhanced anti-tumor immunity in mice, and histidine-rich diets improved outcomes in specific patient subgroups.

Conclusions:

• Metabolic profiling combined with clinical data can predict ICI response in advanced cancer.
• Histidine emerges as a potentially favorable prognostic metabolite, suggesting a role for dietary interventions.
• Further research into metabolic determinants could personalize cancer immunotherapy.