Bacterial tryptophan metabolites in cancer and atherosclerosis: insights for a role in immune checkpoint inhibition
Bibliographic record
Abstract
The gut microbiota plays a pivotal role in human health, partly through the production of bioactive metabolites from dietary tryptophan. These indole derivatives have emerged as key modulators of immune function, inflammation, and metabolic health and have been linked to various diseases. In the context of cancer, indole derivatives are increasingly being studied as promising modulators of immune checkpoint inhibitor (ICI) therapy, with accumulating evidence indicating potential for various derivatives to enhance therapeutic efficacy. ICI therapy is associated with various immune-related adverse events, including accelerated progression of atherosclerotic cardiovascular disease. Given their immunomodulatory properties, there is a growing interest in the usage of indole metabolites to mitigate these cardiovascular complications. This mini-review summarizes current knowledge on the roles of microbiota-derived indoles in cancer, ICI therapy, and atherosclerosis. Though direct evidence linking bacterial tryptophan-derived metabolites to ICI-associated atherosclerosis is currently lacking, accumulating evidence indicates that indole derivatives regulate pathways involved in both anti-tumor immunity and atherosclerosis. Advancing our understanding of how the microbiome and its metabolites influence both cancer and cardiovascular disease will be crucial for developing personalized, metabolite-based strategies to improve outcomes in patients undergoing ICI therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".