Impact of the Gut Microbiome on Immune Checkpoint Inhibitor Efficacy—A Systematic Review
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (icis) are increasingly being used in clinical practice, improving outcomes for cancer patients. Preclinical models showed significant interaction between the gut microbiome (gm) and response to icis. However, that interaction remains unclear in clinical practice. Methods: We performed a systematic review in medline to determine■ whether antibiotics affect ici efficacy,■ whether baseline gm composition and ici efficacy show any correlations,■ whether baseline gm composition and emergence of immune-related adverse events (iraes) show any correlations, and■ whether gm manipulation can alleviate the iraes.Included publications had to be written in English or French and had to describe a quantifiable link between gm composition or its modification and the response to icis or the occurrence of iraes, or both. Results: Of 1451 articles published before December 2018, 13 publications met the inclusion criteria. Five full-text articles and two abstracts highlighted a negative effect of antibiotics on ici efficacy. The composition of the gm was associated with ici efficacy in five full-text articles and one abstract, and with iraes in two full-text articles. In 2 cases, fecal microbiota transplantation was reported to reduce immune colitis. Conclusions: If possible, antibiotics should be avoided before ici treatment because of their negative effect on ici anticancer efficacy. No specific commensal bacterium was associated with ici efficacy, but an intact gm with high bacterial diversity and a good ratio of "responder-associated" bacteria to "non-responder-associated" bacteria seem to be correlated with better patient outcomes. Fecal microbiota transplantation is a promising technique for reducing ici-associated colitis.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".