Following your gut: the emerging role of the gut microbiota in predicting and treating immune-related adverse events
Bibliographic record
Abstract
PURPOSE OF REVIEW: Although immune checkpoint inhibition has reshaped the therapeutic landscape leading to improved outcomes across an array of both solid and hematologic malignancies, a significant source of morbidity is caused by immune-related adverse events (irAEs) caused by these agents. RECENT FINDINGS: The gut microbiota has emerged as a biomarker of response to these agents, and more recently, also as a key determinant of development of irAEs. Emerging data have revealed that enrichment of certain bacterial genera is associated with an increased risk of irAEs, with the most robust evidence pointing to an intimate connection with the development of immune-related diarrhea and colitis. These bacteria include Bacteroides , Enterobacteriaceae, and Proteobacteria (such as Klebsiella and Proteus ) . Lachnospiraceae spp. and Streptococcus spp. have been implicated irAE-wide in the context of ipilimumab. SUMMARY: We review recent lines of evidence pointing to the role of baseline gut microbiota on the development of irAE, and the potentials for therapeutic manipulation of the gut microbiota in order to reduce irAE severity. The connections between gut microbiome signatures of response and toxicity will need to be untangled in further studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".