Gut Microbiota in Immuno-Oncology: A Practical Guide for Medical Oncologists With a Focus on Antibiotics Stewardship
Bibliographic record
Abstract
The gut microbiota has emerged as a critical determinant of immune checkpoint inhibitor (ICI) efficacy, resistance, and toxicity. Retrospective and prospective studies profiling the taxonomic composition of intestinal microbes of patients treated with ICI have revealed specific gut microbial signatures associated with response. By contrast, dysbiosis, which can be caused by chronic inflammatory processes (such as cancer) or comedications, is a risk factor of resistance to ICI. Recent large-scale meta-analyses have confirmed that antibiotic (ATB) use before or during ICI therapy alters the microbiota repertoire and significantly shortens overall survival, even after adjusting for prognostic factors. These results underscore the importance of implementing ATB stewardship recommendations in routine oncology practice. Microbiota-centered interventions are now being explored to treat gut dysbiosis and optimize ICI responses. Early-phase clinical trials evaluating fecal microbiota transplantation (FMT) from ICI responders or healthy donors have shown that this approach is safe and provided preliminary data on potential efficacy to overcome both primary and secondary resistance to ICI in melanoma, non-small cell lung cancer, and renal cell carcinoma. More targeted interventions including live bacterial products including Clostridium butyricum and Akkermansia massiliensis represent novel microbiome-based adjunct therapies. Likewise, dietary interventions, such as high-fiber diets, have shown promise in enhancing ICI activity. In this ASCO Educational Book, we summarize the current state-of-the-evidence of the clinical relevance of the intestinal microbiota in cancer immunotherapy and provide a practical guide for ATB stewardship.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".