The negative impact of antibiotics on outcomes in cancer patients treated with immunotherapy: a new independent prognostic factor?
Bibliographic record
Abstract
Immune-checkpoint inhibitors (ICI) now represent the standard of care for several cancer types. In pre-clinical models, absence of an intact gut microbiome negatively impacted ICI efficacy and these findings permitted to unravel the importance of the commensal microbiota in immuno-oncology. Recently, multiple clinical studies including more than 1800 patients in aggregate demonstrated the negative predictive impact of treatments with broad-spectrum antibiotics (ATB) on cancer patients receiving ICI. Altogether, these results have led to the hypothesis that ATB-induced dysbiosis might influence the clinical response through the modulation of the gut microbiome. Controversy still remains, as ATB treatment might simply constitute a surrogate marker of unfit or immunodeficient patients. In this review, we summarize recent publications addressing the impact of the gut microbiome on ICI efficacy, discuss currently available data on the effect of ATB administered in different time-frames respect to ICI initiation, and finally, evoke the therapeutic implications of these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".