Gut microbiotas and immune checkpoint inhibitor therapy response: a causal or coincidental relationship?
Bibliographic record
Abstract
As the largest immune organ, human gut microbiome could influence the efficacy of immune checkpoint inhibitor therapy (ICI). However, identifying contributory microbes from over 35,000 species is virtually impossible and the identified microbes are not consistent among studies. The reason for the disparity may be that the microbes found in feces are markers of other factors that link immune response and microbiotas. Notably, gut microbiome is influenced by stool consistency, diet and other lifestyle factors. Therefore, the ICI and microbiotas relationship must be adjusted for potential confounders and analyzed longitudinally. Moreover, a recent study where 11 low-abundance commensal bacteria induced interferon-γ-producing CD8 T cells, challenges the validity of the abundance-oriented microbiotas investigations. This study also confirmed the hierarchy in immunogenic roles among microbiotas. Fecal transplantation trials in germ-free mice provided "the proof of principle" that germ-free mice reproduce the donor's microbiome and corresponding ICI efficacy. However, species-specific biological differences prevent direct extrapolation between the results in murine and human models. Fecal transplantation or supplementation with microbes found in ICI responders requires caution due to potential adverse events.
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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 | 0.001 |
| 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 | 0.001 |
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".