20MO Hallmarks of gut health influence toxicity and survival outcomes after immune-checkpoint inhibitors
Bibliographic record
Abstract
Background: Immune-related adverse events (irAEs) are side effects related to ICI, varying in severity, onset time and organ involvement.Recent studies showed the gut microbiome (GM) involvement in the risk of irAEs.In this study, we combined host and microbial metagenomics (MGS) to explore the influence of GM on treatment response and risk of irAEs.Methods: NCT04567446 provided MGS in patients (pts) with advanced non-small cell lung cancer, renal cell carcinoma and bladder cancer (n=613) treated with ICI alone (ICI cohort, n=529 pts) or in combination with chemotherapy (CT+ICI cohort, n=59) in France and Canada across the ClinicObiome project.Pts who experienced severe (≥ grade 3) irAEs after ICI+/-CT were compared to those who did not using microbial MGS parameters (diversity, PCoA and LEfSe).The host exfoliome (i.e., human reads in stool samples, estimated via MGS) association with overall survival (OS) using multivariate Cox regression models.Results: The GM of Pts with severe irAEs (13.3%) was less diverse and distinct compared to patients without severe irAEs (n=775 samples), before and during irAEs (n=125 samples), notably with an overrepresentation of several members of the Prevotellaceae family and Enterocloster spp.Interestingly, CT+ICI pts with severe irAEs had the most dysbiotic GM, characterized by a lower diversity and dominated by tolerogenic species.After the advent of irAE, the GM exhibits reduced diversity, with a distinct GM dominated by Prevotella spp, and a burst of Escherichia coli, a biomarker of inflammation.Prevotella copri B was associated to an increased overall survival of pts.Conclusions: Host-microbial interactions influence immunity and therefore ICI prognosis and irAEs.We found Prevotellaceae members as potential biomarkers for irAEs, as well as survival.The host exfoliome is an interesting parameter that may reflect gut fitness, requiring further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".