Gut microbiome composition to predict resistance in renal cell carcinoma (RCC) patients on nivolumab.
Bibliographic record
Abstract
4519 Background: Efforts are ongoing to identify mechanisms driving response/resistance to immune checkpoint inhibitors (ICI) in order to personalize therapy. Recently, we speculated that antibiotics (ATB)-related dysbiosis decreases activity of ICI in cancer pts. We and others reported that outcome with ICI in melanoma and epithelial cancers were influenced by the microbiome composition. Here we evaluated the impact of microbiome in RCC. Methods: Within a large cohort of RCC pts (n = 85) treated in the NIVOREN study with nivolumab at Gustave Roussy, we prospectively collected fecal samples (n = 69). Of note, the minority of them received ATB before starting ICI (n = 11). Pts were classified as either primary resistant (PD) or non-PD based on RECIST (outcome, 6 months PFS). Metagenomic (MG) data from whole genome sequencing (WGS) were analyzed by multivariate and pair-wise/fold ratio (FR). Then, ICI-resistant RENCA mice were compensated with fecal microbiota transplantation (FMT) from non-PD pts or with commensals identified by WGS-MG to restore responsiveness to ICI to establish cause-effect relationship between dysbiosis and resistance. Results: After a median follow-up of 14 months, 27 (39%) pts were PD and 42 (61%) pts were non-PD, based on best response. Considering pts who received ATB, 8 (73%) were PD and 3 (27%) were non-PD (p = 0.01). The microbiome alfa (intra-sample) and beta (inter-sample) diversity were not significantly different among PD and non-PD RCC pts. However, specific gut MG-fingerprints were related to best responses and/or PFS6. Excluding ATB treated patients, Akkermansia muciniphila and Bacteroides salyersiae were more abundant in non-PD pts with a FR of 2.65 (p = 0.01) and 27.09 (p = 0.05), respectively. Finally, we showed that Bacteroides (B. salyersiae but not B. xylanisolvens) or A. muciniphila could restore the efficacy of ICI in
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".