Mega- and meta-analyses of fecal metagenomic studies assessing response to immune checkpoint inhibitors
Bibliographic record
Abstract
Abstract Purpose Gut microbiota have been associated with response to immune checkpoint inhibitors (ICI) including anti-PD-1 and anti-CTLA-4 antibodies. However, inter-study difference in design, patient cohorts and data analysis pose challenges to identifying species consistently associated with response to ICI or lack thereof. Experimental Design We uniformly processed and analyzed data from three studies of microbial metagenomes in cancer immunotherapy response (four distinct data sets) to identify species consistently associated with response or non-response (n=190 patient samples). Metagenomic data were processed and analyzed using Metaphlan v2.0. Meta- and mega-analyses were performed using a two-part modelling approach of species present in at least 20% of samples to account for both prevalence and relative abundance differences between responders/non-responders. Results Meta- and mega-analyses identified five species that were concordantly significantly different between responders and non-responders. Amongst them, Bacteroides thetaiotaomicron and Clostridium bolteae relative abundance (RA) were independently predictive of non-response to immunotherapy when data sets were combined and analyzed using mega-analyses (AUC 0.59 95% CI 0.51-0.68 and AUC 0.61 95% CI 0.52-0.69, respectively). Conclusions Meta- and mega-analysis of published metagenomic studies identified bacterial species both positively and negatively associated with immunotherapy responsiveness across four published cohorts.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".