Mega- and meta-analyses of fecal metagenomic studies in predicting response to immune checkpoint inhibitors.
Bibliographic record
Abstract
2570 Background: A number of studies have demonstrated that the gut microbiome of responders to immune checkpoint inhibitors (ICI) is compositionally different compared to that of non-responders. However, differences in study design, patient cohorts and bioinformatic analyses make it challenging to identify bacterial species consistently associated with response to ICI across different cohorts and cancer types. Methods: We leveraged the statistical power of mega- and meta-analyses to identify bacterial species consistently associated with response to ICI using data from three published fecal metagenomic studies (Gopalakrishnan et al., Science 2018; Matson et al., Science 2018; Routy et al., Science 2018). Metagenomic data was uniformly processed and analyzed using Metaphlan v2.0. We conducted a two-part modelling approach of bacterial species present in at least 20% of samples to account for both prevalence and relative abundance differences between responders/non-responders. Results: A total of 190 patients (n = 103 responders; n = 87 non-responders) were included from the three studies. Data from Routy et al., was analyzed as subsets based on tumor type for a total of 4 analyzed cohorts. We identified five species including Bacteroides thetaiotaomicron, Clostridium bolteae, Holdemania filiformis, Clostridiaceae bacterium JC118 and Escherichia coli that were concordantly significantly different between responders and non-responders using both meta- and mega-analyses. B. 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: Despite inter-cohort heterogeneity in tumor type, treatment regimens, and sequencing modalities, meta- and mega analysis of published metagenomic studies identified generalizable bacterial species associated with ICI response or lack thereof. B. thetaiotaomicron and C. bolteae were predictors of non-response to ICI suggesting the clinical potential of narrow spectrum anti-biotics targeting non-response associated bacterial species to improve outcomes in ICI recipients.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".