P02.05 How proton pump inhibitors blunt immune checkpoint inhibitors efficacy: a role of the microbiome?
Bibliographic record
Abstract
<h3>Background</h3> While Immune checkpoint inhibitors (ICIs) are revolutionizing the management of many advanced cancers, several studies have reported that the gut microbiota composition may have an impact on ICI response. Antibiotics have been shown to alter the efficacy of immunotherapies, but other commonly used comedications known to interact with the microbiota might also impact the clinical benefit of those treatments. Clinical studies revealed that patients treated with ICI could be stratified into responder (R) or non-responder (NR) according to their microbiota composition. In a total of 635 patients with advanced cancer treated with anti PD-1, anti PD-L1 or anti CTLA-4 between 2015 and 2017 in Bordeaux, M. Kostine et al. described an association between the baseline use of co-medications, including proton pump inhibitors (PPIs), and a significantly shortened overall survival.<sup>1</sup> Our objectives are a) to address whether PPIs impact the ICI response, and b) to understand if the underlying mechanism involves the gut microbiota. <h3>Materials and Methods</h3> We first explored the impact of the PPI omeprazole on the composition of the microbiota in different segments of the gut. This was conducted in mice after long- or short-time exposure to omeprazole. In parallel, we explored omeprazole-induced changes in the intestinal transcriptome using bulk RNA sequencing of gut tissue segments. In other experiments, we interrogated the impact of omeprazole on anti-PD-1 efficacy in mice transplanted with different cancer cell-lines. Using 16S rDNA sequencing, we characterized both the gut as well as the local tumor microbiomes of R and NR mice. <h3>Results</h3> Our results revealed that omeprazole treatment resulted in a decrease of bacteria associated with a healthy gut and an expansion of oral bacteria and environmental pathobionts, consistent with published studies.<sup>2, 3</sup> Notably, omeprazole administration led to a striking reduction in Lachnospiraceae spp., which are enriched in the ‘microbiotype’ of ICI-responder patients.<sup>4</sup> Multi-omics integration of the gut microbiome and transcriptional data sets using weighted gene co-expression network analysis (WGCNA) identified omeprazole-induced transcriptional modules in the colon significantly associated with depletion or enrichment of specific microbiota components. From this integration, we will reconstruct the bacterial and host metabolic networks towards identifying metabolic signals linked to impaired anti-tumor immunity. <h3>Conclusions</h3> Collectively, our results present the impact of PPI on microbiome changes in tumor-bearing individuals and unravel potential mechanisms for intervention aimed at enhancing the anti-tumoral immune responses elicited by immunotherapies. <h3>References</h3> Kostine, M. <i>et al. Eur J Cancer</i> 2021;<b>157</b>:474–484. PMID: 34649118. Imhann, F. <i>et al. Gut</i> 2016;<b>65</b>:740–748. PMID: 26657899. Jackson, M. A. <i>et al. Gut</i> 2016;<b>65</b>:749–756. PMID: 26719299. McCulloch, J. A., <i>et al. Nat Med</i> 2022;<b>28</b>:545–556. PMID: 35228752. <h3>Disclosure Information</h3> <b>E. Ramel:</b> None. <b>M. Masson:</b> None. <b>D. Challopin:</b> None. <b>A. Barre:</b> None. <b>M. Nikolski:</b> None. <b>M. Kostine:</b> None. <b>M. Saleh:</b> None.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.085 | 0.004 |
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".