<i>Helicobacter pylori</i> infection has a detrimental impact on the efficacy of cancer immunotherapies
Bibliographic record
Abstract
Objective In this study, we determined whether Helicobacter pylori ( H. pylori ) infection dampens the efficacy of cancer immunotherapies. Design Using mouse models, we evaluated whether immune checkpoint inhibitors or vaccine-based immunotherapies are effective in reducing tumour volumes of H. pylori -infected mice. In humans, we evaluated the correlation between H. pylori seropositivity and the efficacy of the programmed cell death protein 1 (PD-1) blockade therapy in patients with non-small-cell lung cancer (NSCLC). Results In mice engrafted with MC38 colon adenocarcinoma or B16-OVA melanoma cells, the tumour volumes of non-infected mice undergoing anticytotoxic T-lymphocyte-associated protein 4 and/or programmed death ligand 1 or anti-cancer vaccine treatments were significantly smaller than those of infected mice. We observed a decreased number and activation status of tumour-specific CD8 + T cells in the tumours of infected mice treated with cancer immunotherapies independent of the gut microbiome composition. Additionally, by performing an in vitro co-culture assay, we observed that dendritic cells of infected mice promote lower tumour-specific CD8 + T cell proliferation. We performed retrospective human clinical studies in two independent cohorts. In the Dijon cohort, H. pylori seropositivity was found to be associated with a decreased NSCLC patient survival on anti-PD-1 therapy. The survival median for H. pylori seropositive patients was 6.7 months compared with 15.4 months for seronegative patients (p=0.001). Additionally, in the Montreal cohort, H. pylori seropositivity was found to be associated with an apparent decrease of NSCLC patient progression-free survival on anti-PD-1 therapy. Conclusion Our study unveils for the first time that the stomach microbiota affects the response to cancer immunotherapies and that H. pylori serology would be a powerful tool to personalize cancer immunotherapy treatment.
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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 | 0.000 |
| 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) | 0.001 | 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".