Impact of Proton Pump Inhibitor Use on Progression-Free and Overall Survival in Cancer Patients Undergoing Immune Checkpoint Inhibitor Therapy: A Systematic Review and Meta-Analysis of Recent Studies
Bibliographic record
Abstract
Background: The introduction of immunotherapy has significantly improved survival outcomes in many solid tumors. However, a subset of patients exhibits limited responsiveness to immune checkpoint inhibitors (ICIs). Emerging evidence indicates that the gut microbiota plays a critical role in modulating the effectiveness of immunotherapy. Consequently, the concurrent use of certain medications that disrupt microbial diversity may contribute to reduced treatment efficacy. Among the agents implicated in altering the gut microbiota are antibiotics and proton pump inhibitors (PPIs). Methods: A systematic literature search was conducted in PubMed, Scopus, and EMBASE. Eligible studies assessed the association between PPI use and progression-free survival (PFS) and/or overall survival (OS) in patients with solid tumors receiving ICIs. They reported hazard ratios (HRs) with 95% confidence intervals (CIs). The analysis focused on studies published between November 2022 and January 2025, in continuity with prior comprehensive meta-analyses that included studies up to November 2022. This contiguity-based approach enabled a focused evaluation of recent evidence, minimizing redundancy while allowing for the detection of evolving trends in clinical practice and methodology. Data were synthesized using both fixed-effects and random-effects models and visualized via Forest plots. Study quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) and the Newcastle–Ottawa Scale (NOS). Between-study heterogeneity and publication bias were evaluated using I2 statistics and funnel plots. Results: From a pool of over 400 screened articles between November 2022 and January 2025, seven studies met the inclusion criteria. The PFS analysis incorporated data from 1367 participants, while the OS analysis included 10,420 individuals. Use of PPIs was linked to a 12% higher risk of disease progression (HR = 1.12; 95% CI: 0.90–1.34) and an 18% increased mortality risk (HR = 1.18; 95% CI: 1.11–1.25). Conclusions: The observed association between PPIs exposure and reduced efficacy of ICIs, as reflected in worsened PFS and OS outcomes, highlights a potential clinical concern that merits further investigation in prospective studies.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".