Enhanced Immunotherapeutic Efficacy of Anti–PD-L1 Antibody in Combination with an EP4 Antagonist
Bibliographic record
Abstract
Abstract Combination treatment approaches are increasingly considered to overcome resistance to immunotherapy targeting immunoinhibitory molecules such as programmed death (PD)–1 and PD-ligand 1 (PD-L1). Previous studies have demonstrated that the therapeutic efficacy of anti–PD-L1 Abs is enhanced by combination treatment with cyclooxygenase-2 inhibitors, through downregulation of the immunosuppressive eicosanoid PGE2, although the underlying mechanism remains unclear. In this study, we show that serum PGE2 levels are upregulated after anti–PD-L1 Ab administration in a bovine model of immunotherapy and that PGE2 directly inhibits T cell activation via its receptor E prostanoid (EP) 4. Additionally, anti–PD-L1 Ab induces TNF-α production and TNF-α blockade reduces PGE2 production in the presence of anti–PD-L1 Ab, suggesting that anti–PD-L1 Ab–induced TNF-α impairs T cell activation by PGE2 upregulation. Our studies examining the therapeutic potential of the dual blockade of PD-L1 and EP4 in bovine and murine immune cells reveal that the dual blockade of PD-L1 and EP4 significantly enhances Th1 cytokine production in vitro. Finally, we show that the dual blockade decreases tumor volume and prolongs survival in mice inoculated with the murine lymphoma cell line EG7. Altogether, these results suggest that TNF-α induced by anti–PD-L1 Ab treatment is associated with T cell dysfunction via PGE2/EP4 pathway and that the dual blockade of PD-L1 and EP4 should be considered as a novel immunotherapy for cancer.
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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 | 0.001 |
| 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".