Countering tumor-derived PGE2-EP2/4 signaling mediates neutrophil activation
Bibliographic record
Abstract
Neutrophils have recently been demonstrated to play a variable role in cancer progression, likely due to adaptability to environmental signals that influence neutrophil phenotype and function. In the tumor microenvironment, tumor-derived prostaglandin E2 is well established as a major suppressor of immune cell function and facilitates tumor immune evasion. However, while prostaglandin E2 is widely regarded to signal through the E-prostanoid receptors type (EP) 2 and EP4 on neutrophils, the exact effects of targeting this axis therapeutically in the context of cancer remain largely unexplored. In this study, we investigated the effect of prostaglandin E2 signaling via EP2/4 in human neutrophils using specific EP2 and EP4 antagonists. We report that countering EP2/4 signaling yields an activated phenotype together with the acquisition of proinflammatory properties, including the augmented production of reactive oxygen species, enhanced phagocytosis, and increased production of IL-8. Given the phagocytic nature of neutrophils, we encapsulated EP2/4 antagonists into poly (lactic-co-glycolic acid) nanoparticles for the tailored delivery of these antagonists. Importantly, nanoparticles achieved robust phenotypical and functional activation of healthy neutrophils as well as cancer patient-derived neutrophils exposed to tumor-derived prostaglandin E2. Together, these results identify the role of EP2/4 in regulating activation of human neutrophils and illustrate the relevance of targeting this axis with nanoparticles in the context of cancer for the reactivation of tumor-exposed neutrophils. We propose that targeting this axis in neutrophils using nanoparticles can serve to attenuate tumor-induced suppression by steering them toward a proinflammatory state and thus potentially support antitumor immune responses.
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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".