Cyclooxygenase-2 Inhibition Potentiates the Efficacy of Vascular Endothelial Growth Factor Blockade and Promotes an Immune Stimulatory Microenvironment in Preclinical Models of Pancreatic Cancer
Bibliographic record
Abstract
Abstract Resistance to standard therapy remains a major challenge in the treatment of pancreatic ductal adenocarcinoma (PDA). Although anti-VEGF therapy delays PDA progression, therapy-induced hypoxia results in a less differentiated mesenchymal-like tumor cell phenotype, which reinforces the need for effective companion therapies. COX-2 inhibition has been shown to promote tumor cell differentiation and improve standard therapy response in PDA. Here, we evaluate the efficacy of COX-2 inhibition and VEGF blockade in preclinical models of PDA. In vivo, the combination therapy was more effective in limiting tumor growth and metastasis than single-agent therapy. Combination therapy also reversed anti-VEGF–induced epithelial–mesenchymal transition and collagen deposition and altered the immune landscape by increasing tumor-associated CD8+ T cells while reducing FoxP3+ T cells and FasL expression on the tumor endothelium. Implications: Together, these findings demonstrate that COX-2 inhibition enhances the efficacy of anti-VEGF therapy by reducing hypoxia-induced epithelial plasticity and promoting an immune landscape that might facilitate immune activation. Visual Overview: http://mcr.aacrjournals.org/content/molcanres/17/2/348/F1.large.jpg.
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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) | 0.001 | 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) | 0.004 | 0.001 |
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".