Host and Direct Antitumor Effects and Profound Reduction in Tumor Metastasis with Selective EP4 Receptor Antagonism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Prostaglandin E(2) (PGE(2)), one of the major metabolites of cyclooxygenase-2, has been implicated in tumorigenesis and tumor progression in several human cancers, including colorectal and lung. Here, we show that one of the PGE(2) receptors, the EP4 receptor, plays an important role in metastasis in both of these tumor types. Using i.v. injected Lewis lung carcinoma (3LL), we found that tumor metastasis to lung was significantly reduced when mice were treated with a specific EP4 antagonist ONO-AE3-208 or when EP4 receptor expression was knocked down in the tumor cells using RNA interference technology. Host EP4 receptors also contributed to tumor metastasis and tumor growth with decreased metastasis and tumor growth observed in EP4 receptor knockout animals. In vitro tumor cell adhesion, motility, invasion, colony formation, and Akt phosphorylation were all significantly inhibited when 3LL cells were treated with the EP4 receptor-specific antagonist. When the cells were treated with an EP4-specific agonist (AE1-734), we observed a worsening of these same features in vitro. Treatment with ONO-AE3-208 also profoundly decreased liver metastases after intrasplenic injection of MC26 colon cancer cells. Our data show that selective antagonism of EP4 receptor signaling results in a profound reduction in lung and colon cancer metastasis. Selective antagonism of the EP4 receptor may thus represent a novel therapeutic approach for the treatment of cancer and especially its propensity to metastasize.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it