Abstract B67: Analysis of function and inhibition of PGE2 pathway members MRP4 and EP4 in treatment of ovarian cancer
Bibliographic record
Abstract
Abstract Ovarian cancer has the highest mortality incidence of all gynecologic malignancies in the United States. The majority of ovarian cancer cases lead to recurrent disease that is often incurable and fatal due to innate or acquired chemoresistance; therefore, novel therapeutic interventions are desperately needed. Cyclooxygenases–COX-1 and COX-2–are enzymes that catalyze the production of prostaglandin E2 (PGE2), an important inflammatory lipid mediator that is functionally linked to progression of many cancers, including breast and ovarian cancer. PGE2 is exported from the cell via multidrug resistance-associated protein 4 (MRP4) where it acts in a paracrine and autocrine manner by activating a family of four G-protein coupled receptors (EP1-4) that are linked to different intracellular signaling pathways. EP2 and EP4 can activate PKA/cAMP, PI3K and ERK pathways. We hypothesize that the EP4 receptor has increased expression in ovarian cancer and that binding of its cognate ligand, PGE2, will drive ovarian cancer progression. We also hypothesize that alternation of the tumor microenvironment via MRP4 will also lead to inhibition of EP4-mediated signaling and affect phenotypes associated with ovarian cancer progression. In order to test this hypothesis, we analyzed the expression of the EP4 and MRP4 in a human ovarian cancer tissue microarray (TMA) as well as human ovarian cancer cell lines. Immunohistochemical analysis of EP4 on the TMA composed of varying histologies, including serous, endometrioid, and clear-cell, as well as normal ovarian tissue, revealed that EP4 was expressed in 38.7% of ovarian cancer tissues, whereas EP4 had no or low expression in 10 normal ovarian tissue samples. Immunohistochemistry of MRP4 also revealed increased expression in ovarian cancer histologies compared to normal ovarian tissue. Serous, endometrioid, and clear-cell subtypes presented with a majority of 4+ and 3+ staining intensities compared to normal ovarian tissue, which presented with mostly 2+ and 1+ staining, and none of the normal ovarian tissue presented with 4+ intensity. EP4 and MRP4 also has increased expression in multiple ovarian cancer cells lines including those representing low-grade serous, clear-cell, and high-grade serous ovarian cancer. Treatment of these cell lines with an EP4 antagonist resulted in decreased proliferation and migration compared to vehicle control. Consistent with the pharmacologic data, treatment of ovarian cancer cell lines with siRNA directed against the EP4 receptor led to decreased proliferation and migration. Inhibition of PGE2 export via MRP4 inhibitor Ceefourin and probenecid results in increased sensitization of ovarian cancer cell lines to treatment with paclitaxel. Based on these data, targeting of the PGE2 EP4 receptor and PGE2 export via MRP4 should be investigated further for the treatment of ovarian cancer. Citation Format: Jocelyn Reader, McMillan Ching, Cong (Ava) Fan, Sulan Wu, Paul Staats, Teklu Legesse, Olga Goloubeva, Ningbo Jian, Mark Carey, Amy Fulton, Dana Roque, Gautam Rao. Analysis of function and inhibition of PGE2 pathway members MRP4 and EP4 in treatment of ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B67.
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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) | 0.003 | 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".