Abstract B67: Analysis of function and inhibition of PGE2 pathway members MRP4 and EP4 in treatment of ovarian cancer
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».