Abstract 1766: Targeting epigenetic regulation in clear cell renal cell carcinoma reveals PRMT1 as a novel target
Notice bibliographique
Résumé
Abstract Sporadic Renal Cell Carcinoma (RCC) is dominated by the clear cell subtype (ccRCC) and overwhelmingly associated with a biallelic inactivation of the von Hippel-Lindau (VHL) gene, leading to constitutive activation of the hypoxia response and deleterious alterations to gene expression, metabolism and growth. However, VHL inactivation alone is insufficient to cause cancer. Other key genetic players identified through patient cohort sequencing include frequent inactivating mutations in epigenetic regulatory enzymes. The high frequency of these alterations in ccRCC implicate epigenetic vulnerabilities that may be exploited to develop new therapies.Accordingly, our lab has completed a proliferative screen in patient derived ccRCC models of the Structural Genomics Consortium's (SGC) epiprobe library, a panel of high-quality, small molecule inhibitors directed against an array of epigenetic targets. A particularly favorable inhibition profile was noted for MS023, a probe with potent activity against the type I protein arginine methytransferase family (PRMT1, 3, 4, 6 and 8). PRMTs transfer methyl groups to both nuclear histones and cytoplasmic targets, influencing gene expression, cell signaling, growth and viability. Specific PRMT3, 4 and 6 inhibitors failed to inhibit ccRCC cell growth in our screen, and PRMT8 is not expressed in this cell type, thus PRMT1 is the primary target for growth inhibition of ccRCC by MS023. Upon CRISPR-mediated knockout of PRMT1 followed by a growth competition assay of knock-out vs control cells, all PRMT1-knockout cells dropped out of culture within 4 passages. In ccRCC cell lines with tetracycline-inducible PRMT1 directed shRNAs, cell proliferation was inhibited upon induction with doxycycline both in vitro and in vivo. Additionally, PRMT1 over expression vectors introduced in our ccRCC cell lines, successfully rescued the proliferative phenotype of these cells in the presence of MS023 treatment. Finally, treatment of mice bearing ccRCC xenografts with MS023 led to significant inhibition of tumor growth in vivo. Transcriptomic profiling of our ccRCC models in the presence of MS023 vs control has been performed via RNA Seq and results suggest that this probe is acting as a potent regulator of the cell cycle. We are mapping changes in the H4R3me2a histone mark with MS023 treatment to complement our transcriptomic data and identify direct genetic targets regulated by PRMT1.Evidence continues to mount that dysregulation of PRMT1 is implicated in cancer biology, but to our knowledge, no investigations have been performed in the context of ccRCC. As our nascent understanding of the regulation, function and clinical relevance of arginine methylation continues to expand, this project represents an exciting opportunity to contribute to this body of knowledge, while describing novel therapeutic approaches to ccRCC with the potential for rapid clinical translation. Citation Format: Joseph Paul Walton, Anthony Apostoli, Jalna Meens, Julia Dmytryshyn, Cheryl Arrowsmith, Laurie AIlles. Targeting epigenetic regulation in clear cell renal cell carcinoma reveals PRMT1 as a novel target [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1766.
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) | 0,002 | 0,001 |
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 ».