Targeting Oncoprotein Translation with Rocaglates in MYC-Driven Lymphoma
Notice bibliographique
Résumé
Abstract Background: c-MYC (MYC) is commonly dysregulated in aggressive B cell lymphomas. MYC associated lymphoma, especially Double Hit lymphoma (DHL) and Double-Expression Lymphoma (DEL) which are characterized by MYC and BCL2 dual overexpression usually present with the inferior outcome as rapid disease progression and poor response to standard chemotherapy regimen. Nevertheless, MYC is considered as an "undruggable" target and targeting strategies such as suppressing MYC transcription by bromodomain (BRD)-4 inhibitors have been widely investigated in both preclinical models and clinical trials. However, increasing evidence has shown that lymphoma cells displayed a wide range of resistance to BRD-4 inhibition, due to transcription adaptation or kinome reprogramming. Hence, alternative approaches for suppressing MYC or its function are urgently needed. Strategies directed against oncoprotein translation may efficiently repress key oncoproteins regardless of the abundant MYC mRNA due to genetic aberrations and secondary transcription up-regulated by MYC. Rocaglate is a class of natural products derived from plants of the Aglaia genus that have been demonstrated to potently inhibit protein translation initiation via eIF4A. The use of rocaglates for anti-cancer treatment was limited due to the scarcity and instability of these natural products e.g. Silvestrol. Recent chemical modification and screening studies have unveiled a few synthetic rocaglates that are more potent than Silvestrol such as SDS-1-021-(−), which unlocked the potential use of rocaglates for clinical applications. Methods and Results: To probe effective reagents for MYC-driven lymphoma, a screening of 50 drugs targeting common oncogenic pathways and tumorigenic machinery was performed in two isogenic B-lymphoma lines. Several protein translation inhibitors, such as mTOR kinase inhibitors (TORKi), and eIF4A inhibitor Silvestrol were identified as the most potent drugs in all of the tested cells. Further, we found that Silvestrol but not TORKi efficiently repressed MYC protein translation in MYC-driven B lymphoma cells, whereas neither of them inhibited BCL2 expression. Moreover, we demonstrated that eIF4E knockdown or eIF4E/eIF4G disruptor Briciclib did not significantly affect MYC expression, whereas eIF4A inhibitor hippuristanol and rocaglates derivate SDS-1-021-(−) diminished MYC expression similar to that observed in Silvestrol treated cells. By using dual luciferase assay, we demonstrated that rocaglates had stronger cap-dependent and IRES translation inhibition than TORKi in lymphoma cells. Next, by native RNA immunoprecipitation and siRNA knocking down, we found that rocaglates repressed MYC translation not only via eIF4A1 but also via eIF4A2, however, with different underlying mechanisms. Furthermore, to explore the molecular targets of rocaglates treatment in B cell lymphoma, we performed TMT-Mass Spectrometry which identified multiple oncoproteins including NEK2, MYC, MCL1, TCF3, BCL6, PLK1, AURKA, and WEE1 were significantly down-regulated by SDS-1-021-(−) treatment. Finally, we demonstrated that SDS-1-021-(−) is highly potent as a single agent and synergized with ABT199 at a low dose (0.2mg/kg) in PDX models with DHL/DEL. Brief summary : Our pre-clinical study provided strong evidence that rocaglates but not TORKi efficiently suppress MYC protein translation because 1). Rocaglates exhibit strong inhibition on both Cap- and IRES-dependent translation, 2). Rocaglates decrease PLK1 and AURKA/B thus destabilizing MYC protein. The synthetic rocaglate SDS-1-021-(−) is a potent agent that exhibits significant synergistic killing effect with ABT199 on DHL/DEL cells in the pre-clinical animal study. Figure Figure. Disclosures Lunning: TG Therapeutics: Consultancy; AbbVie: Consultancy; Genentech: Consultancy; Astra-Zeneca: Consultancy; Genzyme: Consultancy; Celgene: Consultancy; Bayer: Consultancy; Gilead: Consultancy; Spectrum: Consultancy; Genentech: Consultancy; Seattle Genetics: Consultancy; Portola: Consultancy; Kite: Consultancy; Juno: Consultancy; Janssen: Consultancy; Verastem: Consultancy. Vose:Novartis: Honoraria, Research Funding; Epizyme: Honoraria; Incyte Corp.: Research Funding; Bristol Myers Squibb: Research Funding; Kite Pharma: Research Funding; Merck Sharp & Dohme Corp.: Research Funding; Abbvie: Honoraria; Legend Pharmaceuticals: Honoraria; Seattle Genetics, Inc.: Research Funding; Acerta Pharma: Research Funding; Celgene: Research Funding; Roche: Honoraria.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».