Targeting PLZF Enhances Glucocorticoid Sensitivity in Acute Myeloid Leukemia
Notice bibliographique
Résumé
Glucocorticoids (GCs) are Glucocorticoid Receptor (GR) agonists that are extensively used in clinic, from management of autoimmune and inflammatory disorders to being the corner stone in treatment of adult and childhood lymphoid cancers. Unlike lymphoblastic leukemias, acute myeloid leukemia (AML) cells are generally resistant to glucocorticoids and the mechanisms underlying this resistance are yet to be elucidated. Recent efforts by our group and others suggest that certain genetic alterations, such as RUNX1 loss of function mutations, or therapy-induced stress by chemo-agents such as cytarabine or small molecules like FLT3-inhibitors could sensitize AML cells to GCs, most probably via up-regulation of glucocorticoid receptor (GR) expression. To better characterize GC-response in AML, we compared gene expression landscape of GC-resistant versus GC-sensitive AML cell lines exposed to dexamethasone (Dex) for different time points. Interestingly, we found that the number of down-regulated genes upon GC treatment is similar in both cell lines. However, GC-sensitive cell line showed 3 to 6 times more up-regulated genes following GC treatment compared to the resistant cell line. A significant portion of up-regulated genes in the sensitive cells were targets of GR transcription factor. Gene set enrichment analysis revealed up-regulation of genes associated with macrophage differentiation, inflammation, and apoptosis pathways only in the sensitive cells. To identify modulators of the GC response in AML, we conducted a genome-wide CRISPR/Cas9-based genetic screen on resistant cell lines in the presence or absence of Dex. We identified the promyelocytic leukemia zinc factor (PLZF) as a critical transcription factor that determines GC-response in AML cells. In line with this, we showed that PLZF knockdown conferred sensitivity to GC-resistant AML cells, whereas PLZF over-expression conferred resistance to the sensitive cells. Transcriptome analysis showed an overlap of up-regulated pathways between PLZF-depleted cells and GC-sensitive cells in presence of Dex including inflammation and apoptosis pathways, suggesting that PLZF depletion primes AML cells to GC-response. Furthermore, Dex treatment induced expression of a subset of GR target genes in PLZF depleted cells. Importantly, this effect was not associated with significant changes in GR global protein levels or sub-cellular distribution. PLZF has been recently shown to be a neosubstrate for Cereblon-mediated protein degradation. To define potential therapeutic strategies that target PLZF in AML, we selected two cereblon modulating molecules including Lenalidomide, which is commonly used in the clinic for treatment of hematological malignancies, and CC-3060 compound that is more potent in PLZF degradation. Combination of Dex with PLZF-degrading compounds increased cell death in a significant portion of 95 primary AML samples tested, and as expected, a broader synergistic effect was obtained with CC-3060 compound. Combination of Dex and Lenalidomide appeared more effective in samples harboring NPM1 mutation, independently of recurrent additional mutations of FLT3 or DNMT3A. Importantly, these combinations had no cytotoxicity effect on normal CD34+ cordblood cells in vitro. Current efforts are ongoing to assess efficacy of this newly identified combinatorial therapy in vivo. All together our study shows that GR trans-activating capacity is diminished in resistant AML cells leading to a failure in activating pro-differentiation and anti-leukemic genes upon Dex treatment. PLZF interferes with GC-induced cell death in AML cells at least in part via suppressing 100 of genes involved in GC-response. PLZF suppression induces inflammatory response and sensitizes AML cells to anti-inflammatory effects of glucocorticoids. Based on our results, potent PLZF degrading compounds hold promises to potentiate GC-response in AML.
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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 ».