Targeting PLZF Enhances Glucocorticoid Sensitivity in Acute Myeloid Leukemia
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
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".