Preclinical Determination of the Efficacy of Epigenetic Therapy in High Risk Myeloid Disease Using the Zebrafish Model
Bibliographic record
Abstract
Abstract Epigenetic therapy implies the use of drugs that target regulators of gene expression, such as DNA and histone methylation or histone acetylation, without altering the DNA coding sequence. Epigenetic mechanisms have been found to be perturbed in myeloid diseases, and in fact, the DNA demethylating agents, 5-azacytadine and decitabine are FDA approved drugs for myelodysplastic syndrome. However, use of epigenetic therapies in other types of myeloid disease including acute myeloid leukemia (AML) has been met with variable success, suggesting efficacy might be improved by preselecting subtypes of disease in which there is a high degree of epigenetic dysregulation. AML has recently been found to be associated with a host of epigenetic abnormalities including mutations in DNA methyltransferase 3A (DNMT3A), ten-eleven translocation-2 (TET2) and enhancer of zeste homolog 2 (EZH2). We generated a transgenic zebrafish model of high risk myeloid disease expressing the human NUP98-HOXA9 (NHA9) fusion oncogene, a genetic lesion for which epigenetic dysregulation has not previously been identified. Transgenic embryos exhibit an increase in immature myeloid cells at the expense of erythroid cells and adult fish develop a myeloproliferative neoplasm (MPN). Leveraging this model in a microarray screen, we identified 3-fold elevated levels of the epigenetic regulator, dnmt1, the major maintenance methyltransferase, for the first time in high risk AML. Decitabine specifically inhibits DNMT1 and treatment of NHA9 transgenic embryos with 75µM decitabine restored normal hematopoiesis, as evidenced by normal numbers of leukocytes and red cells. Moreover, using hematopoietic stem cell (HSC) reporter lines and whole mount in situ hybridization, we identified a 2-3 fold increase in this population in NHA9 embryos, suggesting the HSC as the cell of origin in this disease. Interestingly, 75µM decitabine therapy also restored normal HSC numbers. Strikingly, we discovered synergy when we combined sub-monotherapeutic doses of DNMT1 inhibitors, decitabine (10-25µM) or zebularine (100µM), plus histone deacetylase inhibitors, valproic acid (25-100µM) or trichostatin A (250nM), to inhibit the effects of NHA9 on hematopoiesis. To determine if NHA9 expression directly results in changes to DNA methylation, we performed MeDIP-Seq on genomic DNA from pools of untreated and decitabine-treated NHA9 or control embryos. Untreated NHA9 embryos displayed significantly higher methylation levels at the regions of gene promoters compared to control embryos, which were restored to control levels following treatment with 75µM decitabine. Similarly, combination therapy with 10µM decitabine and 25µM valproic acid significantly reduced methylation to near control levels following a 5 hour treatment, while prolonged exposures to these same doses resulted in profound global hypomethylation. These findings elucidate underlying mechanisms in the pathogenesis of NHA9-induced myeloid disease and propose novel actionable epigenetic drug targets. Furthermore, we highlight the opportunities inherent in the zebrafish model as a unique in vivo platform for the preclinical screening of epigenetic based combination therapy. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".