The Implications of DNMT Mutations and the Prognostic and Therapeutic Relevance of DNMTis in AML: A Literature Review
Bibliographic record
Abstract
Introduction: Acute myeloid leukemia (AML) is a highly heterogeneous and aggressive form of blood cancer characterized by the halted differentiation and proliferation of hematopoietic stem cells (HSCs). Normal hematopoietic functioning is regulated by DNA methyltransferase (DNMT) enzymes which modify the DNA epigenetic landscape. DNMT malfunction is associated with AML, therefore, DNMT inhibitors (DNMTis), such as azacytidine, are being investigated as a potential treatment option for AML patients. This literature review aims to identify the implications of DNMT mutations in AML and the therapeutic value of DNMTis. Methods: A literature search was conducted using databases including the York University library and PubMed using keywords such as “AML”, “DNMT”, “DMNTi”. Studies were restricted to publication dates between 2010 to 2024. Results: DNMT3A mutations, specifically at Arginine 882, are common amongst AML patients. Additionally, ten-eleven translocation methylcytosine dioxygenase 2 (TET2) mutations correlate with AML incidence. Reduced catalytic activity of DNMTs caused by mutations can cause hypomethylation and increased gene transcription or hypermethylation and decreased gene transcription. Depending on the patient genome and responsiveness, DNMTis promote normal cell functioning in malignant cells. Aberrant HSC clonal expansion and proliferation within the bone marrow leads to dysregulated hematopoiesis. This characteristic of AML is correlated with DNMT mutations. Discussion: DNMTis have high therapeutic potential because of their ability to reverse aberrant DNMT methylation patterns while having synergistic effects alongside other treatments. Also, DNA methylation pattern sequencing, such as chromatin accessibility studies, can be useful as predictive biomarkers for AML. The research limitations include navigating the complexity of AML and the variability of responses to DNMTi therapies. Future research should investigate patient biomarkers which could tailor treatment options. Conclusion: The mortality and complexity of AML warrant further investigation into its underlying causes and potential treatments. As a combinatorial and generally well-tolerated treatment, DNMTis are highly promising. Genomic testing that includes methylation level assessment is vital in appropriately detecting biomarkers that can direct patient treatment plans.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".