Nucleic acid therapeutics as differentiation agents for myeloid leukemias
Bibliographic record
Abstract
Differentiation therapy has proven to be a success story for patients with acute promyelocytic leukemia. However, the remaining subtypes of acute myeloid leukemia (AML) are treated with cytotoxic chemotherapies that have limited efficacy and a high likelihood of resistance. As differentiation arrest is a hallmark of AML, there is increased interest in developing differentiation-inducing agents to enhance disease-free survival. Here, we provide a comprehensive review of current reports and future avenues of nucleic acid therapeutics for AML, focusing on the use of targeted nucleic acid drugs to promote differentiation. Specifically, we compare and discuss the precision of small interfering RNA, small activating RNA, antisense oligonucleotides, and aptamers to modulate gene expression patterns that drive leukemic cell differentiation. We delve into preclinical and clinical studies that demonstrate the efficacy of nucleic acid-based differentiation therapies to induce leukemic cell maturation and reduce disease burden. By directly influencing the expression of key genes involved in myeloid maturation, nucleic acid therapeutics hold the potential to induce the differentiation of leukemic cells towards a more mature and less aggressive phenotype. Furthermore, we discuss the most critical challenges associated with developing nucleic acid therapeutics for myeloid malignancies. By introducing the progress in the field and identifying future opportunities, we aim to highlight the power of nucleic acid therapeutics in reshaping the landscape of myeloid leukemia treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".