Differentiation therapy for myeloid malignancies: beyond cytotoxicity
Bibliographic record
Abstract
Blocked cellular differentiation is a central pathologic feature of the myeloid malignancies, myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). Treatment regimens promoting differentiation have resulted in incredible cure rates in certain AML subtypes, such as acute promyelocytic leukemia. Over the past several years, we have seen many new therapies for MDS/AML enter clinical practice, including epigenetic therapies (e.g., 5-azacitidine), isocitrate dehydrogenase (IDH) inhibitors, fms-like kinase 3 (FLT3) inhibitors, and lenalidomide for deletion 5q (del5q) MDS. Despite not being developed with the intent of manipulating differentiation, induction of differentiation is a major mechanism by which several of these novel agents function. In this review, we examine the new therapeutic landscape for these diseases, focusing on the role of hematopoietic differentiation and the impact of inflammation and aging. We review how current therapies in MDS/AML promote differentiation as a part of their therapeutic effect, and the cellular mechanisms by which this occurs. We then outline potential novel avenues to achieve differentiation in the myeloid malignancies for therapeutic purposes. This emerging body of knowledge about the importance of relieving differentiation blockade with anti-neoplastic therapies is important to understand how current novel agents function and may open avenues to developing new treatments that explicitly target cellular differentiation. Moving beyond cytotoxic agents has the potential to open new and unexpected avenues in the treatment of myeloid malignancies, hopefully providing more efficacy with reduced toxicity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".