Metabolic Vulnerabilities and Epigenetic Dysregulation in Myeloproliferative Neoplasms
Bibliographic record
Abstract
The Janus kinase 2 (JAK2)-driven myeloproliferative neoplasms (MPNs) are associated with clonal myelopoiesis, elevated risk of death due to thrombotic complications, and transformation to acute myeloid leukemia (AML). JAK2 inhibitors improve the quality of life for MPN patients, but these approved therapeutics do not readily reduce the natural course of disease or antagonize the neoplastic clone. An understanding of the molecular and cellular changes requisite for MPN development and progression are needed to develop improved therapies. Recently, murine MPN models were demonstrated to exhibit metabolic vulnerabilities due to a high dependence on glucose. Neoplastic hematopoietic progenitor cells in these mice express elevated levels of glycolytic enzymes and exhibit enhanced levels of glycolysis and oxidative phosphorylation, and the disease phenotype of these MPN model mice is antagonized by glycolytic inhibition. While all MPN-driving mutations lead to aberrant JAK2 activation, these mutations often co-exist with mutations in genes that encode epigenetic regulators, including loss of function mutations known to enhance MPN progression. In this perspective we discuss how altered activity of epigenetic regulators (e.g., methylation and acetylation) in MPN-driving stem and progenitor cells may alter cellular metabolism and contribute to the MPN phenotype and progression of disease. Specific metabolic changes associated with epigenetic deregulation may identify patient populations that exhibit specific metabolic vulnerabilities that are absent in normal hematopoietic cells, and thus provide a potential basis for the development of more effective personalized therapeutic approaches.
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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) | 0.000 | 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) | 0.001 | 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".