RNA binding protein‐directed control of leukemic stem cell evolution and function
Bibliographic record
Abstract
Strict control over hematopoietic stem cell decision making is essential for healthy life-long blood production and underpins the origins of hematopoietic diseases. Acute myeloid leukemia (AML) in particular is a devastating hematopoietic malignancy that arises from the clonal evolution of disease-initiating primitive cells which acquire compounding genetic changes over time and culminate in the generation of leukemic stem cells (LSCs). Understanding the molecular underpinnings of these driver cells throughout their development will be instrumental in the interception of leukemia, the enabling of effective treatment of pre-leukemic conditions, as well as the development of strategies to target frank AML disease. To this point, a number of precancerous myeloid disorders and age-related alterations are proving as instructive models to gain insights into the initiation of LSCs. Here, we explore this myeloid dysregulation at the level of post-transcriptional control, where RNA-binding proteins (RBPs) function as core effectors. Through regulating the interplay of a myriad of RNA metabolic processes, RBPs orchestrate transcript fates to govern gene expression in health and disease. We describe the expanding appreciation of the role of RBPs and their post-transcriptional networks in sustaining healthy hematopoiesis and their dysregulation in the pathogenesis of clonal myeloid disorders and AML, with a particular emphasis on findings described in human stem cells. Lastly, we discuss key breakthroughs that highlight RBPs and post-transcriptional control as actionable targets for precision therapy of AML.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".