FLT3 is genetically essential for ITD-mutated leukemic stem cells but dispensable for human hematopoietic stem cells
Bibliographic record
Abstract
ABSTRACT: Leukemic stem cells (LSCs) fuel acute myeloid leukemia (AML) growth and relapse, but therapies tailored toward eradicating LSCs without harming normal hematopoietic stem cells (HSCs) are lacking. FMS-like tyrosine kinase 3 (FLT3) is considered an important therapeutic target due to frequent mutation in AML and association with relapse. However, there has been limited clinical success with FLT3 drug targeting, suggesting either that FLT3 is not a vulnerability in LSC or that more potent inhibition is required, a scenario where HSC toxicity could become limiting. We tested these possibilities by ablating FLT3 using CRISPR/Cas9-mediated FLT3 knockout (FLT3-KO) in human LSCs and HSCs followed by functional xenograft assays. FLT3-KO in LSCs from FLT3-internal tandem duplication (ITD)-mutated but not FLT3-wild-type AMLs resulted in short-term leukemic grafts of FLT3-KO edited cells that disappeared by 12 weeks. By contrast, FLT3-KO in HSCs from the fetal liver, cord blood, and adult bone marrow did not impair multilineage hematopoiesis in primary and secondary xenografts. Our study establishes FLT3 as an ideal therapeutic target where ITD-positive LSCs are eradicated upon FLT3 deletion whereas HSCs are spared. These findings support the development of more potent FLT3-targeting drugs or gene-editing approaches for LSC eradication to improve clinical outcomes.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".