Modeling the functional heterogeneity of leukemia stem cells: role of STAT5 in leukemia stem cell self-renewal
Bibliographic record
Abstract
Although the cancer stem cell (CSC) concept implies that CSCs are rare, recent reports suggest that CSCs may be frequent in some cancers. We hypothesized that the proportion of leukemia stem cells would vary as a function of the number of dysregulated pathways. Constitutive expression of MN1 served as a 1-oncogene model, and coexpression of MN1 and a HOX gene served as a 2-oncogene model. Leukemia-initiating cell (LIC) number and in vitro expansion potential of LICs were functionally assessed by limiting dilution analyses. LIC expansion potential was 132-fold increased in the 2- compared with the 1-oncogene model, although phenotypically, both leukemias were similar. The 2-oncogene model was characterized by granulocyte-macrophage colony-stimulating factor (GM-CSF) hypersensitivity and activated STAT/ERK signaling. GM-CSF hypersensitivity of the 2-oncogene model (MN1/HOXA9) was lost in Stat5b(-/-) cells, and the LIC expansion potential was reduced by 86- and 28-fold in Stat5b(-/-) and Stat1(-/-) cells, respectively. Interestingly, in 201 acute myeloid leukemia (AML) patients, coexpression of MN1 and HOXA9 was restricted to patients with the poorest prognosis and was associated with highly active STAT signaling. Our data demonstrate the functional heterogeneity of LICs and show that STAT signaling is critical for leukemia stem cell self-renewal in MN1- and HOXA9-expressing leukemias.
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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) | 0.001 | 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".