Leukemia stem cells in T-ALL require active Hif1α and Wnt signaling
Bibliographic record
Abstract
The Wnt signaling pathway has been shown to play important roles in normal hematopoietic stem cell biology and in the development of both acute and chronic myelogenous leukemia. Its role in maintaining established leukemia stem cells, which are more directly relevant to patients with disease, however, is less clear. To address what role Wnt signaling may play in T-cell acute lymphoblastic leukemia (T-ALL), we used a stably integrated fluorescent Wnt reporter construct to interrogate endogenous Wnt signaling activity in vivo. In this study, we report that active Wnt signaling is restricted to minor subpopulations within bulk tumors, that these Wnt-active subsets are highly enriched for leukemia-initiating cells (LICs), and that genetic inactivation of β-catenin severely reduces LIC frequency. We show further that β-catenin transcription is upregulated by hypoxia through hypoxia-inducible factor 1α (Hif1α) stabilization, and that deletion of Hif1α also severely reduces LIC frequency. Of note, the deletion of β-catenin or Hif1α did not impair the growth or viability of bulk tumor cells, suggesting that elements of the Wnt and Hif pathways specifically support leukemia stem cells. We also confirm the relevance of these findings to human disease using cell lines and patient-derived xenografts, suggesting that targeting these pathways could benefit patients with T-ALL.
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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 | 0.001 |
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".