UTX inhibition as selective epigenetic therapy against TAL1-driven T-cell acute lymphoblastic leukemia
Bibliographic record
Abstract
T-cell acute lymphoblastic leukemia (T-ALL) is a heterogeneous group of hematological tumors composed of distinct subtypes that vary in their genetic abnormalities, gene expression signatures, and prognoses. However, it remains unclear whether T-ALL subtypes differ at the functional level, and, as such, T-ALL treatments are uniformly applied across subtypes, leading to variable responses between patients. Here we reveal the existence of a subtype-specific epigenetic vulnerability in T-ALL by which a particular subgroup of T-ALL characterized by expression of the oncogenic transcription factor TAL1 is uniquely sensitive to variations in the dosage and activity of the histone 3 Lys27 (H3K27) demethylase UTX/KDM6A. Specifically, we identify UTX as a coactivator of TAL1 and show that it acts as a major regulator of the TAL1 leukemic gene expression program. Furthermore, we demonstrate that UTX, previously described as a tumor suppressor in T-ALL, is in fact a pro-oncogenic cofactor essential for leukemia maintenance in TAL1-positive (but not TAL1-negative) T-ALL. Exploiting this subtype-specific epigenetic vulnerability, we propose a novel therapeutic approach based on UTX inhibition through in vivo administration of an H3K27 demethylase inhibitor that efficiently kills TAL1-positive primary human leukemia. These findings provide the first opportunity to develop personalized epigenetic therapy for T-ALL patients.
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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".