USP7 cooperates with NOTCH1 to drive the oncogenic transcriptional program in T cell leukemia
Bibliographic record
Abstract
Abstract T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive disease, affecting children and adults. Treatments 1-6 show high response rates but have debilitating effects and carry risk of relapse 5,7,8 . Previous work implicated NOTCH1 and other oncogenes 1,2,9-20 . However, direct inhibition of these pathways affects healthy tissues and cancer alike. Here, we demonstrate that ubiquitin-specific protease 7 (USP7) 21-32 controls leukemia growth by stabilizing the levels of the NOTCH1 and JMJD3 demethylase. USP7 is overexpressed T-ALL and is transcriptionally regulated by NOTCH1. In turn, USP7 controls NOTCH1 through deubiquitination. USP7 is bound to oncogenic targets and controls gene expression through H2B ubiquitination and H3K27me3 changes via stabilization of NOTCH1 and JMJD3. We also show that USP7 and NOTCH1 bind T-ALL superenhancers, and USP7 inhibition alters associated gene activity. These results provide a new model for deubiquitinase activity through recruitment to oncogenic chromatin loci and regulation of both oncogenic transcription factors and chromatin marks to promote leukemia. USP7 inhibition 33 significantly blocked T-ALL cell growth in vitro and in vivo. Our studies also show that USP7 is upregulated in the aggressive high-risk cases of T-ALL and suggest that USP7 expression might be a prognostic marker in ALL and its inhibition could be a therapeutic tool against aggressive leukemia.
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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".