USP7 Cooperates with NOTCH1 to Drive the Oncogenic Transcriptional Program in T-Cell Leukemia
Bibliographic record
Abstract
Abstract Purpose: T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive disease, affecting children and adults. Chemotherapy treatments show high response rates but have debilitating effects and carry risk of relapse. Previous work implicated NOTCH1 and other oncogenes. However, direct inhibition of these pathways affects healthy tissues and cancer alike. Our goal in this work has been to identify enzymes active in T-ALL whose activity could be targeted for therapeutic purposes. Experimental Design: To identify and characterize new NOTCH1 druggable partners in T-ALL, we coupled studies of the NOTCH1 interactome to expression analysis and a series of functional analyses in cell lines, patient samples, and xenograft models. Results: We demonstrate that ubiquitin-specific protease 7 (USP7) interacts with NOTCH1 and controls leukemia growth by stabilizing the levels of NOTCH1 and JMJD3 histone demethylase. USP7 is highly expressed in T-ALL and is transcriptionally regulated by NOTCH1. In turn, USP7 controls NOTCH1 levels through deubiquitination. USP7 binds oncogenic targets and controls gene expression through stabilization of NOTCH1 and JMJD3 and ultimately H3K27me3 changes. We also show that USP7 and NOTCH1 bind T-ALL superenhancers, and inhibition of USP7 leads to a decrease of the transcriptional levels of NOTCH1 targets and significantly blocks T-ALL cell growth in vitro and in vivo. Conclusions: These results provide a new model for USP7 deubiquitinase activity through recruitment to oncogenic chromatin loci and regulation of both oncogenic transcription factors and chromatin marks to promote leukemia. Our studies also show that targeting USP7 inhibition could be a therapeutic strategy in aggressive leukemia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".