JAK/BCL2 inhibition acts synergistically with LSD1 inhibitors to selectively target ETP-ALL
Bibliographic record
Abstract
Abstract ETP-ALL (Early T cell Progenitor Acute Lymphoblastic Leukemia) represents a high-risk subtype of T cell acute lymphocytic leukemia (T-ALL). Therapeutically, ETP-ALL patients frequently relapse after conventional chemotherapy highlighting the need for alternative therapeutic approaches. Using our ZEB2Tg ETP-ALL mouse model we previously documented the potential utility of the catalytic LSD1 inhibitor (GSK2879552) for treating mouse/human ETP-ALL. However, this approach proved to be inefficient, especially in killing human LOUCY cell ETP-ALL xenografts in vivo. Here we have revealed the novel involvement of ZEB2/LSD1 complexes in repressing the intrinsic apoptosis pathway by inhibiting the expression of several pro-apoptotic proteins such as BIM (BCL2L11) as a major driver for ETP-ALL survival. Treatment with LSD1i (particularly with the steric inhibitor SP2509) restored the expression of ZEB2/LSD1 pro-apoptotic BIM (BCL2L11) target. In combination with a JAK/STAT pathway inhibitor (JAKi, Ruxolitinib) or with a direct inhibitor of the anti-apoptotic BCL2 protein (BCL2i, ABT-199) resistance of human and mouse ETP-ALL to LSD1i was reversed. This new combination approach efficiently inhibited the growth of human and mouse ETP-ALL cells in vivo by enhancing their differentiation and triggering an apoptotic response. These results set the stage for novel combination therapies to be used in clinical trials to treat ETP-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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".