BET inhibition as a single or combined therapeutic approach in primary paediatric B-precursor acute lymphoblastic leukaemia
Bibliographic record
Abstract
Paediatric B-precursor ALL is a highly curable disease, however, treatment resistance in some patients and the long-term toxic effects of current therapies pose the need for more targeted therapeutic approaches. We addressed the cytotoxic effect of JQ1, a highly selective inhibitor against the transcriptional regulators, bromodomain and extra-terminal (BET) family of proteins, in paediatric ALL. We showed a potent in vitro cytotoxic response of a panel of primary ALL to JQ1, independent of their prognostic features but dependent on high MYC expression and coupled with transcriptional downregulation of multiple pro-survival pathways. In agreement with earlier studies, JQ1 induced cell cycle arrest. Here we show that BET inhibition also reduced c-Myc protein stability and suppressed progression of DNA replication forks in ALL cells. Consistent with c-Myc depletion and downregulation of pro-survival pathways JQ1 sensitised primary ALL samples to the classic ALL therapeutic agent dexamethasone. Finally, we demonstrated that JQ1 reduces ALL growth in ALL xenograft models, both as a single agent and in combination with dexamethasone. We conclude that targeting BET proteins should be considered as a new therapeutic strategy for the treatment of paediatric ALL and particularly those cases that exhibit suboptimal responses to standard treatment.
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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) | 0.001 | 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) | 0.001 | 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".