Promiscuous targeting of bromodomains by bromosporine identifies BET proteins as master regulators of primary transcription response in leukemia
Bibliographic record
Abstract
Bromodomains (BRDs) have emerged as compelling targets for cancer therapy. The development of selective and potent BET (bromo and extra-terminal) inhibitors and their significant activity in diverse tumor models have rapidly translated into clinical studies and have motivated drug development efforts targeting non-BET BRDs. However, the complex multidomain/subunit architecture of BRD protein complexes complicates predictions of the consequences of their pharmacological targeting. To address this issue, we developed a promiscuous BRD inhibitor [bromosporine (BSP)] that broadly targets BRDs (including BETs) with nanomolar affinity, creating a tool for the identification of cellular processes and diseases where BRDs have a regulatory function. As a proof of principle, we studied the effects of BSP on leukemic cell lines known to be sensitive to BET inhibition and found, as expected, strong antiproliferative activity. Comparison of the modulation of transcriptional profiles by BSP after a short exposure to the inhibitor resulted in a BET inhibitor signature but no significant additional changes in transcription that could account for inhibition of other BRDs. Thus, nonselective targeting of BRDs identified BETs, but not other BRDs, as master regulators of context-dependent primary transcription response.
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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".