Achieving clinical success with BET inhibitors as anti-cancer agents
Bibliographic record
Abstract
The transcriptional upregulation of oncogenes is a driving force behind the progression of many tumours. However, until a decade ago, the concept of 'switching off' these oncogenic pathways represented a formidable challenge. Research has revealed that members of the bromo- and extra-terminal domain (BET) motif family are key activators of oncogenic networks in a spectrum of cancers; their function depends on their recruitment to chromatin through two bromodomains (BD1 and BD2). The advent of potent inhibitors of BET proteins (BETi), which target either one or both bromodomains, represents an important step towards the goal of suppressing oncogenic networks within tumours. Here, we discuss the biology of BET proteins, advances in BETi design and highlight potential biomarkers predicting their activity. We also outline the logic of incorporating BETi into combination therapies to enhance its efficacy. We suggest that understanding mechanisms of activity, defining predictive biomarkers and identifying potent synergies represents a roadmap for clinical success using BETi.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".