Targeting class I histone deacetylases in cancer therapy
Bibliographic record
Abstract
INTRODUCTION: Class I histone deacetylases (HDACs) are often overexpressed in cancer, and their inhibition typically leads cancer cells, but not normal cells, to apoptosis. Hence, the field of cancer therapy has experienced a continued surge in the development of HDAC inhibitors. AREAS COVERED: Class I comprises of HDAC1, 2, 3 and 8. HDAC1, 2 and 3 are active as subunits of multiprotein complexes while an HDAC8 complex has not been identified. Besides being a major contributor to poor prognosis in childhood neuroblastoma, little is known of HDAC8 functions and substrates. The targeting and activities of HDAC1 - 3 are modulated by post-translational modifications and association with numerous proteins. The composition of the various HDAC complexes is cell type dependent and fluctuates with intra- and intercellular stimuli. These HDAC complexes play roles at multiple levels in gene expression and genome stability. The application of isoform-specific HDAC inhibitors has met with varying success in clinical trials. EXPERT OPINION: To elucidate the mechanism and cellular impact of HDAC inhibitors, we need to identify the spectrum of class I HDAC complexes and their functions. In the cases of HDAC1 - 3, selectivity of HDAC inhibitors should be directed against relevant complexes. HDAC8 active site unique features facilitate the design of selective inhibitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".