Chemical Screen Identifies Diverse and Novel Histone Deacetylase Inhibitors as Repressors of NUT Function: Implications for NUT Carcinoma Pathogenesis and Treatment
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Bibliographic record
Abstract
Abstract NUT carcinoma (NC), characterized most commonly by the BRD4-NUTM1 fusion, is a rare, aggressive variant of squamous carcinoma with no effective treatment. BRD4-NUT drives growth and maintains the poorly differentiated state of NC by activating pro-growth genes such as MYC, through the formation of massive, hyperacetylated, superenhancer-like domains termed megadomains. BRD4-NUT–mediated hyperacetylation of chromatin is facilitated by the chromatin-targeting tandem bromodomains of BRD4, combined with NUT, which recruits the histone acetyltransferase, p300. Here, we developed a high-throughput small-molecule screen to identify inhibitors of transcriptional activation by NUT. In this dCAS9-based GFP-reporter assay, the strongest hits were diverse histone deacetylase (HDAC) inhibitors. Two structurally unrelated HDAC inhibitors, panobinostat and the novel compound, IRBM6, both repressed growth and induced differentiation of NC cells in proportion to their inhibition of NUT transcriptional activity. These two compounds repressed transcription of megadomain-associated oncogenic genes, such as MYC and SOX2, while upregulating pro-differentiation, non-megadomain–associated genes, including JUN, FOS, and key cell-cycle regulators, such as CDKN1A. The transcriptional changes correlate with depletion of BRD4-NUT from megadomains, and redistribution of the p300/CBP-associated chromatin acetylation mark, H3K27ac, away from megadomains toward regular enhancer regions previously populated by H3K27ac. In NC xenograft models, we demonstrated that suppression of tumor growth by panobinostat was comparable with that of bromodomain inhibition, and when combined they improved both survival and growth suppression. Implications: The findings provide mechanistic and preclinical rationale for the use of HDAC inhibitors, alone or combined with other agents, in the treatment of NUT carcinoma.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it