Chemical perturbations impacting histone acetylation govern colorectal cancer differentiation
Bibliographic record
Abstract
Dysregulated epigenetic programs that restrict differentiation, reactivate fetal genes, and confer phenotypic plasticity are critical to colorectal cancer (CRC) development. By screening a small molecule library targeting epigenetic regulators using our dual reporter system, we found that inhibiting histone deacetylase (HDAC) 1/2 promotes CRC differentiation and anti-tumor activity. Comprehensive biochemical, chemical, and genetic experiments revealed that on-target blockade of the HDAC1/2 catalytic domain mediated the differentiated phenotype. Unbiased profiling of histone posttranslational modifications induced by HDAC1/2 inhibition nominated acetylation of specific histone lysine residues as potential regulators of differentiation. Genome-wide assessment of implicated marks indicated that H3K27ac gains at HDAC1/2-bound regions associated with open chromatin and upregulation of differentiation genes upon HDAC1/2 inhibition. Disrupting H3K27ac by degrading acetyltransferase EP300 rescued HDAC1/2 inhibitor-mediated differentiation of a patient-derived CRC model using single cell RNA-sequencing. Genetic screens revealed that DAPK3 contributes to CRC differentiation induced by HDAC1/2 inhibition. These results highlight the importance of specific chemically targetable histone modifications in governing cancer cell states and epigenetic reprogramming as a therapeutic strategy in CRC.
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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".