Molecular mechanisms of bifunctional vitamin D receptor agonist-histone deacetylase inhibitor hybrid molecules in triple-negative breast cancer
Bibliographic record
Abstract
The active form of vitamin D, 1,25-dihydroxyvitamin D (1,25D), and its analogues signal through the nuclear vitamin D receptor (VDR), a ligand-regulated transcription factor, and have been extensively investigated as anticancer agents. 1,25D and its analogs have potential in combination therapies because they exhibit synergistic activities with other anticancer agents such as histone deacetylase inhibitors (HDACi). We have developed a series of hybrid molecules that combine HDACi within the backbone of a VDR agonist and thus represent fully integrated bifunctional molecules. They exhibit anti-tumor efficacy in reducing tumor growth and metastases in an aggressive model of triple-negative breast cancer. However, their solubility is limited by their hydrophobic diarylpentane cores. Our goals here were two-fold: (1) to improve the solubility of hybrids by introducing nitrogen into diarylpentane cores, and (2) to investigate the molecular mechanisms underlying their anti-tumor efficacy by performing comparative gene expression profiling studies with 1,25D and the potent HDACi suberoylanilide hydroxamic acid (SAHA). We found that substituting aryl with pyrydyl rings did not sacrifice bifunctionality and modestly improved solubility. Notably, one compound, AM-193, displayed enhanced potency as a VDR agonist and in cellular assays of cytotoxicity. RNAseq studies in triple negative breast cancer cells revealed that gene expression profiles of hybrids were very similar to that of 1,25D, as was that observed with 1,25D and SAHA combined. The effects of SAHA alone on gene expression were limited and distinct from those 1,25D or hybrids. The combined results suggest that efficacy of hybrids arises from targeting HDACs that do not have a direct role in gene regulation. Moreover, pathways analysis revealed that hybrids regulate numerous genes controlling immune cell infiltration into tumors and suppress the expression of several secreted molecules that promote breast cancer growth and metastasis.
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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".