Repositioning FDA-Approved Drugs in Combination with Epigenetic Drugs to Reprogram Colon Cancer Epigenome
Bibliographic record
Abstract
Epigenetic drugs, such as DNA methylation inhibitors (DNMTi) or histone deacetylase inhibitors (HDACi), are approved in monotherapy for cancer treatment. These drugs reprogram gene expression profiles, reactivate tumor suppressor genes (TSG) producing cancer cell differentiation and apoptosis. Epigenetic drugs have been shown to synergize with other epigenetic drugs or various anticancer drugs. To discover new molecular entities that enhance epigenetic therapy, we performed a high-throughput screening using FDA-approved libraries in combination with DNMTi or HDACi. As a screening model, we used YB5 system, a human colon cancer cell line, which contains an epigenetically silenced CMV-GFP locus, mimicking TSG silencing in cancer. CMV-GFP reactivation is triggered by DNMTi or HDACi and responds synergistically to DNMTi/HDACi combination, which phenocopies TSG reactivation upon epigenetic therapy. GFP fluorescence was used as a quantitative readout for epigenetic activity. We discovered that 45 FDA-approved drugs (4% of all drugs tested) in our FDA-approved libraries enhanced DNMTi and HDACi activity, mainly belonging to anticancer and antiarrhythmic drug classes. Transcriptome analysis revealed that combination of decitabine (DNMTi) with the antiarrhythmic proscillaridin A produced profound gene expression reprogramming, which was associated with downregulation of 153 epigenetic regulators, including two known oncogenes in colon cancer (SYMD3 and KDM8). Also, we identified about 85 FDA-approved drugs that antagonized DNMTi and HDACi activity through cytotoxic mechanisms, suggesting detrimental drug interactions for patients undergoing epigenetic therapy. Overall, our drug screening identified new combinations of epigenetic and FDA-approved drugs, which can be rapidly implemented into clinical trials. Mol Cancer Ther; 16(2); 397-407. ©2016 AACR.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".