Synergistic anti-breast cancer effect of a combined treatment with the methyl donor<i>S</i>-adenosyl methionine and the DNA methylation inhibitor 5-aza-2′-deoxycytidine
Bibliographic record
Abstract
DNA-demethylating agents activate tumor suppressor genes that are silenced by DNA methylation in cancer and are therefore emerging as a novel approach to cancer therapy. 5-azacytidine (VIDAZA), the first representative of this class of drugs was approved for treatment of myelodysplastic syndromes and is currently being tested on other cancers including solid tumors. However, 5-azacytidine or its deoxy-analog, 5-aza-2'-deoxycytidine (5-azaCdR) could also induce methylated prometastatic genes by DNA demethylation and induce cancer cell invasiveness. Since 5-azacytidine is a potent cancer growth inhibitor, we tested whether combining it with a DNA-methylating agent, the methyl donor S-adenosyl methionine (SAM), would block the adverse demethylating activity of 5-azaCdR while maintaining its growth suppression effects. We show here using several invasive and non-invasive breast cancer cell lines that SAM inhibits global- and gene-specific demethylation induced by 5-azaCdR, prevents 5-azaCdR activation of prometastatic genes uPA and MMP2, resulting in inhibition of cell invasiveness while augmenting the growth inhibitory effects of 5-azaCdR and its effects on tumor suppressor genes. Combination of drugs acting on the DNA methylation machinery at different levels is proposed as a new strategy for epigenetic therapy of cancer.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".