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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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