S‐adenosylmethionine in combination with decitabine shows enhanced anti‐cancer effects in repressing breast cancer growth and metastasis
Bibliographic record
Abstract
Abnormal DNA methylation orchestrates many of the cancer-related gene expression irregularities such as the inactivation of tumour suppressor genes through hypermethylation as well as activation of prometastatic genes through hypomethylation. The fact that DNA methylation abnormalities can be chemically reversed positions the DNA methylation machinery as an attractive target for anti-cancer drug development. However, although in vitro studies suggested that targeting concordantly hypo- and hypermethylation is of benefit in suppressing both oncogenic and prometastatic functions of breast cancer cells, this has never been tested in a therapeutic setting in vivo. In this context, we investigated the combined therapeutic effects of an approved nutraceutical agent S-adenosylmethionine (SAM) and FDA-approved hypomethylating agent decitabine using the MDA-MB-231 xenograft model of breast cancer and found a pronounced reduction in mammary tumour volume and lung metastasis compared to the animals in the control and monotherapy treatment arms. Immunohistochemical assessment of the primary breast tumours showed a significantly reduced expression of proliferation (Ki-67) and angiogenesis (CD31) markers following combination therapy as compared to the control group. Global transcriptome and methylome analyses have revealed that the combination therapy regulates genes from several key cancer-related pathways that are abnormally expressed in breast tumours. To our knowledge, this is the first preclinical study demonstrating the anti-cancer therapeutic potential of using a combination of methylating (SAM) and demethylating agent (decitabine) in vivo. Results from this study provide a molecularly founded rationale for clinically testing a combination of agents targeting the epigenome to reduce the morbidity and mortality from breast 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) | 0.001 | 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".