Inhibition of DNMT1 and ERRα crosstalk suppresses breast cancer via derepression of IRF4
Bibliographic record
Abstract
DNA methylation is implicated in the acquisition of malignant phenotypes, and the use of epigenetic modulating drugs is a promising anti-cancer therapeutic strategy. 5-aza-2'deoxycytidine (decitabine, 5-azadC) is an FDA-approved DNA methyltransferase (DNMT) inhibitor with proven effectiveness against hematological malignancies and more recently triple-negative breast cancer (BC). Herein, genetic or pharmacological studies uncovered a hitherto unknown feedforward molecular link between DNMT1 and the estrogen related receptor α (ERRα), a key transcriptional regulator of cellular metabolism. Mechanistically, DNMT1 promotes ERRα stability which in turn couples DNMT1 transcription with that of the methionine cycle and S-adenosylmethionine synthesis to drive DNA methylation. In vitro and in vivo investigation using a pre-clinical mouse model of BC demonstrated a clear therapeutic advantage for combined administration of the ERRα inhibitor C29 with 5-azadC. A large-scale bisulfite genomic sequencing analysis revealed specific methylation perturbations fostering the discovery that reversal of promoter hypermethylation and consequently derepression of the tumor suppressor gene, IRF4, is a factor underlying the observed BC suppressive effects. This work thus uncovers a critical role of ERRα in the crosstalk between transcriptional control of metabolism and epigenetics and illustrates the potential for targeting ERRα in combination with DNMT inhibitors for BC treatment and other epigenetics-driven malignancies.
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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".