Minireview: The Link Between ERα Corepressors and Histone Deacetylases in Tamoxifen Resistance in Breast Cancer
Bibliographic record
Abstract
Approximately 70% of breast cancers express the estrogen receptor (ER)α and are treated with the ERα antagonist, tamoxifen. However, resistance to tamoxifen frequently develops in advanced breast cancer, in part due to a down-regulation of ERα corepressors. Nuclear receptor corepressors function by attenuating hormone responses and have been shown to potentiate tamoxifen action in various biological systems. Recent genomic data on breast cancers has revealed that genetic and/or genomic events target ERα corepressors in the majority of breast tumors, suggesting that the loss of nuclear receptor corepressor activity may represent an important mechanism that contributes to intrinsic and acquired tamoxifen resistance. Here, the biological functions of ERα corepressors are critically reviewed to elucidate their role in modifying endocrine sensitivity in breast cancer. We highlight a mechanism of gene repression common to corepressors previously shown to enhance the antitumorigenic effects of tamoxifen, which involves the recruitment of histone deacetylases (HDACs) to DNA. As an indicator of epigenetic disequilibrium, the loss of ERα corepressors may predispose cancer cells to the cytotoxic effects of HDAC inhibitors, a class of drug that has been shown to effectively reverse tamoxifen resistance in numerous studies. HDAC inhibition thus appears as a promising therapeutic approach that deserves to be further explored as an avenue to restore drug sensitivity in corepressor-deficient and tamoxifen-resistant breast cancers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".