ERbeta sensitizes breast cancer cells to retinoic acid: evidence of transcriptional crosstalk.
Bibliographic record
Abstract
The ability of retinoids to inhibit breast cancer cell growth correlates with estrogen receptor (ER) alpha status, as shown by the antiproliferative effects of retinoids in ERalpha-positive breast cancer cells and their use as chemopreventive agents in premenopausal women. The discovery of ERbeta, also present in breast cancer cells, has added a new level of complexity to this malignancy. To determine the retinoid response in ERbeta-expressing breast cancer cells, we used retroviral transduction of ERbeta in ER-negative MDA-MB-231 cells. Western blot and immunofluorescence confirmed expression and nuclear localization of ERbeta, whereas functionality was shown using an estrogen response element-containing reporter. A significant retinoic acid (RA)-mediated growth inhibition was observed in the transduced ERbeta-positive cells as shown by proliferation assays. Addition of estradiol, tamoxifen, or ICI 182,780 had no effect on cell growth and did not alter RA sensitivity. We observed that retinoids altered ERbeta-mediated transcriptional activity from an estrogen response element, which was confirmed by decreased expression of the pS2 gene, and from an activator protein response element. Conversely, the expression of ERbeta altered RA receptor (RAR) beta expression, resulting in greater induction of RARbeta gene expression on RA treatment, without altered expression of RARalpha. Our data provide evidence of transcriptional crosstalk between ERbeta and RAR in ERbeta-positive breast cancer cells that are growth inhibited by RA.
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".