Preferential Estrogen Receptor β Ligands Reduce Bcl-2 Expression in Hormone-Resistant Breast Cancer Cells to Increase Autophagy
Bibliographic record
Abstract
Acquired resistance to selective estrogen receptor (ER) modulators (SERM) and downregulators (SERD) is a significant clinical problem in the treatment of estrogen (E2) receptor-positive (ER(+)) breast cancers. There are two ER subtypes, ERα and ERβ, which promote and inhibit breast cancer cell proliferation, respectively. Although ER(+) breast cancers typically express a high ratio of ERα to ERβ, the acquisition of SERM resistance in vitro and in vivo is associated with increased relative expression of the ERβ. On some gene enhancers, ERβ has been shown to function in opposition to the ERα in the presence of E2. Here, we demonstrate that two different ERβ agonists, WAY-20070 and a novel "A-CD" estrogen called L17, produce a marked reduction in G(2)-M phase correlated with effects on cyclin D1 and cyclin E expression in a SERM/SERD-resistant breast cancer cell line. ERβ agonists recruited both the ERα and ERβ to the Bcl-2 E2-response element strongly reducing Bcl-2 mRNA and protein in an ERβ-dependent manner. L17 recruited RIP140 to the Bcl-2 promoter in cells overexpressing ERβ. Exposure to the ERβ ligands also resulted in increased processing of LC3-I to LC3-II, indicative of enhanced autophagic flux. The coaddition of ERβ agonist and the autophagy inhibitor chloroquine resulted in a significant accumulation of sub-G1 DNA which was completely prevented by the addition of the caspase inhibitor Z-VAD-FMK. We propose that combined therapies with an ERβ agonist and an inhibitor of autophagy may provide the basis for a novel approach to the treatment of SERM/SERD-resistant breast cancers.
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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".