Inducible upregulation of oestrogen receptor-β1 affects oestrogen and tamoxifen responsiveness in MCF7 human breast cancer cells
Bibliographic record
Abstract
To investigate the effect of altered oestrogen receptor (ER)alpha and ERbeta expression on oestrogen and anti-oestrogen action in breast cancer, we have stably expressed an inducible ERbeta1 in MCF7 breast cancer cells. Stably expressing clones were isolated and over-expression of ERbeta1 correlated with increased levels of specific radiolabelled oestradiol (E2) binding. Increased ERbeta1 did not affect endogenous levels of ERalpha but increased progesterone receptor (PR) levels. Over-expression of ERbeta1 reduced growth responses to E2 in contrast to little if any effect of over-expression of ERalpha. In oestrogen-replete conditions, over-expression of ERbeta1 but not ERalpha reduced proliferation. Over-expression of ERbeta1 did not result in anti-oestrogen resistance but was associated with increased sensitivity to 4-hydroxytamoxifen. Our results suggested that over-expression of ERbeta1 in the presence of an endogenously expressed ERalpha was associated with tamoxifen sensitivity but may negatively modulate ERalpha-mediated growth. However, not all ERalpha activities were inhibited since endogenous PR expression was increased by both ERalpha and ERbeta1 over-expression. These data paralleled those seen in some in vivo studies showing a relationship between PR and ERbeta expression as well as ERbeta expression and tamoxifen sensitivity of ER-positive breast cancer patients. These models are relevant and will be useful for dissecting the role of ERbeta1 expression in ER-positive 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) | 0.000 | 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".