Low Levels of Estrogen Receptor β Protein Predict Resistance to Tamoxifen Therapy in Breast Cancer
Bibliographic record
Abstract
PURPOSE: Breast cancer is a hormone-dependent cancer, and the presence of estrogen receptor alpha (ER-alpha) in tumors is used clinically to predict the likelihood of response to hormonal therapies. The clinical value of the second recently identified ER isoform, called ER-beta, is less clear, and there is currently conflicting data concerning its potential role as a prognostic or predictive factor. EXPERIMENTAL DESIGN: To assess whether ER-beta expression is associated with clinical outcome, protein levels were measured by immunoblot analysis of a retrospective bank of tumor cell lysates from 305 axillary node-positive patients. A total of 119 received no adjuvant therapy, and 186 were treated with tamoxifen only. The median follow-up time was 65 months. Univariate and multivariate Cox regression modeling was done to assess the prognostic and predictive significance of ER-beta expression. RESULTS: Expression of ER-beta protein did not correlate significantly with any other clinical variables, including ER and progesterone levels (as measured ligand binding assay), tumor size, age, or axillary nodal status. In the untreated population, those patients whose tumors who expressed both receptor isoforms exhibited the most favorable outcome as compared with those patients who had lost ER-alpha expression. However, there was no association between ER-beta levels alone and either disease-free or overall survival in the untreated patient population. In contrast, in both univariate and multivariate analyses, high levels of ER-beta predicted an improved disease-free and overall survival in patients treated with adjuvant tamoxifen therapy. CONCLUSIONS: These findings provide evidence that ER-beta may be an independent predictor of response to tamoxifen in breast cancer. Furthermore, these results suggest that ER-beta may influence tumor progression in ways different from those mediated by the ER-alpha isoform.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".