The role of estrogen receptors isoforms in breast cancer
Bibliographic record
Abstract
Background: Estrogen and progesterone receptor (ER/PR) status is an accepted predictive marker in breast cancer. It is well known that breast tumors, which are ER(+) are more likely to respond to endocrine therapy. However, certain percentage of ER(+)/PR(+) tumors do not respond to endocrine therapy. Identification of the second estrogen receptor, named estrogen receptor beta (ER?), as well as the existence of numerous isoforms/splice variants of both ER? and ER?, suggests that complex regulation of estrogen action exists. In this study, we analyze does the expression of two ER? isoforms correlates with ER?/PR status. Methods: Sixty samples of primary operable breast carcinomas were analyzed for ER? and PR protein levels and for mRNA expression of two ER? isoforms (ER?1 and ER??5). ER? and PR proteins were measured by classical biochemical techniques, and ER? mRNAs were measured by real-time RT-PCR. Results: Tumors are divided in three groups according to relative level of mRNA for ER?1 and ER??5. We found that there is no correlation of ER?1 mRNA expression with ER? and PR protein levels. We confirmed the existence of inverse correlation of ER??5 with PR and of ER??5 with ER? in the group of postmenopausal patients. In the subsets of tumors defined by ER?/PR status, we found that percentage of tumors, which concomitantly expressed high levels of both transcripts, are parallel with those that do not response to tamoxifen treatment. Conclusion: Inverse correlation of ER? with ER??5 and PR with ER??5isoform suggests that ER??5 may have inhibitory effect on ER? activity in postmenopausal patients. In addition, we point out that determination of expression profiles of ER? and ER? isoforms in the defined groups of patient are necessary for elucidating its involvement in endocrine resistance. .
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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 | 0.001 | 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".