17β-Estradiol and tamoxifen stimulate rapid and transient ERK activation in MCF-7 cells via distinct signaling mechanisms
Bibliographic record
Abstract
Traditionally, estrogen signaling was thought to be mediated strictly through genomic pathways. Recently, however, it has been demonstrated that estrogen stimulation of cells leads to rapid nongenomic effects including ERK activation. While the precise mechanism of this action is still under investigation, it is known that activation of the epidermal growth factor receptor, the Src tyrosine kinase, and metalloproteinases are involved in this process. More recently, tamoxifen, an anti-hormonal agent used to treat breast cancer, has been shown to also activate ERK. The pathways by which it does so, however, are not known. Using the MCF-7 human breast carcinoma cell line as a model system, we show that ERK is rapidly and transiently activated in cells challenged with epidermal growth factor (EGF), 17beta-estradiol (E2) or tamoxifen. The ERK activation response to E2 and tamoxifen was kinetically similar, although the response to tamoxifen was delayed relative to that of E2 stimulation. The effect of the EGFR inhibitor AG1517 revealed that E2 and tamoxifen were both equally dependent on EGFR for activation of ERK. In contrast, inhibition of Src or metalloproteinases caused distinct effects on ERK activation by E2 and tamoxifen. Thus, while both E2 and tamoxifen induced activation of ERK, the differences in the effects of inhibitors of Src or metalloproteinases on ERK activation indicated that E2 and tamoxifen do so via distinct molecular mechanisms.
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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".