Src promotes estrogen-dependent estrogen receptor α proteolysis in human breast cancer
Bibliographic record
Abstract
Estrogen drives both transcriptional activation and proteolysis of estrogen receptor alpha (ER alpha; encoded by ESR1). Here we observed variable and overlapping ESR1 mRNA levels in 200 ER alpha-negative and 50 ER alpha-positive primary breast cancers examined, which suggests important posttranscriptional ER alpha regulation. Our results indicate that Src cooperates with estrogen to activate ER alpha proteolysis. Inducible Src stimulated ligand-activated ER alpha transcriptional activity and reduced ER alpha t(1/2). Src and ER alpha levels were inversely correlated in primary breast cancers. ER alpha-negative primary breast cancers and cell lines showed increased Src levels and/or activity compared with ER alpha-positive cancers and cells. ER alpha t(1/2) was reduced in ER alpha-negative cell lines. In both ER alpha-positive and -negative cell lines, both proteasome and Src inhibitors increased ER alpha levels. Src inhibition impaired ligand-activated ER alpha ubiquitylation and increased ER alpha levels. Src siRNA impaired ligand-activated ER alpha loss in BT-20 cells. Pretreatment with Src increased ER alpha ubiquitylation and degradation in vitro. These findings provide what we believe to be a novel link between Src activation and ER alpha proteolysis and support a model whereby crosstalk between liganded ER alpha and Src drives ER alpha transcriptional activity and targets ER alpha for ubiquitin-dependent proteolysis. Oncogenic Src activation may promote not only proliferation, but also estrogen-activated ER alpha loss in a subset of ER alpha-negative breast cancers, altering prognosis and response to therapy.
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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 | 0.003 | 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 | 0.001 |
| 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".