Estrogen receptor signaling is reprogrammed during breast tumorigenesis
Bibliographic record
Abstract
Limited knowledge of the changes in estrogen receptor (ER) signaling during the transformation of the normal mammary gland to breast cancer hinders the development of effective prevention and treatment strategies. Differences in estrogen signaling between normal human primary breast epithelial cells and primary breast tumors obtained immediately following surgical excision were explored. Transcriptional profiling of normal ER + mature luminal mammary epithelial cells and ER + breast tumors revealed significant difference in the response to estrogen stimulation. Consistent with these differences in gene expression, the normal and tumor ER cistromes were distinct and sufficient to segregate normal breast tissues from breast tumors. The selective enrichment of the DNA binding motif GRHL2 in the breast cancer-specific ER cistrome suggests that it may play a role in the differential function of ER in breast cancer. Depletion of GRHL2 resulted in altered ER binding and differential transcriptional responses to estrogen stimulation. Furthermore, GRHL2 was demonstrated to be essential for estrogen-stimulated proliferation of ER + breast cancer cells. DLC1 was also identified as an estrogen-induced tumor suppressor in the normal mammary gland with decreased expression in breast cancer. In clinical cohorts, loss of DLC1 and gain of GRHL2 expression are associated with ER + breast cancer and are independently predictive for worse survival. This study suggests that normal ER signaling is lost and tumor-specific ER signaling is gained during breast tumorigenesis. Unraveling these changes in ER signaling during breast cancer progression should aid the development of more effective prevention strategies and targeted therapeutics.
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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) | 0.001 | 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".