Selective Activation of the Glucocorticoid Receptor by Steroid Antagonists in Human Breast Cancer and Osteosarcoma Cells
Bibliographic record
Abstract
Steroid hormones regulate the transcription of numerous genes via high affinity receptors that act in concert with chromatin remodeling complexes, coactivators and corepressors. We have compared the activities of a variety of glucocorticoid receptor (GR) antagonists in breast cancer and osteosarcoma cell lines engineered to stably maintain the mouse mammary tumor virus promoter. In both cell types, GR activation by dexamethasone occurs via the disruption of mouse mammary tumor virus chromatin structure and the recruitment of receptor coactivator proteins. However, when challenged with a variety of antagonists the GR displays differential ability to activate transcription within the two cell types. For the breast cancer cells, the antagonists fail to activate the promoter and do not promote the association of the GR with either remodeling or coactivator proteins. In contrast, in osteosarcoma cells, the antiglucocorticoids, RU486 and RU43044, exhibit partial agonist activity. The capacity of these antagonists to stimulate transcription in the osteosarcoma cells is reflected in the ability of the RU486-bound receptor to remodel chromatin and associate with chromatin-remodeling proteins. Similarly, the observation that the RU486-bound receptor does not fully activate transcription is consistent with its inability to recruit receptor coactivator proteins.
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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) | 0.001 | 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".