Differential Regulation of Breast Cancer-Associated Genes by Progesterone Receptor Isoforms PRA and PRB in a New Bi-Inducible Breast Cancer Cell Line
Bibliographic record
Abstract
Progesterone receptor isoforms (PRA and PRB) are expressed at equal levels in normal mammary cells. However, alteration in PRA/PRB expression is often observed in aggressive breast cancer suggesting differential contribution of PR isoforms in carcinogenesis. The mechanisms underlying such processes remain to be established mainly due to paucity of appropriate cellular models. To investigate the role of PR isoforms and the impact of imbalanced PRA/PRB ratio in transcriptional regulation, we have generated an original human breast cancer cell line conditionally expressing PRA and/or PRB in dose-dependence of non-steroid inducers. We first focused on PR-dependent transcriptional regulation of the paracrine growth factor gene amphiregulin (AREG) playing important role in cancer. Interestingly, unliganded PRA increases AREG expression, independently of estrogen receptor, yet inhibitable by antiprogestins. We show that functional outcome of epidermal growth factor (EGF) on such regulation is highly dependent on PRA/PRB ratio. Using this valuable model, genome-wide transcriptomic studies allowed us to determine the differential effects of PRA and PRB as a function of hormonal status. We identified a large number of novel PR-regulated genes notably implicated in breast cancer and metastasis and demonstrated that imbalanced PRA/PRB ratio strongly impact their expression predicting poor outcome in breast cancer. In sum, our unique cell-based system strongly suggests that PRA/PRB ratio is a critical determinant of PR target gene selectivity and responses to hormonal/growth factor stimuli. These findings provide molecular support for the aggressive phenotype of breast cancers with impaired expression of PRA or PRB.
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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 | 0.000 | 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) | 0.000 | 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".