Loss of PPARγ expression in mammary secretory epithelial cells creates a pro‐breast tumorigenic environment
Bibliographic record
Abstract
Breast cancer is the leading cause of new cancer diagnoses among women. Using peroxisome proliferator-activated receptor (PPAR)γ((+/-)) mice, we showed normal expression of PPARγ was critical to stop 7,12-dimethylbenz[a]anthracene (DMBA)-induced breast tumorigenesis. PPARγ is expressed in many breast cell types including mammary secretory epithelial (MSE) cells. MSEs proliferate as required during pregnancy, and undergo apoptosis or reversible transdifferentiation during involution once lactation is complete. Thus, MSE-specific loss of PPARγ was hypothesized to enhance DMBA-mediated breast tumorigenesis. To test this, MSE cell-specific PPARγ knockout (PPARγ-MSE KO) and control (PPARγ-WT) mice were generated, mated and allowed to nurse for three days. One week after involution, dams were treated with DMBA to initiate breast tumors, and randomized on week 7 to continue receiving a normal chow diet (DMBA Only: PPARγ-WT, n = 15; PPARγ-MSE KO, n = 25) or one supplemented with a PPARγ activating drug (DMBA + ROSI: PPARγ-WT, n = 17; PPARγ-MSE KO, n = 24), and monitored for changes in breast tumor outcomes. PPARγ-MSE KOs had significantly lower overall survival and decreased mammary tumor latency as compared to PPARγ-WT controls. PPARγ activation significantly reduced DMBA-mediated malignant mammary tumor volumes irrespective of genotype. MSE-specific PPARγ loss resulted in decreased mammary gland expression of PTEN and Bax, increased superoxide anion production, and elevated serum eotaxin and RANTES, creating a protumorigenic environment. Moreover, PPARγ activation in MSEs delayed mammary tumor growth in part by down-regulating Cox-1, Cox-2 and cyclin D1. Collectively, these studies highlight a protective role of MSE-specific PPARγ during breast tumorigenesis, and support a novel chemotherapeutic role of PPARγ activation in breast cancer.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".