Stromal adipocyte PPARγ protects against breast tumorigenesis
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor (PPAR)γ regulates the expression of genes essential for fat storage, primarily through its activity in adipocytes. It also has a role in carcinogenesis. PPARγ normally stops the in vivo progression of 7,12-dimethylbenz[a]anthracene (DMBA)-mediated breast tumours as revealed with PPARγ haploinsufficient mice. Since many cell types associated with the mammary gland express PPARγ, each with unique signal patterns, this study aimed to define which tissues are required for PPARγ-dependent antitumour effects. Accordingly, adipocyte-specific PPARγ knockout (PPARγ-A KO) mice and their wild-type (PPARγ-WT) controls were generated, and treated with DMBA for 6 weeks to initiate breast tumorigenesis. On week 7, mice were randomized to continue on normal chow diet or one supplemented with rosiglitazone (ROSI), and followed for 25 weeks for tumour outcomes. In PPARγ-A KO versus PPARγ-WT mice, malignant mammary tumour incidence was significantly higher and mammary tumour latency was decreased. DMBA + ROSI treatment reduced average mammary tumour volumes by 50%. Gene expression analyses of mammary glands by quantitative real-time polymerase chain reaction and immunofluorescence indicated that untreated PPARγ-A KOs had significantly decreased BRCA1 expression in mammary stromal adipocytes. Compared with PPARγ-WT mice, serum leptin levels in PPARγ-A KOs were also significantly higher throughout the study. Together, these data are the first to suggest that in vivo PPARγ expression in mammary stromal adipocytes attenuates breast tumorigenesis through BRCA1 upregulation and decreased leptin secretion. This study supports a protective effect of activating PPARγ as a novel chemopreventive therapy for breast cancer.
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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".