Cyclooxygenase-2 Inhibition for the Prophylaxis and Treatment of Preinvasive Breast Cancer in a Her-2/Neu Mouse Model
Bibliographic record
Abstract
Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer. Several molecular alterations have been identified in DCIS. Among them, cyclooxygenase 2 (COX-2) overexpression has been shown in 60% to 80% of DCIS cases. Celecoxib is a nonsteroidal anti-inflammatory drug that selectively inhibits COX-2. In this study, we evaluated whether COX-2 inhibition by celecoxib can reduce the incidence of preinvasive breast cancer and its progression to invasive breast cancer in a mouse model exhibiting a similar phenotype to human solid-pattern DCIS. We have used the mouse model mouse mammary tumor virus (MMTV)-Neu to investigate this possibility. These mice carry a rat Her-2/Neu transgene and are known to develop DCIS-like lesions. Our results showed that celecoxib (500 ppm) given as prophylaxis was neither able to prevent tumor development nor delay tumor appearance compared with untreated mice. Furthermore, when the drug was given early in tumorigenesis, it did not reduce the progression of preinvasive to invasive tumors nor prevent lung metastasis. Reduction of prostaglandin levels was, however, achieved in mammary tumors of treated mice. In addition, celecoxib treatment caused an increase in apoptosis and decreased vascular endothelial growth factor expression in treated animals. Our results contrast with some previously published studies and highlight the complexity of the relationship between COX-2 and 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".