Gene Transfer of Matrix Metalloproteinase-9 Induces Tumor Regression of Breast Cancer<i>In vivo</i>
Bibliographic record
Abstract
Matrix metalloproteinases (MMP) are important regulators of angiogenesis and tumor progression by degradation of extracellular matrix. Clinical trials using MMP inhibitors have failed and recent studies suggest that MMPs may in contrast suppress tumor growth. It is not known, however, if MMPs or their inhibitors, tissue inhibitor of metalloproteinases (TIMP), can be used as therapy of established cancer. Here, adenovirus vectors carrying the human genes for MMP-9, TIMP-1, or empty controls were injected intratumorally in breast cancers established in mice supplemented with estradiol and treated with tamoxifen. Microdialysis was used to quantify MMP activity and sampling of endostatin and vascular endothelial growth factor (VEGF) in situ. We show that AdMMP-9 increased MMP activity in vivo, decreased tumor growth rate, and decreased microvessel area significantly. AdMMP-9 therapy resulted in significantly increased levels of endostatin in vivo, whereas VEGF levels were unaffected. As previously shown, tamoxifen exposure by itself increased MMP activity in all treatment groups. Moreover, the combined therapy with AdMMP-9 and tamoxifen further reduced tumor growth and increased the endostatin levels compared with either treatment alone. Gene transfer of TIMP-1 had no effects on tumor progression and counteracted the therapeutic effect of tamoxifen in our breast cancer model. This is the first report showing that overexpression of MMP-9 results in increased generation of antiangiogenic fragments, decreased angiogenesis, and therapeutic effects of established 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".