Inflammation Induced by MMP-9 Enhances Tumor Regression of Experimental Breast Cancer
Bibliographic record
Abstract
Matrix metalloproteinases (MMPs) have been suggested as therapeutic targets in cancer treatment, but broad-spectrum MMP inhibitors have failed in clinical trials. Recent data suggest that several MMPs including MMP-9 exert both pro- and antitumorigenic properties. This is also the case of the natural inhibitors of MMPs, tissue inhibitor of metalloproteinases (TIMPs). The inhibitor of MMP-9 is TIMP-1, and high levels of this enzyme have been associated with decreased survival in breast cancer. Inflammation is one hallmark of cancer progression, and MMPs/TIMPs may be involved in the local immune regulation. We investigated the role of MMP-9/TIMP-1 in regulating innate antitumor immunity in breast cancer. Breast cancers were established in nude mice and treated with intratumoral injections of adenoviruses carrying the human TIMP-1 or MMP-9 gene (AdMMP-9). In vivo microdialysis for sampling of cancer cell-derived (human) and stroma-derived (murine) proteins, immunostainings, as well as cell cultures were performed. We report a dose-dependent decrease of tumor growth and angiogenesis after AdMMP-9 treatment. In addition to increased generation of endostatin, AdMMP-9 promoted an antitumor immune response by inducing massive neutrophil infiltration. Neutrophil depletion prior to gene transfer abolished the therapeutic effects of AdMMP-9. Additionally, AdMMP-9 activated tumor-infiltrating macrophages into a tumor-inhibiting phenotype both in vivo and in vitro. AdMMP-9 also inhibited tumor growth in immune-competent mice bearing breast cancers. Adenoviruses carrying the human TIMP-1 gene had no effect on tumor growth or the immune response. Our novel data identify MMP-9 as a potent player in modulating the innate immune response into antitumor activities.
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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) | 0.002 | 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".