Stromelysin-2 (Matrix Metalloproteinase 10) Is Inducible in Lymphoma Cells and Accelerates the Growth of Lymphoid Tumors In Vivo
Bibliographic record
Abstract
Matrix metalloproteinase (MMP) 10 (stromelysin-2) is known to degrade various components of the extracellular matrix; however, the signals that regulate its expression and its role in lymphoma growth remain unknown. In the present work, we report the up-regulated expression of MMP10 in T lymphoma cells following contact with endothelial cells. The induction of MMP10 was found to be dependent on the specific interaction between LFA-1 and ICAM-1, which play a central role in regulating the expression of genes involved in the rate-limiting steps of lymphoma development. MMP10, but not MMP3 (stromelysin-1), was also up-regulated in human B lymphoma cells following exposure to IL-4, IL-6, and IL-13, but not to IL-1. To gain further insight into the role of MMP10 in lymphoma development, we generated lymphoma cell lines constitutively expressing high levels of MMP10 and studied these cells for their ability to form thymic lymphoma in vivo. Mice injected with lymphoma cells constitutively expressing MMP10 developed thymic lymphoma more rapidly than those injected with control lymphoma cells. These results provide the first in vivo evidence that overexpression of MMP10 promotes tumor development, and indicate that MMP10 induction is an important pathway activated not only upon ICAM-1/LFA-1-mediated intercellular contact, but also following activation of tumor cells with inflammatory cytokines.
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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 | 0.001 |
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".