Proteolysis during Tumor Cell Extravasation In Vitro: Metalloproteinase Involvement across Tumor Cell Types
Bibliographic record
Abstract
To test if proteolysis is involved in tumor cell extravasation, we developed an in vitro model where tumor cells cross an endothelial monolayer cultured on a basement membrane. Using this model we classified the ability of the cells to transmigrate through the endothelial cell barrier onto the underlying matrix, and scored this invasion according to the stage of passage through the endothelium. Metalloproteinase inhibitors reduced tumor cell extravasation by at least 35%. Visualization of protease and cell adhesion molecules by confocal microscopy demonstrated the cell surface localization of MMP-2, MMP-9, MT1-MMP, furin, CD44 and αvβ3, during the process of transendothelial migration. By the addition of inhibitors and bio-modulators we assessed the functional requirement of the aforementioned molecules for efficient migration. Proteolytic digestion occurred at the cell-matrix interface and was most evident during the migratory stage. All of the inhibitors and biomodulators affected the transition of the tumor cells into the migratory stage, highlighting the most prevalent use of proteolysis at this particular step of tumor cell extravasation. These data suggest that a proteolytic interface operates at the tumor cell surface within the tumor-endothelial cell microenvironment.
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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) | 0.001 | 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".