Angiopoietin-1 deficiency increases tumor metastasis in mice
Bibliographic record
Abstract
BACKGROUND: Angipoietin-1 activation of the tyrosine kinase receptor Tek expressed mainly on endothelial cells leads to survival and stabilization of endothelial cells. Studies have shown that Angiopoietin-1 counteracts permeability induced by a number of stimuli. Here, we test the hypothesis that loss of Angiopoietin-1/Tek signaling in the vasculature would increase metastasis. METHODS: Angiopoietin-1 was deleted in mice just before birth using floxed Angiopoietin-1 and Tek mice crossed to doxycycline-inducible bitransgenic ROSA-rtTA/tetO-Cre mice. By crossing Angiopoietin-1 knockout mice to the MMTV-PyMT autochthonous mouse breast cancer model, we investigated primary tumor growth and metastasis to the lung. Furthermore, we utilized B16F10 melanoma cells subcutaneous and experimental lung metastasis models in Angiopoietin-1 and Tek knockout mice. RESULTS: We found that primary tumor growth in MMTV-PyMT mice was unaffected, while metastasis to the lung was significantly increased in Angiopoietin-1 knockout MMTV-PyMT mice. In addition, angiopoietin-1 deficient mice exhibited a significant increase in lung metastasis of B16F10 melanoma cells, compared to wild type mice 3 weeks after injection. Additional experiments showed that this was likely an early event due to increased attachment or extravasation of tumor cells, since seeding of tumor cells was significantly increased 4 and 24 h post tail vein injection. Finally, using inducible Tek knockout mice, we showed a significant increase in tumor cell seeding to the lung, suggesting that Angiopoietin-1/Tek signaling is important for vascular integrity to limit metastasis. CONCLUSIONS: This study show that loss of the Angiopoietin-1/Tek vascular growth factor system leads to increased metastasis without affecting primary tumor growth.
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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) | 0.000 | 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) | 0.003 | 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".