Contribution of Host-Derived Tissue Factor to Tumor Neovascularization
Bibliographic record
Abstract
OBJECTIVE: The role of host-derived tissue factor (TF) in tumor growth, angiogenesis, and metastasis has hitherto been unclear and was investigated in this study. METHODS AND RESULTS: We compared tumor growth, vascularity, and responses to cyclophosphamide (CTX) of tumors in wild-type (wt) mice, or in animals with TF levels reduced by 99% (low-TF mice). Global growth rate of 3 different types of transplantable tumors (LLC, B16F1, and ES teratoma) or metastasis were unchanged in low-TF mice. However, several unexpected tumor/context-specific alterations were observed in these mice, including: (1) reduced tumor blood vessel size in B16F1 tumors; (2) larger spleen size and greater tolerance to CTX toxicity in the LLC model; (3) aborted tumor growth after inoculation of TF-deficient tumor cells (ES TF(-/-)) in low-TF mice. TF-deficient tumor cells grew readily in mice with normal TF levels and attracted exclusively host-related blood vessels (without vasculogenic mimicry). We postulate that this complementarity may result from tumor-vascular transfer of TF-containing microvesicles, as we observed such transfer using human cancer cells (A431) and mouse endothelial cells, both in vitro and in vivo. CONCLUSIONS: Our study points to an important but context-dependent role of host TF in tumor formation, angiogenesis and therapy.
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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 | 0.000 |
| 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".