Inhibition of endothelial FAK activity prevents tumor metastasis by enhancing barrier function
Bibliographic record
Abstract
Pharmacological focal adhesion kinase (FAK) inhibition prevents tumor growth and metastasis, via actions on both tumor and stromal cells. In this paper, we show that vascular endothelial cadherin (VEC) tyrosine (Y) 658 is a target of FAK in tumor-associated endothelial cells (ECs). Conditional kinase-dead FAK knockin within ECs inhibited recombinant vascular endothelial growth factor (VEGF-A) and tumor-induced VEC-Y658 phosphorylation in vivo. Adherence of VEGF-expressing tumor cells to ECs triggered FAK-dependent VEC-Y658 phosphorylation. Both FAK inhibition and VEC-Y658F mutation within ECs prevented VEGF-initiated paracellular permeability and tumor cell transmigration across EC barriers. In mice, EC FAK inhibition prevented VEGF-dependent tumor cell extravasation and melanoma dermal to lung metastasis without affecting primary tumor growth. As pharmacological c-Src or FAK inhibition prevents VEGF-stimulated c-Src and FAK translocation to EC adherens junctions, but FAK inhibition does not alter c-Src activation, our experiments identify EC FAK as a key intermediate between c-Src and the regulation of EC barrier function controlling tumor metastasis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".