Endothelial expression of autocrine VEGF upon the uptake of tumor-derived microvesicles containing oncogenic EGFR
Bibliographic record
Abstract
Activated EGF receptor (EGFR) plays an oncogenic role in several human malignancies. Although the intracellular effects of EGFR are well studied, its ability to induce and modulate tumor angiogenesis is less understood. We found previously that oncogenic EGFR can be shed from cancer cells as cargo of membrane microvesicles (MVs), which can interact with surfaces of other cells. Here we report that MVs produced by human cancer cells harboring activated EGFR (A431, A549, DLD-1) can be taken up by cultured endothelial cells, in which they elicit EGFR-dependent responses, including activation of MAPK and Akt pathways. These responses can be blocked by annexin V and its homodimer, Diannexin, both of which cloak phosphatidylserine residues on the surfaces of MVs. Interestingly, the intercellular EGFR transfer is also accompanied by the onset of VEGF expression in endothelial cells and by autocrine activation of its key signaling receptor (VEGF receptor-2). In A431 human tumor xenografts in mice, angiogenic endothelial cells stain positively for human EGFR and phospho-EGFR, while treatment with Diannexin leads to a reduction of tumor growth rate and microvascular density. Thus, we propose that oncogene-containing tumor cell-derived MVs could act as a unique form of angiogenesis-modulating stimuli and are capable of switching endothelial cells to act in an autocrine mode.
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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".