Vascular Endothelial Growth Factor–Mediated Decrease in Plasma Soluble Vascular Endothelial Growth Factor Receptor-2 Levels as a Surrogate Biomarker for Tumor Growth
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) is a potent proangiogenic protein that activates VEGF receptor (VEGFR) tyrosine kinases expressed by vascular endothelial cells. We previously showed that one of these receptors, VEGFR-2, has a truncated soluble form (sVEGFR-2) that can be detected in mouse and human plasma. Because activation of VEGFR-2 plays an important role in tumor angiogenesis, clinical interest in monitoring plasma sVEGFR-2 levels in cancer patients has focused on its potential exploitation as a surrogate biomarker for disease progression as well as assessing efficacy/activity of antiangiogenic drugs, particularly those that target VEGF or VEGFR-2. However, no preclinical studies have been done to study sVEGFR-2 during tumor growth or the mechanisms involved in its modulation. Using spontaneously growing tumors and both localized and metastatic human tumor xenografts, we evaluated the relationship between sVEGFR-2 and tumor burden as well as underlying factors governing protein level modulation in vivo. Our results show an inverse relationship between the levels of sVEGFR-2 and tumor size. Furthermore, using various methods of VEGF overexpression in vivo, including cell transfection and adenoviral delivery, we found plasma sVEGFR-2 decreases to be mediated largely by tumor-derived VEGF. Finally, in vitro studies indicate VEGF-mediated sVEGFR-2 modulation is the result of ligand-induced down-regulation of the VEGFR-2 from the cell surface. Taken together, these findings may be pertinent to further clinical exploitation of plasma sVEGFR-2 levels as a surrogate biomarker of VEGF-dependent tumor growth as well as an activity indicator of antiangiogenic drugs that target the VEGFR system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".