Data from 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
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. [Cancer Res 2008;68(2):521–9]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".