VEGFR2 Expression and TGF-β Signaling in Initial and Recurrent High-Grade Human Glioma
Bibliographic record
Abstract
OBJECTIVE: Bevacizumab has promising activity against glioma, although reasons for poor efficacy and variable response rates in certain patients are unclear. Vascular endothelial growth factor receptor 2 (VEGFR2) is heterogeneously expressed within the microvasculature of various malignancies. Moreover, transforming growth factor β (TGF-β), a negative prognostic factor for glioma, is intimately involved in angiogenesis including VEGFR2 regulation. Our objective was to associate expression of VEGFR2 and TGF-β activity with clinicopathological features of human glioma. METHODS: Expression patterns determined by immunohistochemistry for VEGFR2 and phosphorylated Smad2 in human gliomas were compared to overall survival, progression-free survival (PFS), initial versus recurrent tumors and tumor grade. RESULTS: Endothelial VEGFR2 expression was low or undetectable in normal tissue but the proportion of VEGFR2-positive vessels increased with tumor grade. Decreased PFS was associated with tumors whose vessels had increased proportions of VEGFR2 at recurrence. Neither parenchymal nor endothelial cell p-Smad2 was associated with tumor grade; however, the former was negatively correlated with overall survival in glioblastoma multiforme. CONCLUSIONS: The molecular phenotype of the vasculature based on the status of VEGFR2 but not p-Smad2 is related to aspects of glioma progression and patient response. Changes in VEGFR2-positive vessels may account for variable therapeutic efficacy of anti-angiogenic agents.
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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".