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Human Pathology
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Human Pathology
Article . 2010 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Quantitative expression of VEGF, VEGF-R1, VEGF-R2, and VEGF-R3 in melanoma tissue microarrays

Authors: Janice M, Mehnert; Mary M, McCarthy; Lucia, Jilaveanu; Keith T, Flaherty; Saadia, Aziz; Robert L, Camp; David L, Rimm; +1 Authors

Quantitative expression of VEGF, VEGF-R1, VEGF-R2, and VEGF-R3 in melanoma tissue microarrays

Abstract

Angiogenesis is required for progression and metastasis of melanoma. Analysis of angiogenic molecules in benign and malignant tissues may allow identification of markers useful for prediction of sensitivity to antiangiogenic agents. We hypothesized that differential expression of vascular endothelial growth factor (VEGF) and its receptors VEGF-R1, VEGF-R2, and VEGF-R3 would be higher in melanomas than nevi and higher in advanced melanoma. Using automated quantitative analysis, we quantified VEGF, -R1, -R2 and -R3 expression in melanoma tissue microarrays composed of 540 nevi and 468 melanoma specimens (198 primaries, 270 metastases). VEGF, VEGF-R1, VEGF-R2, and VEGF-R3 expression was significantly higher in melanomas than nevi by unpaired t tests (P < .0001). VEGF-R2 expression was higher in metastatic specimens (P < .0001), but VEGF-R3 expression was higher in primaries (P < .0001). VEGF was coexpressed with all 3 receptors when assessed by Spearman's rank correlation. VEGF, VEGF-R1, VEGF-R2, and VEGF-R3 expression is higher in melanomas than nevi. Higher expression of VEGF-R2 was found in metastases versus primaries, supporting the idea that selection for an angiogenic phenotype in metastatic melanoma is conferred via up-regulation of VEGF-R2. However, higher expression of VEGF-R3 was seen on primary lesions, potentially implicating this receptor in initiation of lymphatic tumor spread. Clinical trials using antiangiogenic agents in melanoma should include correlative assays of VEGF, VEGF-R1, VEGF-R2, and VEGF-R3 as biomarkers of response to therapy, preferably using quantitative methods such as automated quantitative analysis. Such assessments could assist with evaluation of these molecules as therapeutic targets in melanoma, ultimately facilitating improved selection of patients for treatment.

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Keywords

Vascular Endothelial Growth Factor A, Skin Neoplasms, Vascular Endothelial Growth Factor Receptor-1, Blotting, Western, Vascular Endothelial Growth Factor Receptor-3, Immunohistochemistry, Severity of Illness Index, Vascular Endothelial Growth Factor Receptor-2, Statistics, Nonparametric, Cell Line, Tissue Array Analysis, Disease Progression, Image Processing, Computer-Assisted, Humans, Regression Analysis, Melanoma, Nevus, Proportional Hazards Models

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    99
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
99
Top 10%
Top 10%
Top 1%
bronze