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Targeting neuropilin-1 to inhibit VEGF signaling in cancer: Comparison of therapeutic approaches.

Mac Gabhann F, Popel AS - PLoS Comput. Biol. (2006)

Bottom Line: Using the first molecularly detailed computational model of VEGF and its receptors, we have shown previously that the VEGFR-Neuropilin interactions explain the observed differential effects of VEGF isoforms on VEGF signaling in vitro, and demonstrated potent VEGF inhibition by an antibody to Neuropilin-1 that does not block ligand binding but blocks subsequent receptor coupling.The model predicts that blockade of Neuropilin-VEGFR coupling is significantly more effective than other approaches in decreasing VEGF-VEGFR2 signaling.In addition, tumor types with different receptor expression levels respond differently to each of these treatments.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America. feilim@jhu.edu

ABSTRACT
Angiogenesis (neovascularization) plays a crucial role in a variety of physiological and pathological conditions including cancer, cardiovascular disease, and wound healing. Vascular endothelial growth factor (VEGF) is a critical regulator of angiogenesis. Multiple VEGF receptors are expressed on endothelial cells, including signaling receptor tyrosine kinases (VEGFR1 and VEGFR2) and the nonsignaling co-receptor Neuropilin-1. Neuropilin-1 binds only the isoform of VEGF responsible for pathological angiogenesis (VEGF165), and is thus a potential target for inhibiting VEGF signaling. Using the first molecularly detailed computational model of VEGF and its receptors, we have shown previously that the VEGFR-Neuropilin interactions explain the observed differential effects of VEGF isoforms on VEGF signaling in vitro, and demonstrated potent VEGF inhibition by an antibody to Neuropilin-1 that does not block ligand binding but blocks subsequent receptor coupling. In the present study, we extend that computational model to simulation of in vivo VEGF transport and binding, and predict the in vivo efficacy of several Neuropilin-targeted therapies in inhibiting VEGF signaling: (a) blocking Neuropilin-1 expression; (b) blocking VEGF binding to Neuropilin-1; (c) blocking Neuropilin-VEGFR coupling. The model predicts that blockade of Neuropilin-VEGFR coupling is significantly more effective than other approaches in decreasing VEGF-VEGFR2 signaling. In addition, tumor types with different receptor expression levels respond differently to each of these treatments. In designing human therapeutics, the mechanism of attacking the target plays a significant role in the outcome: of the strategies tested here, drugs with similar properties to the Neuropilin-1 antibody are predicted to be most effective. The tumor type and the microenvironment of the target tissue are also significant in determining therapeutic efficacy of each of the treatments studied.

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VEGF Signaling Inhibition Is Effective for a Shorter Period of Time for a Tissue with a Higher Microvascular Density(A–C) VEGF–VEGFR2 complex formation on the endothelial cells following each of the three treatments. The tumor modeled here expresses 10,000 VEGFR2 and 100,000 Neuropilin per endothelial cell. Gray lines represent the case of 2% vascular volume, as depicted in Figure 3; the black lines represent 4.2% vascular volume. Note that while the 103/cell, pM, and nM scales apply to both the gray and black lines, the pmol/L tissue scales apply only to the black lines; the normalization is different for the gray lines (see Figure 3 for the correct scales).(D–F) Free (unbound) VEGF concentration in the interstitial space. *VEGF121 secreted at the same rate as VEGF165.(G–I) The concentration of VEGF inhibitor in the interstitial space, or density of Neuropilin on the blood vessel endothelial cell surface.
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pcbi-0020180-g008: VEGF Signaling Inhibition Is Effective for a Shorter Period of Time for a Tissue with a Higher Microvascular Density(A–C) VEGF–VEGFR2 complex formation on the endothelial cells following each of the three treatments. The tumor modeled here expresses 10,000 VEGFR2 and 100,000 Neuropilin per endothelial cell. Gray lines represent the case of 2% vascular volume, as depicted in Figure 3; the black lines represent 4.2% vascular volume. Note that while the 103/cell, pM, and nM scales apply to both the gray and black lines, the pmol/L tissue scales apply only to the black lines; the normalization is different for the gray lines (see Figure 3 for the correct scales).(D–F) Free (unbound) VEGF concentration in the interstitial space. *VEGF121 secreted at the same rate as VEGF165.(G–I) The concentration of VEGF inhibitor in the interstitial space, or density of Neuropilin on the blood vessel endothelial cell surface.

Mentions: The vascular density appears to range widely for breast cancer, with 100–500 capillaries/mm2 cross-sectional area of tissue measured in different tumor samples [35]. Many studies of vascular density in breast cancer have been performed (see [36] for a comprehensive list and review), and typical average values are 100–250 capillaries/mm2. For the capillary dimensions described above, a capillary density of 235 capillaries/mm2 gives a vascular volume of 2% cm3/cm3 tissue. This is lower than the microvascular volume of 5% (even allowing for volume of the endothelial cells) found in studies of vascular volume [37,38]. This volume would require a significantly higher capillary density (490 capillaries/mm2) for the average capillary size noted above. The reason for the discrepancy may be the inclusion in that study of larger microvessels, or the differences between the types of cancer studied. We performed most of the simulations (Figures 3–7) using the 2% vascular space, but also performed simulations for the larger vascular volume and included those for comparison (Figures 8 and 9).


Targeting neuropilin-1 to inhibit VEGF signaling in cancer: Comparison of therapeutic approaches.

Mac Gabhann F, Popel AS - PLoS Comput. Biol. (2006)

VEGF Signaling Inhibition Is Effective for a Shorter Period of Time for a Tissue with a Higher Microvascular Density(A–C) VEGF–VEGFR2 complex formation on the endothelial cells following each of the three treatments. The tumor modeled here expresses 10,000 VEGFR2 and 100,000 Neuropilin per endothelial cell. Gray lines represent the case of 2% vascular volume, as depicted in Figure 3; the black lines represent 4.2% vascular volume. Note that while the 103/cell, pM, and nM scales apply to both the gray and black lines, the pmol/L tissue scales apply only to the black lines; the normalization is different for the gray lines (see Figure 3 for the correct scales).(D–F) Free (unbound) VEGF concentration in the interstitial space. *VEGF121 secreted at the same rate as VEGF165.(G–I) The concentration of VEGF inhibitor in the interstitial space, or density of Neuropilin on the blood vessel endothelial cell surface.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1761657&req=5

pcbi-0020180-g008: VEGF Signaling Inhibition Is Effective for a Shorter Period of Time for a Tissue with a Higher Microvascular Density(A–C) VEGF–VEGFR2 complex formation on the endothelial cells following each of the three treatments. The tumor modeled here expresses 10,000 VEGFR2 and 100,000 Neuropilin per endothelial cell. Gray lines represent the case of 2% vascular volume, as depicted in Figure 3; the black lines represent 4.2% vascular volume. Note that while the 103/cell, pM, and nM scales apply to both the gray and black lines, the pmol/L tissue scales apply only to the black lines; the normalization is different for the gray lines (see Figure 3 for the correct scales).(D–F) Free (unbound) VEGF concentration in the interstitial space. *VEGF121 secreted at the same rate as VEGF165.(G–I) The concentration of VEGF inhibitor in the interstitial space, or density of Neuropilin on the blood vessel endothelial cell surface.
Mentions: The vascular density appears to range widely for breast cancer, with 100–500 capillaries/mm2 cross-sectional area of tissue measured in different tumor samples [35]. Many studies of vascular density in breast cancer have been performed (see [36] for a comprehensive list and review), and typical average values are 100–250 capillaries/mm2. For the capillary dimensions described above, a capillary density of 235 capillaries/mm2 gives a vascular volume of 2% cm3/cm3 tissue. This is lower than the microvascular volume of 5% (even allowing for volume of the endothelial cells) found in studies of vascular volume [37,38]. This volume would require a significantly higher capillary density (490 capillaries/mm2) for the average capillary size noted above. The reason for the discrepancy may be the inclusion in that study of larger microvessels, or the differences between the types of cancer studied. We performed most of the simulations (Figures 3–7) using the 2% vascular space, but also performed simulations for the larger vascular volume and included those for comparison (Figures 8 and 9).

Bottom Line: Using the first molecularly detailed computational model of VEGF and its receptors, we have shown previously that the VEGFR-Neuropilin interactions explain the observed differential effects of VEGF isoforms on VEGF signaling in vitro, and demonstrated potent VEGF inhibition by an antibody to Neuropilin-1 that does not block ligand binding but blocks subsequent receptor coupling.The model predicts that blockade of Neuropilin-VEGFR coupling is significantly more effective than other approaches in decreasing VEGF-VEGFR2 signaling.In addition, tumor types with different receptor expression levels respond differently to each of these treatments.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America. feilim@jhu.edu

ABSTRACT
Angiogenesis (neovascularization) plays a crucial role in a variety of physiological and pathological conditions including cancer, cardiovascular disease, and wound healing. Vascular endothelial growth factor (VEGF) is a critical regulator of angiogenesis. Multiple VEGF receptors are expressed on endothelial cells, including signaling receptor tyrosine kinases (VEGFR1 and VEGFR2) and the nonsignaling co-receptor Neuropilin-1. Neuropilin-1 binds only the isoform of VEGF responsible for pathological angiogenesis (VEGF165), and is thus a potential target for inhibiting VEGF signaling. Using the first molecularly detailed computational model of VEGF and its receptors, we have shown previously that the VEGFR-Neuropilin interactions explain the observed differential effects of VEGF isoforms on VEGF signaling in vitro, and demonstrated potent VEGF inhibition by an antibody to Neuropilin-1 that does not block ligand binding but blocks subsequent receptor coupling. In the present study, we extend that computational model to simulation of in vivo VEGF transport and binding, and predict the in vivo efficacy of several Neuropilin-targeted therapies in inhibiting VEGF signaling: (a) blocking Neuropilin-1 expression; (b) blocking VEGF binding to Neuropilin-1; (c) blocking Neuropilin-VEGFR coupling. The model predicts that blockade of Neuropilin-VEGFR coupling is significantly more effective than other approaches in decreasing VEGF-VEGFR2 signaling. In addition, tumor types with different receptor expression levels respond differently to each of these treatments. In designing human therapeutics, the mechanism of attacking the target plays a significant role in the outcome: of the strategies tested here, drugs with similar properties to the Neuropilin-1 antibody are predicted to be most effective. The tumor type and the microenvironment of the target tissue are also significant in determining therapeutic efficacy of each of the treatments studied.

Show MeSH
Related in: MedlinePlus