Tissue Eng Lecture_102413

5050 for tumor cells vegf neutralizing agents are

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Unformatted text preview: predictions: VEGF levels and distribution Whole-body Tumor Predicted Experimental Normal 3.2 pM 0.3 - 3.0 Blood 2.0 pM 0.4 - 3.0 Tumor 26.0 pM 8.0 - 389.0 Finley and Popel, J Natl Cancer Inst, 2013 71 Clinical data: VEGF levels 72 Model predictions: VEGF levels and distribution Whole-body Tumor Predicted Experimental Normal 3.2 pM 0.3 - 3.0 Blood 2.0 pM 0.4 - 3.0 Tumor 26.0 pM 8.0 - 389.0 Unbound VEGF in the tumor: greatly exceeds plasma VEGF and is predominantly in the form of VEGF121 73 Role of specific VEGF isoforms FIG. 8. Proposed gradient model of tumorigenic VEGF signaling. VEGF120 produces a diffuse signal (light blue) which recruits peripheral vessels but does little to vascularize the tumor itself. VEGF164 can both recruit vessels with a partially diffusible signal and vascularize the tumor with a partially cell-associated signal. VEGF188 fails to adequately recruit the host vasculature, but vascular endothelium which is captured forms a hypervascular capillary network due to the high local concentration of VEGF. Recruit host vasculature Mediate intermediate response nocopies wild-type expression of VEGF in terms of tumor growth, local densities of VEGF, and vascular density and morphology. This is what would have been predicted from numerous studies of the isoforms and fits with its predominance in studies of isoform-specific expression patterns. There is some evidence in our data for a selective advantage in expression of all of the isoforms: when cells expressing all of the isoforms were mixed, it results in tumors larger than those Grunstein, et any singleCell Biol, 2000 of the isoforms. This produced from al., Mol partial mixture Vascularize tumor It is important, however, to note that the high local VEGF levels and membrane association of the heparin-binding forms of VEGF have functional importance. Evidence for this is seen in the decreased internal vascular density of VEGF120-expressing tumors and in the evident cooperativity in VEGF120 and VEGF188 expression, demonstrated by their coselection in the mixing experiments shown in Fig. 7. These data would suggest a split in the roles that the VEGF isoforms generally 74 play, where VEGF120 is used to recruit vessels to the site of The model predicts targets for anti-angiogenic therapeutics 75 Model predictions: response to anti-VEGF treatment Plasma VEGF initially decreases, and then rebounds above the pretreatment level 76 Clinical data: response to anti-VEGF treatment adapted from Stefanini et al., Cancer Res, 2010 77 Explanation of response to anti-VEGF treatment 78 The model explains clinical observations 79 Quantifying the effect of treatment • “Fold-change” of free (unbound) VEGF in the tumor • • • Quantifies the response to anti-VEGF treatment Compares free VEGF before treatment and VEGF at 3 weeks posttreatment [VEGF]t=3 wks Fold-change = [VEGF]t<0 • Values of the fold-change: • • • = 1: No change > 1: Free VEGF increased < 1: Free VEGF decreased, “therapeutic response” 80 Effect of tumor secretion of VEGF Fold-change = Relative secretion ratio [VEGF]t=3 wks [VEGF]t<0 Therapeutic response 81 Effect of tumor secretion of VEGF Relative secretion ratio Therapeutic response 82 Effect of tumor secretion of VEGF Relative secretion ratio Absolute secretion rate Therapeutic response Response of tumor VEGF depends on tumor microenvironment 83 The model predicts effects of tumor-specific properties 84 Explanation of response to anti-VEGF therapy 85 Explanation of response to anti-VEGF therapy 86 The model helps design relevant experimental studies 87 Summary • Computational model of VEGF-VEGFR interactions provide insight into the distribution of VEGF in the body • The model predicts the level of tumor VEGF following antiVEGF treatment • Anti-VEGF treatment robustly decreases interstitial VEGF for most tumor parameters • The model predicts that anti-VEGF therapy may have countertherapeutic effect for some tumors • Clinical applications: - Anti-VEGF mechanism of action Personalized medicine 88 Conclusions • Angiogenesis is a complex biological process that lends itself to being studied from a systems-level perspective - Model many stimuli, multiple steps, several cell types Integrate experimental data • • Models of angiogenesis aim to study each step in the process • These models can be applied to understand biology and investigate therapeutic strategies There are many opportunities for new research, as the focus of current models is on the first three steps 89 Logsdon, Finley, Popel, and Mac Gabhann. J Cell Molec Med. 2013 (In press) 90...
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