Quantitative methods to regulate angiogenesis: applications to cancer and cardiovascular disease

Weddell, Jared Colin

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https://hdl.handle.net/2142/88030 Copy

Description

Title

Quantitative methods to regulate angiogenesis: applications to cancer and cardiovascular disease

Author(s)

Weddell, Jared Colin

Issue Date

2015-07-14

Director of Research (if dissertation) or Advisor (if thesis)

Imoukhuede, Princess I

Department of Study

Bioengineering

Discipline

Bioengineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Angiogenesis
• heterogeneity
• breast cancer
• cardiovascular disease

Abstract

Angiogenesis, the growth of new microvasculature from pre-existing blood vessels, is essential for tumor growth and metastasis in several cancers, including breast cancer. The vascular endothelial growth factor (VEGF) is the primary signaling molecule promoting angiogenesis. As such, the VEGF signaling axis is a potential target to inhibit tumor angiogenesis. However, full tumor vascular inhibition has yet to be achieved, attributed to the complexity of the vascular environment. Conversely, the ability to induce angiogenesis to vascularize ischemic tissue would provide treatment options for vascular diseases, including peripheral artery disease and coronary artery disease. Hemodynamic forces drive vascular disease progression, and contribute to the induction and directionality of vessel growth. Thus, full vascular control can be obtained by targeting VEGF signaling to inhibit angiogenesis and by targeting hemodynamic forces to promote angiogenesis. To this end, I developed computational approaches to individually understand the effects of VEGF signaling and hemodynamic forces on angiogenesis. Firstly, VEGF signaling models enable anti-angiogenic treatment efficacy to be correlated to features of cells or the microenvironment, which can help us understand a major challenge in tumor vascular inhibition: tumor heterogeneity. Indeed, cell population heterogeneity has been identified as an important consideration in cellular response to VEGF treatment, and is also a major factor in angiogenic drug resistances. However, there are few techniques available to represent and explore how heterogeneity is linked to population response. Recent high-throughput genomic, proteomic, and cellomic approaches offer opportunities for profiling heterogeneity on several scales. We have recently examined heterogeneity in VEGFR membrane localization in endothelial cells. We and others processed the heterogeneous data through ensemble averaging and integrated the data into computational models of anti-angiogenic drug effects in breast cancer. Here we show that additional modeling insight can be gained when cellular heterogeneity is considered. We present comprehensive statistical and computational methods for analyzing cellomic data sets and integrating them into deterministic models. We present a novel method for optimizing the fit of statistical distributions to heterogeneous data sets to preserve important data and exclude outliers. We compare methods of representing heterogeneous data and show methodology can affect model predictions up to 3.9-fold. We find that VEGF levels, a target for tuning angiogenesis, are more sensitive to VEGFR1 cell surface levels than VEGFR2; updating VEGFR1 levels in the tumor model gave a 64% change in free VEGF levels in the blood compartment, whereas updating VEGFR2 levels gave a 17% change. Furthermore, we find that subpopulations of tumor cells and tumor endothelial cells (tEC) expressing high levels of VEGFR (> 35,000 VEGFR/cell) negate anti-VEGF treatments. We show that lowering the VEGFR membrane insertion rate for these subpopulations recovers the anti-angiogenic effect of anti-VEGF treatment, revealing new treatment targets for specific tumor cell subpopulations. This novel method of characterizing heterogeneous distributions shows for the first time how different representations of the same data set lead to different predictions of drug efficacy. Secondly, to understand how to better promote angiogenesis, accurate quantification of hemodynamic forces is essential. Numerical simulations allow for this quantification. However, due to the complexity of numerical simulations, blood is often assumed to be Newtonian, despite being non-Newtonian in nature. To ensure accurate representation of hemodynamic forces, we compare hemodynamics between Newtonian and non-Newtonian models of blood. We test these models in both healthy and atherosclerotic arteries. For the non-Newtonian model, we employ a shear-rate dependent fluid (SDF) constitutive model, based on the works by Yasuda et al in 1981. We first verify our stabilized finite element numerical method with the benchmark lid-driven cavity flow problem. Numerical simulations show that the Newtonian model gives similar velocity profiles in the 2-dimensional cavity given different height and width dimensions, given the same Reynolds number. Conversely, the SDF model gave dissimilar velocity profiles, differing from the Newtonian velocity profiles by up to 25% in velocity magnitudes. This difference can affect estimation in platelet distribution within blood vessels or magnetic nanoparticle delivery. Wall shear stress (WSS) is an important quantity involved in vascular remodeling through integrin and adhesion molecule mechanotransduction. The SDF model gave a 7.3-fold greater WSS than the Newtonian model at the top of the 3-dimensional cavity. The SDF model gave a 37.7-fold greater WSS than the Newtonian model at artery walls located immediately after bifurcations in the idealized femoral artery tree. The pressure drop across arteries reveals arterial sections highly resistive to flow which correlates with stenosis formation. Numerical simulations give the pressure drop across the idealized femoral artery tree with the SDF model which is approximately 2.3-fold higher than with the Newtonian model. In atherosclerotic lesion models, the SDF model gives over 1 Pa higher WSS than the Newtonian model, a difference correlated with over twice as many adherent monocytes to endothelial cells from the Newtonian model compared to the SDF model. Together, these computational approaches provide a necessary step towards obtaining full vascular control, through inhibiting or promoting angiogenesis, respectively.

Graduation Semester

2015-8

Type of Resource

text

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http://hdl.handle.net/2142/88030

Copyright and License Information

Copyright 2015 Jared Weddell

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