Effect of Tumor Microenvironment on Tumor VEGF During Anti-VEGF Treatment: Systems Biology Predictions

Background

Vascular endothelial growth factor (VEGF) is known to be a potent promoter of angiogenesis under both physiological and pathological conditions. Given its role in regulating tumor vascularization, VEGF has been targeted in various cancer treatments, and anti-VEGF therapy has been used clinically for treatment of several types of cancer. Systems biology approaches, particularly computational models, provide insight into the complexity of tumor angiogenesis. These models complement experimental studies and aid in the development of effective therapies targeting angiogenesis.

Methods

We developed an experiment-based, molecular-detailed compartment model of VEGF kinetics and transport to investigate the distribution of two major VEGF isoforms (VEGF₁₂₁ and VEGF₁₆₅) in the body. The model is applied to predict the dynamics of tumor VEGF and, importantly, to gain insight into how tumor VEGF responds to an intravenous injection of an anti-VEGF agent.

Results

The model predicts that free VEGF in the tumor interstitium is seven to 13 times higher than plasma VEGF and is predominantly in the form of VEGF₁₂₁ (>70%), predictions that are validated by experimental data. The model also predicts that tumor VEGF can increase or decrease with anti-VEGF treatment depending on tumor microenvironment, pointing to the importance of personalized medicine.

Conclusions

This computational study suggests that the rate of VEGF secretion by tumor cells may serve as a biomarker to predict the patient population that is likely to respond to anti-VEGF treatment. Thus, the model predictions have important clinical relevance and may aid clinicians and clinical researchers seeking interpretation of pharmacokinetic and pharmacodynamic observations and optimization of anti-VEGF therapies.

Funding

This work was supported by the National Institutes of Health (R01 CA138264; to ASP and F32 CA154213 to SDF) and the UNCF-Merck Science Initiative (to SDF).

Supplementary Material

Supplementary Data:

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Affiliations of authors:Department of Biomedical Engineering (SDF, ASP) and Sidney Kimmel Comprehensive Cancer Center (ASP), Johns Hopkins University School of Medicine, Baltimore MD.

^{Corresponding author.}

Correspondence to: Stacey D. Finley, PhD, Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, 720 Rutland Ave, 613 Traylor Research Bldg, Baltimore, MD 21205 (e-mail ude.uhj@yelnifds).

Received 2012 Jun 28; Revised 2013 Mar 8; Accepted 2013 Mar 22.

Abstract

Background

Methods

Results

Conclusions

Abstract

Vascular endothelial growth factor (VEGF) promotes various processes involved in angiogenesis, including endothelial cell proliferation, adhesion, migration, and chemotaxis (1). Angiogenesis is a hallmark of cancer (2) and has been targeted by various cancer therapies, with a focused effort on drugs that inhibit VEGF. Several antiangiogenic agents have been approved by the US Food and Drug Administration (FDA) to treat various cancers and other diseases. Bevacizumab (Genentech, South San Francisco, CA), a recombinant humanized monoclonal antibody to VEGF, is approved for the treatment of metastatic colorectal and kidney cancer, glioblastoma, and non–small cell lung cancer. Ziv-aflibercept (Regeneron, Tarrytown, NY), a soluble decoy receptor for VEGF, is an FDA-approved agent for the treatment of metastatic colorectal cancer and is currently in clinical trials for the treatment of several other cancer types. Other FDA-approved antiangiogenic cancer therapeutics include axitinib, pazopanib, regorafenib, sorafenib, and sunitinib. These agents are small molecule kinase inhibitors with various targets such as VEGF receptors, platelet-derived growth factor receptors, fibroblast growth factor receptors, and Raf kinase.

Systems biology approaches are useful in gaining a broader understanding of the complexity of angiogenesis. Computational models can be applied to generate and test biological hypotheses and can aid in the development of effective therapies that target angiogenesis (3). Additionally, models can provide a framework to predict promising drug targets and identify patient populations that will respond to a particular therapy.

We have developed a molecular-detailed compartment model that is useful in understanding VEGF dynamics in the body. The model is based on detailed biochemical kinetics and molecular transport and has been validated against available experimental data. It is a predictive tool that can provide insight into the distribution of VEGF in the body and the effects of systemic administration of anti-VEGF therapeutics, such as bevacizumab and aflibercept. We have applied the model to understand and explain clinical observations of anti-VEGF agents (4) and predict the effect of the drugs (5,6). Here, we present three important model predictions regarding the pretreatment levels of VEGF₁₂₁ and VEGF₁₆₅ and the dynamic response of plasma and tumor VEGF to anti-VEGF treatment. We compare our results with available experimental data and propose clinical applications of the model predictions.

* See Supplementary Table 1 (available online).

† No data available.

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Notes

SDF and ASP designed the study. SDF performed the simulations and wrote the manuscript. All authors analyzed results and prepared the final manuscript.

We thank Drs Feilim Mac Gabhann and Herbert I. Hurwitz for their constructive comments and members of our laboratory for helpful discussions.

Notes

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