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Dan G. Duda, Lance L. Munn, Rakesh K. Jain, Can We Identify Predictive Biomarkers for Antiangiogenic Therapy of Cancer Using Mathematical Modeling?, JNCI: Journal of the National Cancer Institute, Volume 105, Issue 11, 5 June 2013, Pages 762–765, https://doi.org/10.1093/jnci/djt114
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Angiogenesis (the term referring generically to new blood vessel formation) is a hallmark of all solid tumors ( 1 ), and vascular endothelial growth factor (VEGF) is the most prevalent and potent angiogenic growth factor in these tumors ( 2 , 3 ). For this reason, there have been intense efforts to develop therapies that target the VEGF pathway. Currently, there are nine antiangiogenic drugs approved by the US Food and Drug Administration and one pending approval ( Table 1 ) ( 4 ). These agents are antibodies that target VEGF itself (bevacizumab and aflibercept) or its receptor VEGFR2 (ramucirumab), or are tyrosine kinase inhibitors that interfere with VEGFR2 signaling as well as other receptor and cellular kinases. Introduction of these new drugs over the last decade has established antiangiogenic therapy as a novel therapeutic modality, but their implementation has raised several important questions. Why do they work in some cancers and not others? Is the mechanism of action similar when targeting the ligand versus the receptors, or when using specific versus multitargeted drugs? Can any of them completely block the VEGF pathway? What mediates the inevitable escape from therapy? Can we find biomarkers to identify patients who will benefit from these agents or pathways that must be targeted when tumors become refractory to a given antiangiogenic agent?