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Translational models of tumor angiogenesis: A nexus of in silico and in vitro models
Authors:Shirin Soleimani  Milad Shamsi  Mehran Akbarpour Ghazani  Hassan Pezeshgi Modarres  Karolina Papera Valente  Mohsen Saghafian  Mehdi Mohammadi Ashani  Mohsen Akbari  Amir Sanati-Nezhad
Affiliation:1. BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada;2. Center for BioEngineering Research and Education, University of Calgary, Calgary, AB T2N 1N4, Canada;3. Department of Mechanical Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran;4. Laboratory for Innovations in MicroEngineering (LiME), Department of Mechanical Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada;5. Division of Medical Sciences, University of Victoria, Victoria, BC V8P 5C2, Canada
Abstract:Emerging evidence shows that endothelial cells are not only the building blocks of vascular networks that enable oxygen and nutrient delivery throughout a tissue but also serve as a rich resource of angiocrine factors. Endothelial cells play key roles in determining cancer progression and response to anti-cancer drugs. Furthermore, the endothelium-specific deposition of extracellular matrix is a key modulator of the availability of angiocrine factors to both stromal and cancer cells. Considering tumor vascular network as a decisive factor in cancer pathogenesis and treatment response, these networks need to be an inseparable component of cancer models. Both computational and in vitro experimental models have been extensively developed to model tumor-endothelium interactions. While informative, they have been developed in different communities and do not yet represent a comprehensive platform. In this review, we overview the necessity of incorporating vascular networks for both in vitro and in silico cancer models and discuss recent progresses and challenges of in vitro experimental microfluidic cancer vasculature-on-chip systems and their in silico counterparts. We further highlight how these two approaches can merge together with the aim of presenting a predictive combinatorial platform for studying cancer pathogenesis and testing the efficacy of single or multi-drug therapeutics for cancer treatment.
Keywords:Microfluidic-based cancer models  Computational cancer models  Tumor-educated vasculature
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