Embolization-on-a-chip: novel vascularized liver tumor model for evaluation of cellular and cytokine response to embolic agents
Source abstract
Abstract
Abstract Blood vessel embolization is a well-established treatment modality for liver cancer. Novel shear-thinning hydrogels (STH) have been developed to address the need for safer and more effective local delivery of embolic agents and therapeutics. However, embolization therapies are currently optimized in animal models, which often differ from humans at the cellular, tissue, and organ levels. We aim to evaluate the efficacy of novel embolic agents such as STH using a human-relevant in vitro model that recapitulates human hepatocellular carcinoma capillary networks. A vascularized human liver-tumor-on-a-chip model was developed to assess embolic agent performance. The effects of drug-eluting STH (DESTH) on tumor cell viability, surface marker expression, vasculature morphology, and cytokine responses were evaluated. To study the effects of embolization on microvasculature morphology independent of the chemotherapy compound, we evaluated the effect of different drug-free embolic agents on the vascular tumor microenvironment under flow conditions. DESTH treatment induced tumor cell death, downregulated the expression of epithelial cell adhesion molecules in HepG2, increased levels of cytokines such as interleukin-4 (IL-4), granulocyte-macrophage colony-stimulating factor, and vascular endothelial growth factor, and decreased albumin secretion. Furthermore, different embolic agents exert distinct effects on microvascular morphology, with STH causing complete regression of the microvascular networks. This vascularized liver tumor-on-a-chip model enables human-relevant, real-time assessment of embolic agent efficacy and vascular response and can be applied for the development of innovative and effective embolization therapies for liver cancer.
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Research context
OpenAlex topics: Cancer, Hypoxia, and Metabolism · Renal cell carcinoma treatment · Pancreatic and Hepatic Oncology Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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