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A Pathway to Personalizing Therapy for Metastases Using Liver-on-a-Chip Platforms
Metastasis accounts for most cancer-related deaths. The majority of solid cancers, including those of the breast, colorectum, prostate and skin, metastasize at significant levels to the liver due to its hemodynamic as well as tumor permissive microenvironmental properties. As this occurs prior to de...
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Published in: | Stem cell reviews and reports 2017-06, Vol.13 (3), p.364 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that cite this one |
Online Access: | Get full text |
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Summary: | Metastasis accounts for most cancer-related deaths. The majority of solid cancers, including those of the breast, colorectum, prostate and skin, metastasize at significant levels to the liver due to its hemodynamic as well as tumor permissive microenvironmental properties. As this occurs prior to detection and treatment of the primary tumor, we need to target liver metastases to improve patients' outcomes. Animal models, while proven to be useful in mechanistic studies, do not represent the heterogeneity of human population especially in drug metabolism lack proper human cell-cell interactions, and this gap between animals and humans results in costly and inefficient drug discovery. This underscores the need to accurately model the human liver for disease studies and drug development. Further, the occurrence of liver metastases is influenced by the primary tumor type, sex and race; thus, modeling these specific settings will facilitate the development of personalized/targeted medicine for each specific group. We have adapted such all-human 3D ex vivo hepatic microphysiological system (MPS) (a.k.a. liver-on-a-chip) to investigate human micrometastases. This review focuses on the sources of liver resident cells, especially the iPS cell-derived hepatocytes, and examines some of the advantages and disadvantages of these sources. In addition, this review also examines other potential challenges and limitations in modeling human liver. |
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ISSN: | 2629-3269 2629-3277 |
DOI: | 10.1007/s12015-017-9735-3 |