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3D bioprinting: improvingin vitromodels of metastasis with heterogeneous tumor microenvironments
Author(s) -
Jacob L. Albritton,
Jordan S. Miller
Publication year - 2017
Publication title -
disease models and mechanisms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.327
H-Index - 83
eISSN - 1754-8411
pISSN - 1754-8403
DOI - 10.1242/dmm.025049
Subject(s) - tumor microenvironment , 3d bioprinting , metastasis , in vivo , cancer , tumor heterogeneity , mechanism (biology) , biology , computer science , computational biology , cancer research , tumor cells , tissue engineering , microbiology and biotechnology , philosophy , epistemology , genetics
Even with many advances in treatment over the past decades, cancer still remains a leading cause of death worldwide. Despite the recognized relationship between metastasis and increased mortality rate, surprisingly little is known about the exact mechanism of metastatic progression. Currently available in vitro models cannot replicate the three-dimensionality and heterogeneity of the tumor microenvironment sufficiently to recapitulate many of the known characteristics of tumors in vivo Our understanding of metastatic progression would thus be boosted by the development of in vitro models that could more completely capture the salient features of cancer biology. Bioengineering groups have been working for over two decades to create in vitro microenvironments for application in regenerative medicine and tissue engineering. Over this time, advances in 3D printing technology and biomaterials research have jointly led to the creation of 3D bioprinting, which has improved our ability to develop in vitro models with complexity approaching that of the in vivo tumor microenvironment. In this Review, we give an overview of 3D bioprinting methods developed for tissue engineering, which can be directly applied to constructing in vitro models of heterogeneous tumor microenvironments. We discuss considerations and limitations associated with 3D printing and highlight how these advances could be harnessed to better model metastasis and potentially guide the development of anti-cancer strategies.

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