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Engineered models to parse apart the metastatic cascade

dc.contributor.authorHapach, Lauren A.
dc.contributor.authorMosier, Jenna A.
dc.contributor.authorWang, Wenjun
dc.contributor.authorReinhart-King, Cynthia A.
dc.date.accessioned2020-05-29T12:48:09Z
dc.date.available2020-05-29T12:48:09Z
dc.date.issued2019-08-21
dc.identifier.citationHapach, L.A., Mosier, J.A., Wang, W. et al. Engineered models to parse apart the metastatic cascade. npj Precis. Onc. 3, 20 (2019). https://doi.org/10.1038/s41698-019-0092-3en_US
dc.identifier.othereISSN 397-768X
dc.identifier.urihttp://hdl.handle.net/1803/10022
dc.description.abstractWhile considerable progress has been made in studying genetic and cellular aspects of metastasis with in vitro cell culture and in vivo animal models, the driving mechanisms of each step of metastasis are still relatively unclear due to their complexity. Moreover, little progress has been made in understanding how cellular fitness in one step of the metastatic cascade correlates with ability to survive other subsequent steps. Engineered models incorporate tools such as tailored biomaterials and microfabrication to mimic human disease progression, which when coupled with advanced quantification methods permit comparisons to human patient samples and in vivo studies. Here, we review novel tools and techniques that have been recently developed to dissect key features of the metastatic cascade using primary patient samples and highly representative microenvironments for the purposes of advancing personalized medicine and precision oncology. Although improvements are needed to increase tractability and accessibility while faithfully simulating the in vivo microenvironment, these models are powerful experimental platforms for understanding cancer biology, furthering drug screening, and facilitating development of therapeutics.en_US
dc.description.sponsorshipThis work was supported by funding from the National Institutes of Health (Project numbers: HL127499 and GM131178) and the National Science Foundation to C.A.R. (Award numbers: 1741588 and 1233827) and a Graduate Research Fellowship to L.A.H. (Cornell University NSF Grant DGE-1650441).en_US
dc.language.isoen_USen_US
dc.publisherNPJ Precision Oncologyen_US
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.source.urihttps://www.nature.com/articles/s41698-019-0092-3#additional-information
dc.subjectTUMOR-CELL EXTRAVASATIONen_US
dc.subjectON-A-CHIPen_US
dc.subjectCANCER-CELLSen_US
dc.subjectIN-VITROen_US
dc.subjectMICROFLUIDIC DEVICEen_US
dc.subjectTISSUE MODELen_US
dc.subjectMECHANISMSen_US
dc.subjectINVASIONen_US
dc.subjectCOLLAGENen_US
dc.subjectPATIENTen_US
dc.titleEngineered models to parse apart the metastatic cascadeen_US
dc.typeArticleen_US
dc.identifier.doi10.1038/s41698-019-0092-3


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