dc.contributor.author | Hapach, Lauren A. | |
dc.contributor.author | Mosier, Jenna A. | |
dc.contributor.author | Wang, Wenjun | |
dc.contributor.author | Reinhart-King, Cynthia A. | |
dc.date.accessioned | 2020-05-29T12:48:09Z | |
dc.date.available | 2020-05-29T12:48:09Z | |
dc.date.issued | 2019-08-21 | |
dc.identifier.citation | Hapach, 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/s41698-019-0092-3 | en_US |
dc.identifier.other | eISSN 397-768X | |
dc.identifier.uri | http://hdl.handle.net/1803/10022 | |
dc.description.abstract | While 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.sponsorship | This 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.iso | en_US | en_US |
dc.publisher | NPJ Precision Oncology | en_US |
dc.rights | Open 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.uri | https://www.nature.com/articles/s41698-019-0092-3#additional-information | |
dc.subject | TUMOR-CELL EXTRAVASATION | en_US |
dc.subject | ON-A-CHIP | en_US |
dc.subject | CANCER-CELLS | en_US |
dc.subject | IN-VITRO | en_US |
dc.subject | MICROFLUIDIC DEVICE | en_US |
dc.subject | TISSUE MODEL | en_US |
dc.subject | MECHANISMS | en_US |
dc.subject | INVASION | en_US |
dc.subject | COLLAGEN | en_US |
dc.subject | PATIENT | en_US |
dc.title | Engineered models to parse apart the metastatic cascade | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1038/s41698-019-0092-3 | |