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用于剖析转移级联反应的工程模型。

Engineered models to parse apart the metastatic cascade.

作者信息

Hapach Lauren A, Mosier Jenna A, Wang Wenjun, Reinhart-King Cynthia A

机构信息

1Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14853 USA.

2Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235 USA.

出版信息

NPJ Precis Oncol. 2019 Aug 21;3:20. doi: 10.1038/s41698-019-0092-3. eCollection 2019.

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.

摘要

虽然利用体外细胞培养和体内动物模型在研究转移的遗传和细胞方面已取得了相当大的进展,但由于转移各步骤的驱动机制较为复杂,目前仍相对不清楚。此外,在理解转移级联反应中某一步骤的细胞适应性如何与在后续其他步骤中存活的能力相关联方面进展甚微。工程化模型结合了定制生物材料和微加工等工具来模拟人类疾病进展,再加上先进的定量方法,使得能够与人类患者样本及体内研究进行比较。在此,我们综述了最近开发的新型工具和技术,这些工具和技术利用原发性患者样本和高度代表性的微环境来剖析转移级联反应的关键特征,以推动个性化医疗和精准肿瘤学的发展。尽管在忠实模拟体内微环境的同时,还需要改进以提高其可操作性和可及性,但这些模型是理解癌症生物学、推进药物筛选以及促进治疗方法开发的强大实验平台。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc25/6704099/279acb8fb3f4/41698_2019_92_Fig1_HTML.jpg

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