The use of microphysiological systems to model metastatic cancer.

Autor:	Jackson CE; Materials Science and Engineering, The Kroto Research Institute, University of Sheffield, Sheffield S3 7HQ, United Kingdom.; Insigneo Institute for In Silico Medicine, The Pam Liversidge Building, University of Sheffield, Sheffield S1 3JD, United Kingdom., Green NH; Materials Science and Engineering, The Kroto Research Institute, University of Sheffield, Sheffield S3 7HQ, United Kingdom.; Insigneo Institute for In Silico Medicine, The Pam Liversidge Building, University of Sheffield, Sheffield S1 3JD, United Kingdom., English WR; Norwich Medical School, University of East Anglia, Norwich NR3 7TJ, United Kingdom., Claeyssens F; Materials Science and Engineering, The Kroto Research Institute, University of Sheffield, Sheffield S3 7HQ, United Kingdom.; Insigneo Institute for In Silico Medicine, The Pam Liversidge Building, University of Sheffield, Sheffield S1 3JD, United Kingdom.
Jazyk:	angličtina
Zdroj:	Biofabrication [Biofabrication] 2024 Apr 18; Vol. 16 (3). Date of Electronic Publication: 2024 Apr 18.
DOI:	10.1088/1758-5090/ad3b70
Abstrakt:	Cancer is one of the leading causes of death in the 21st century, with metastasis of cancer attributing to 90% of cancer-related deaths. Therefore, to improve patient outcomes there is a need for better preclinical models to increase the success of translating oncological therapies into the clinic. Current traditional static in vitro models lack a perfusable network which is critical to overcome the diffusional mass transfer limit to provide a mechanism for the exchange of essential nutrients and waste removal, and increase their physiological relevance. Furthermore, these models typically lack cellular heterogeneity and key components of the immune system and tumour microenvironment. This review explores rapidly developing strategies utilising perfusable microphysiological systems (MPS) for investigating cancer cell metastasis. In this review we initially outline the mechanisms of cancer metastasis, highlighting key steps and identifying the current gaps in our understanding of the metastatic cascade, exploring MPS focused on investigating the individual steps of the metastatic cascade before detailing the latest MPS which can investigate multiple components of the cascade. This review then focuses on the factors which can affect the performance of an MPS designed for cancer applications with a final discussion summarising the challenges and future directions for the use of MPS for cancer models. (Creative Commons Attribution license.)
Databáze:	MEDLINE
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