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The liver metastatic niche: modelling the extracellular matrix in metastasis.
- Source :
-
Disease models & mechanisms [Dis Model Mech] 2021 Apr 01; Vol. 14 (4). Date of Electronic Publication: 2021 Apr 15. - Publication Year :
- 2021
-
Abstract
- Dissemination of malignant cells from primary tumours to metastatic sites is a key step in cancer progression. Disseminated tumour cells preferentially settle in specific target organs, and the success of such metastases depends on dynamic interactions between cancer cells and the microenvironments they encounter at secondary sites. Two emerging concepts concerning the biology of metastasis are that organ-specific microenvironments influence the fate of disseminated cancer cells, and that cancer cell-extracellular matrix interactions have important roles at all stages of the metastatic cascade. The extracellular matrix is the complex and dynamic non-cellular component of tissues that provides a physical scaffold and conveys essential adhesive and paracrine signals for a tissue's function. Here, we focus on how extracellular matrix dynamics contribute to liver metastases - a common and deadly event. We discuss how matrix components of the healthy and premetastatic liver support early seeding of disseminated cancer cells, and how the matrix derived from both cancer and liver contributes to the changes in niche composition as metastasis progresses. We also highlight the technical developments that are providing new insights into the stochastic, dynamic and multifaceted roles of the liver extracellular matrix in permitting and sustaining metastasis. An understanding of the contribution of the extracellular matrix to different stages of metastasis may well pave the way to targeted and effective therapies against metastatic disease.<br />Competing Interests: Competing interests The authors declare no competing or financial interests.<br /> (© 2021. Published by The Company of Biologists Ltd.)
Details
- Language :
- English
- ISSN :
- 1754-8411
- Volume :
- 14
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Disease models & mechanisms
- Publication Type :
- Academic Journal
- Accession number :
- 33973625
- Full Text :
- https://doi.org/10.1242/dmm.048801