Varankar, Sagar S ; Bapat, Sharmila A (2019) Uncoupling Traditional Functionalities of Metastasis: The Parting of Ways with Real-Time Assays Journal of Clinical Medicine, 8 (7). p. 941. ISSN 2077-0383
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Official URL: http://doi.org/10.3390/jcm8070941
Related URL: http://dx.doi.org/10.3390/jcm8070941
Abstract
The experimental evaluation of metastasis overly focuses on the gain of migratory and invasive properties, while disregarding the contributions of cellular plasticity, extra-cellular matrix heterogeneity, niche interactions, and tissue architecture. Traditional cell-based assays often restrict the inclusion of these processes and warrant the implementation of approaches that provide an enhanced spatiotemporal resolution of the metastatic cascade. Time lapse imaging represents such an underutilized approach in cancer biology, especially in the context of disease progression. The inclusion of time lapse microscopy and microfluidic devices in routine assays has recently discerned several nuances of the metastatic cascade. Our review emphasizes that a complete comprehension of metastasis in view of evolving ideologies necessitates (i) the use of appropriate, context-specific assays and understanding their inherent limitations; (ii) cautious derivation of inferences to avoid erroneous/overestimated clinical extrapolations; (iii) corroboration between multiple assay outputs to gauge metastatic potential; and (iv) the development of protocols with improved in situ implications. We further believe that the adoption of improved quantitative approaches in these assays can generate predictive algorithms that may expedite therapeutic strategies targeting metastasis via the development of disease relevant model systems. Such approaches could potentiate the restructuring of the cancer metastasis paradigm through an emphasis on the development of next-generation real-time assays.
Item Type: | Article |
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Source: | Copyright of this article belongs to MDPI. |
Keywords: | metastasis; functional read-outs; metastatic modalities; live cell imaging; quantitative metrics |
ID Code: | 129668 |
Deposited On: | 02 Dec 2022 06:11 |
Last Modified: | 06 Dec 2022 10:21 |
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