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|Title:||Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma.|
|Authors:||Wang, Jing;Wuethrich, Alain;Sina, Abu Ali Ibn;Lane, Rebecca E;Lin, Lynlee L;Wang, Yuling;Cebon, Jonathan S;Behren, Andreas;Trau, Matt|
|Affiliation:||Dermatology Research Centre, University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD 4102, Australia|
Centre for Personalized Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
School of Cancer Medicine, La Trobe University, Heidelberg, VIC 3084, Australia
School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD 4072, Australia
Department of Medicine, University of Melbourne, Heidelberg, VIC 3084, Australia
Department of Molecular Sciences, ARC Centre of Excellence for Nanoscale BioPhotonics, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia
Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia
|Citation:||Science advances 2020-02; 6(9): eaax3223|
|Abstract:||Monitoring targeted therapy in real time for cancer patients could provide vital information about the development of drug resistance and improve therapeutic outcomes. Extracellular vesicles (EVs) have recently emerged as a promising cancer biomarker, and EV phenotyping shows high potential for monitoring treatment responses. Here, we demonstrate the feasibility of monitoring patient treatment responses based on the plasma EV phenotypic evolution using a multiplex EV phenotype analyzer chip (EPAC). EPAC incorporates the nanomixing-enhanced microchip and the multiplex surface-enhanced Raman scattering (SERS) nanotag system for direct EV phenotyping without EV enrichment. In a preclinical model, we observe the EV phenotypic heterogeneity and different phenotypic responses to the treatment. Furthermore, we successfully detect cancer-specific EV phenotypes from melanoma patient plasma. We longitudinally monitor the EV phenotypic evolution of eight melanoma patients receiving targeted therapy and find specific EV profiles involved in the development of drug resistance, reflecting the potential of EV phenotyping for monitoring treatment responses.|
|Appears in Collections:||Journal articles|
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