Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/22747
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
Issue Date: Feb-2020
EDate: 2020
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.
URI: http://ahro.austin.org.au/austinjspui/handle/1/22747
DOI: 10.1126/sciadv.aax3223
ORCID: 0000-0001-6080-7998
0000-0001-9569-0478
0000-0001-8099-3863
0000-0002-7650-389X
0000-0003-3100-9668
0000-0003-3627-7397
0000-0002-3898-950X
0000-0001-5329-280X
0000-0001-5516-1280
PubMed URL: 32133394
Type: Journal Article
Appears in Collections:Journal articles

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