Please use this identifier to cite or link to this item:
https://ahro.austin.org.au/austinjspui/handle/1/22747
Title: | Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma. | Austin 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 | Date: | 2020 | Publication information: | Science Advances 2020; 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: | https://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 |
Journal: | Science Advances | PubMed URL: | 32133394 | Type: | Journal Article |
Appears in Collections: | Journal articles |
Show full item record
Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.