Please use this identifier to cite or link to this item:
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 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.
DOI: 10.1126/sciadv.aax3223
ORCID: 0000-0001-6080-7998
Journal: Science Advances
PubMed URL: 32133394
Type: Journal Article
Appears in Collections:Journal articles

Show full item record

Page view(s)

checked on Jan 27, 2023

Google ScholarTM


Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.