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Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma
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 moni...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043913/ https://www.ncbi.nlm.nih.gov/pubmed/32133394 http://dx.doi.org/10.1126/sciadv.aax3223 |
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author | Wang, Jing Wuethrich, Alain Sina, Abu Ali Ibn Lane, Rebecca E. Lin, Lynlee L. Wang, Yuling Cebon, Jonathan Behren, Andreas Trau, Matt |
author_facet | Wang, Jing Wuethrich, Alain Sina, Abu Ali Ibn Lane, Rebecca E. Lin, Lynlee L. Wang, Yuling Cebon, Jonathan Behren, Andreas Trau, Matt |
author_sort | Wang, Jing |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7043913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-70439132020-03-04 Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma Wang, Jing Wuethrich, Alain Sina, Abu Ali Ibn Lane, Rebecca E. Lin, Lynlee L. Wang, Yuling Cebon, Jonathan Behren, Andreas Trau, Matt Sci Adv Research Articles 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. American Association for the Advancement of Science 2020-02-26 /pmc/articles/PMC7043913/ /pubmed/32133394 http://dx.doi.org/10.1126/sciadv.aax3223 Text en Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (http://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Research Articles Wang, Jing Wuethrich, Alain Sina, Abu Ali Ibn Lane, Rebecca E. Lin, Lynlee L. Wang, Yuling Cebon, Jonathan Behren, Andreas Trau, Matt Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma |
title | Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma |
title_full | Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma |
title_fullStr | Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma |
title_full_unstemmed | Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma |
title_short | Tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma |
title_sort | tracking extracellular vesicle phenotypic changes enables treatment monitoring in melanoma |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043913/ https://www.ncbi.nlm.nih.gov/pubmed/32133394 http://dx.doi.org/10.1126/sciadv.aax3223 |
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