Plasma-derived extracellular vesicle analysis and deconvolution enable prediction and tracking of melanoma checkpoint blockade outcome
Name
eabb3461.full.pdf
Description
Published version
Size
985.46 KB
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Unknown
Checksum (MD5)
7164c6c48a5202c4e468a6aeaff8062d
Author(s)
Shi, Alvin
Kasumova, Gyulnara G
Michaud, William A
Cintolo-Gonzalez, Jessica
Díaz-Martínez, Marta
Ohmura, Jacqueline
Mehta, Arnav
Chien, Isabel
Frederick, Dennie T
Cohen, Sonia
Date Issued
2020
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Version
Final published version
Abstract
Immune checkpoint inhibitors (ICIs) show promise, but most patients do not respond. We identify and validate biomarkers from extracellular vesicles (EVs), allowing non-invasive monitoring of tumor- intrinsic and host immune status, as well as a prediction of ICI response. We undertook transcriptomic profiling of plasma-derived EVs and tumors from 50 patients with metastatic melanoma receiving ICI, and validated with an independent EV-only cohort of 30 patients. Plasma-derived EV and tumor transcriptomes correlate. EV profiles reveal drivers of ICI resistance and melanoma progression, exhibit differentially expressed genes/pathways, and correlate with clinical response to ICI. We created a Bayesian probabilistic deconvolution model to estimate contributions from tumor and non-tumor sources, enabling interpretation of differentially expressed genes/pathways. EV RNA-seq mutations also segregated ICI response. EVs serve as a non-invasive biomarker to jointly probe tumor-intrinsic and immune changes to ICI, function as predictive markers of ICI responsiveness, and monitor tumor persistence and immune activation.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1126/sciadv.abb3461
