TY - JOUR
T1 - Combined multi-omics and multi-spectral profiling of plasma extracellular vesicles reveals liquid biopsy biomarkers for glioma diagnosis
AU - Robinson, Stephen David
AU - Haile, Biniam Tsegay
AU - Reily-Bell, Matthew
AU - Iwanowytsch, Olivia
AU - Palmer, Siobhan
AU - Nørøxe, Dorte Schou
AU - Filippou, Panagiota S
AU - Renaut, Joanna
AU - Lazarus, Alan
AU - Antoniou, Georgios
AU - Samuels, Mark
AU - Vella, Viviana
AU - Filippopoulou, Chrysa
AU - Jones, William
AU - Jung, Josephine
AU - Li, Xiaoou
AU - Ji, Nan
AU - Zhang, Yang
AU - Azam, Aleena
AU - Skjoeth-Rasmussen, Jane
AU - Lassen, Ulrik
AU - Saraiva, Adriana
AU - Taha, Ahmad
AU - Slatter, Tania
AU - Jones, Greg
AU - Katare, Rajesh
AU - Butler, Holly J
AU - Baker, Matthew J
AU - Hadjidemetriou, Marilena
AU - Gilbert, Duncan
AU - Towler, Benjamin
AU - Ashkan, Keyoumars
AU - Critchley, Giles
AU - Pearl, Frances M G
AU - Giamas, Georgios
N1 - Copyright © 2026 The Author(s). Published by Elsevier Inc. All rights reserved.
PY - 2026/4/17
Y1 - 2026/4/17
N2 - Plasma small extracellular vesicles (sEVs) are a promising liquid biopsy tool. This study aims to delineate and validate a multimodal plasma sEV biomarker signature for glioma. We use size exclusion chromatography to separate sEVs from plasma (1 mL) and a combination of multi-spectral (Fourier transform infrared/Raman) and orthogonal multi-omics (proteomic/microRNA) approaches on 206 plasma samples (159 individuals) across three independent cohorts. We identify distinct glioma sEV biomolecular profiles, including differences in sEV protein/nucleic acid composition, and consistent alterations in 45 proteins and 20 microRNAs. Machine learning models derived from training cohort data achieve high diagnostic performance (areas under the curve [AUCs] 0.931-0.971), while external validation across independent cohorts confirms the signature's diagnostic potential, with 100% accuracy for the proteomic and multimodal signatures in the longitudinal cohort. Our findings, generated through a rigorous multi-cohort and multi-algorithmic framework, establish the potential of plasma sEV signatures as a clinically relevant diagnostic liquid biopsy approach for glioma.
AB - Plasma small extracellular vesicles (sEVs) are a promising liquid biopsy tool. This study aims to delineate and validate a multimodal plasma sEV biomarker signature for glioma. We use size exclusion chromatography to separate sEVs from plasma (1 mL) and a combination of multi-spectral (Fourier transform infrared/Raman) and orthogonal multi-omics (proteomic/microRNA) approaches on 206 plasma samples (159 individuals) across three independent cohorts. We identify distinct glioma sEV biomolecular profiles, including differences in sEV protein/nucleic acid composition, and consistent alterations in 45 proteins and 20 microRNAs. Machine learning models derived from training cohort data achieve high diagnostic performance (areas under the curve [AUCs] 0.931-0.971), while external validation across independent cohorts confirms the signature's diagnostic potential, with 100% accuracy for the proteomic and multimodal signatures in the longitudinal cohort. Our findings, generated through a rigorous multi-cohort and multi-algorithmic framework, establish the potential of plasma sEV signatures as a clinically relevant diagnostic liquid biopsy approach for glioma.
U2 - 10.1016/j.xcrm.2026.102744
DO - 10.1016/j.xcrm.2026.102744
M3 - Journal article
C2 - 41999751
SN - 2666-3791
SP - 102744
JO - Cell reports. Medicine
JF - Cell reports. Medicine
ER -