Unlocking the Diagnostic Potential of Extracellular Vesicles in Cancer.

Extracellular vesicles (EVs) are nanoscale lipid bilayer particles that are secreted by virtually all cells into biofluids;their cargo of nucleic acids, proteins, lipids, and glycans is tumor type and status informative and can be measuredvia minimally invasive liquid biopsy. Herein, we evaluate critically the EV-based assay diagnostic accuracy in bothsolid and hematologic malignancies, particularly with regard to exemplar biomarker panels and single-part analyticsfor pancreatic, prostate, breast, lung, and colorectal cancers as well as hematologic disorders. We emphasizenewer immune- and affinity-directed isolation/phenotyping technologies that are more selective and scalable thanconventional ultracentrifugation—e.g., microarray capture (EV Array), EV enrichment from cancer cells, magneticnanopore capture, and filtration/thermophoretic routes—along with single-EV readouts for increased informationalcontent. We also put EV diagnostics in a complementary role to established liquid biopsy modalities (e.g., ctDNA/cfDNA) to maximize detection and disease monitoring, rather than as alternatives. Finally, we outline theranosticapplications, including engineered EVs as delivery vehicles and stromal EVs as therapy resistance biomarkers andmediators, to bridge diagnostics with interventional strategies. This roadmap centers clinically meaningful use casesand methodological stringency to drive translation of EV assays into oncology practice

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-25
DOI
https://doi.org/10.5281/zenodo.18771755
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unlocking the Diagnostic Potential of Extracellular Vesicles in Cancer.

Milica Popović
Zenodo (CERN European Organization for Nuclear Research)
Extracellular vesicles in disease
article

Unlocking the Diagnostic Potential of Extracellular Vesicles in Cancer.

Milica Popović
article en

Abstract

Extracellular vesicles (EVs) are nanoscale lipid bilayer particles that are secreted by virtually all cells into biofluids;their cargo of nucleic acids, proteins, lipids, and glycans is tumor type and status informative and can be measuredvia minimally invasive liquid biopsy. Herein, we evaluate critically the EV-based assay diagnostic accuracy in bothsolid and hematologic malignancies, particularly with regard to exemplar biomarker panels and single-part analyticsfor pancreatic, prostate, breast, lung, and colorectal cancers as well as hematologic disorders. We emphasizenewer immune- and affinity-directed isolation/phenotyping technologies that are more selective and scalable thanconventional ultracentrifugation—e.g., microarray capture (EV Array), EV enrichment from cancer cells, magneticnanopore capture, and filtration/thermophoretic routes—along with single-EV readouts for increased informationalcontent. We also put EV diagnostics in a complementary role to established liquid biopsy modalities (e.g., ctDNA/cfDNA) to maximize detection and disease monitoring, rather than as alternatives. Finally, we outline theranosticapplications, including engineered EVs as delivery vehicles and stromal EVs as therapy resistance biomarkers andmediators, to bridge diagnostics with interventional strategies. This roadmap centers clinically meaningful use casesand methodological stringency to drive translation of EV assays into oncology practice

Zenodo (CERN European Organization for Nuclear Research)
Good health and well-being
Openalex Percentile: Top 43%
Extracellular vesicles in disease
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.