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EV-Finder: Direct Detection of Extracellular Vesicle-Associated Proteins by Proximity Extension Assay for
Yoshitaka Tamai1, Fumiko Chiwaki1, Yurika Shiotani1
1Medical Institute for Translational Health Science, ASFREYA Inc., 2-3-8 Shinkiba, Koto-ku, Tokyo 136-0082, Japan.
International Journal of Molecular Sciences
|June 12, 2026
Summary
A new method, EV-finder, directly detects cancer proteins from extracellular vesicles (EVs) in serum using proximity extension assay (PEA). This minimally invasive approach simplifies liquid biopsy for early multi-cancer screening.
Area of Science:
- Biomarker Discovery
- Cancer Diagnostics
- Molecular Biology
Background:
- Early cancer detection is challenging due to low biomarker levels in early stages.
- Extracellular vesicles (EVs) carrying tumor proteins are promising liquid biopsy targets.
- Current EV analysis requires complex isolation steps, hindering workflow efficiency.
Purpose of the Study:
- To develop a direct detection method for EV-associated proteins in serum.
- To establish a simplified workflow for liquid biopsy using proximity extension assay (PEA) technology.
- To evaluate the performance of this method for multi-cancer screening and tissue-of-origin prediction.
Main Methods:
- Developed EV-finder, a framework for direct EV-associated protein detection in serum using PEA.
- Utilized serum samples from 193 cancer patients (five types) and 138 healthy controls.
- Implemented a two-step supervised machine learning model for cancer detection and origin prediction.
Main Results:
- The screening model achieved high performance: AUC 0.985, sensitivity 0.929, specificity 0.957.
- No false positives in a Japanese control cohort; low false positives in a Korean cohort.
- Identified both pan-cancer and cancer-type-specific EV-associated protein signatures.
Conclusions:
- Direct detection of EV-associated proteins from serum using PEA is feasible.
- EV-finder offers a simplified, scalable, and minimally invasive strategy for multi-cancer screening.
- This approach holds potential for early cancer detection and personalized diagnostics.
