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Early-stage multi-cancer detection using an extracellular vesicle protein-based blood test.

Juan Pablo Hinestrosa1, Razelle Kurzrock2,3,4, Jean M Lewis1

  • 1Biological Dynamics, Inc, San Diego, CA USA.

Communications Medicine
|May 23, 2022
PubMed
Summary

Early cancer detection using extracellular vesicle (EV) protein biomarkers shows promise. A blood test classifier achieved 95% AUC for detecting early-stage pancreatic, ovarian, and bladder cancers, aiding survival.

Keywords:
Bladder cancerCancer screeningOvarian cancerPancreatic cancer

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Area of Science:

  • Oncology
  • Biomarker Discovery
  • Molecular Diagnostics

Background:

  • Early cancer detection significantly improves patient survival rates.
  • Late diagnosis of solid tumors often prevents curative surgical resection.
  • Extracellular vesicles (EVs) in circulation offer a promising source for early cancer biomarkers.

Purpose of the Study:

  • To develop and evaluate an EV-based blood biomarker classifier for early-stage (I and II) pancreatic, ovarian, and bladder cancers.
  • To assess the feasibility of using EV protein profiles for cancer detection.
  • To determine the diagnostic performance of the classifier in a case-control study.

Main Methods:

  • Purification of EVs from plasma using an alternating current electrokinetics (ACE) platform.
  • Multi-marker EV-protein profiling.
  • Development of a machine learning algorithm for cancer case discrimination.

Main Results:

  • The EV-based classifier achieved an Area Under the Curve (AUC) of 0.95 (95% CI: 0.92–0.97).
  • The classifier demonstrated 71.2% sensitivity and 99.5% specificity for detecting early-stage cancers.
  • High sensitivity was observed for stage I pancreatic cancer (95.5%), ovarian cancer (74.4%), and bladder cancer (43.8%).

Conclusions:

  • An EV-based, multi-cancer blood test shows potential clinical utility for early cancer detection.
  • The findings support further investigation with larger, prospective studies.
  • The ACE platform allows for small sample volumes and high-throughput analysis.