Development of a blood-based extracellular vesicle classifier for detection of early-stage pancreatic ductal adenocarcinoma

Juan Pablo Hinestrosa; Rosalie C Sears; Harmeet Dhani; Jean M Lewis; Gregor Schroeder; Heath I Balcer; Dove Keith; Brett C Sheppard; Razelle Kurzrock; Paul R Billings

doi:10.1038/s43856-023-00351-4

Development of a blood-based extracellular vesicle classifier for detection of early-stage pancreatic ductal adenocarcinoma

Commun Med (Lond). 2023 Oct 19;3(1):146. doi: 10.1038/s43856-023-00351-4.

Authors

Affiliations

¹ Biological Dynamics Inc, San Diego, CA, USA. jp@biologicaldynamics.com.
² Department of Molecular and Medical Genetics, Brenden-Colson Center for Pancreatic Cancer, Knight Cancer Institute, Oregon Health and Sciences University, Portland, OR, USA.
³ Biological Dynamics Inc, San Diego, CA, USA.
⁴ Brenden-Colson Center for Pancreatic Cancer, Knight Cancer Institute, Oregon Health and Sciences University, Portland, OR, USA.
⁵ Medical College of Wisconsin, Milwaukee, WI, USA.
⁶ Worldwide Innovative Network for Personalized Cancer Medicine, Chevilly-Larue, France.
⁷ Biological Dynamics Inc, San Diego, CA, USA. pbillings@biologicaldynamics.com.

Abstract

Background: Pancreatic ductal adenocarcinoma (PDAC) has an overall 5-year survival rate of just 12.5% and thus is among the leading causes of cancer deaths. When detected at early stages, PDAC survival rates improve substantially. Testing high-risk patients can increase early-stage cancer detection; however, currently available liquid biopsy approaches lack high sensitivity and may not be easily accessible.

Methods: Extracellular vesicles (EVs) were isolated from blood plasma that was collected from a training set of 650 patients (105 PDAC stages I and II, 545 controls). EV proteins were analyzed using a machine learning approach to determine which were the most informative to develop a classifier for early-stage PDAC. The classifier was tested on a validation cohort of 113 patients (30 PDAC stages I and II, 83 controls).

Results: The training set demonstrates an AUC of 0.971 (95% CI = 0.953-0.986) with 93.3% sensitivity (95% CI: 86.9-96.7) at 91.0% specificity (95% CI: 88.3-93.1). The trained classifier is validated using an independent cohort (30 stage I and II cases, 83 controls) and achieves a sensitivity of 90.0% and a specificity of 92.8%.

Conclusions: Liquid biopsy using EVs may provide unique or complementary information that improves early PDAC and other cancer detection. EV protein determinations herein demonstrate that the AC Electrokinetics (ACE) method of EV enrichment provides early-stage detection of cancer distinct from normal or pancreatitis controls.

Plain language summary

Pancreatic cancer is one of the deadliest cancers and it is often detected when it is too late, limiting treatment options and reducing survival rates. Identifying blood-based markers of pancreatic cancer may help us to diagnose it earlier, when it is more treatable. Tiny particles circulating in the blood stream, called extracellular vesicles (EVs), contain useful information about tumors, which can be collected with our innovative technology. In this study, we analyzed markers present within EVs and developed a computational tool using this information to identify people with early-stage pancreatic cancer. With further testing in real-world settings, this approach may prove useful for surveillance and early detection of this deadly disease.