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

• Published in: Communications medicine Vol. 2; no. 1; pp. 29 - 9
• Main Authors: Hinestrosa, Juan Pablo, Kurzrock, Razelle, Lewis, Jean M., Schork, Nicholas J., Schroeder, Gregor, Kamat, Ashish M., Lowy, Andrew M., Eskander, Ramez N., Perrera, Orlando, Searson, David, Rastegar, Kiarash, Hughes, Jake R., Ortiz, Victor, Clark, Iryna, Balcer, Heath I., Arakelyan, Larry, Turner, Robert, Billings, Paul R., Adler, Mark J., Lippman, Scott M., Krishnan, Rajaram
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.03.2022; Nature Portfolio
• Subjects: 631/67/1504/1713; 631/67/1517/1709; 631/67/589/1336; 692/4028/67/2322; 82; 82/29; 82/80; Medicine; Medicine & Public Health; Bladder cancer; Pancreatic cancer; Cancer screening; Ovarian cancer
• ISSN: 2730-664X; 2730-664X
• DOI: 10.1038/s43856-022-00088-6

Background Detecting cancer at early stages significantly increases patient survival rates. Because lethal solid tumors often produce few symptoms before progressing to advanced, metastatic disease, diagnosis frequently occurs when surgical resection is no longer curative. One promising approach to detect early-stage, curable cancers uses biomarkers present in circulating extracellular vesicles (EVs). To explore the feasibility of this approach, we developed an EV-based blood biomarker classifier from EV protein profiles to detect stages I and II pancreatic, ovarian, and bladder cancer. Methods Utilizing an alternating current electrokinetics (ACE) platform to purify EVs from plasma, we use multi-marker EV-protein measurements to develop a machine learning algorithm that can discriminate cancer cases from controls. The ACE isolation method requires small sample volumes, and the streamlined process permits integration into high-throughput workflows. Results In this case-control pilot study, comparison of 139 pathologically confirmed stage I and II cancer cases representing pancreatic, ovarian, or bladder patients against 184 control subjects yields an area under the curve (AUC) of 0.95 (95% CI: 0.92 to 0.97), with sensitivity of 71.2% (95% CI: 63.2 to 78.1) at 99.5% (97.0 to 99.9) specificity. Sensitivity is similar at both early stages [stage I: 70.5% (60.2 to 79.0) and stage II: 72.5% (59.1 to 82.9)]. Detection of stage I cancer reaches 95.5% in pancreatic, 74.4% in ovarian (73.1% in Stage IA) and 43.8% in bladder cancer. Conclusions This work demonstrates that an EV-based, multi-cancer test has potential clinical value for early cancer detection and warrants future expanded studies involving prospective cohorts with multi-year follow-up. Plain Language Summary Finding cancer early can make treatment easier and improve odds of survival. However, many tumors go unnoticed until they have grown large enough to cause symptoms. While scans can detect tumors earlier, routine full-body imaging is impractical for population screening. New cancer detection methods being explored are based on observations that tumors release tiny particles called extracellular vesicles (EVs) into the bloodstream, containing proteins from the tumor. Here, we used a method to purify EVs from patients’ blood followed by a method to detect tumor proteins in the EVs. Our method quickly and accurately detected early-stage pancreatic, ovarian, or bladder cancer. With further testing, this method may provide a useful screening tool for clinicians to detect cancers at an earlier stage. Hinestrosa et al. describe the early-stage detection of cancer using biomarkers present in circulating extracellular vesicles purified via an alternating current electrokinetics platform. They show, in a case-control study, that 95.7% of pancreatic, 75.0% of ovarian and 43.8% of bladder stage I and II cancers can be detected.