Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learning. Issue 3 (29th December 2022)

• Record Type: Journal Article
• Authors: Paproski, Robert J.; Pink, Desmond; Sosnowski, Deborah L.; Vasquez, Catalina; Lewis, John D.
• Is Part Of: Molecular oncology. Volume 17:Issue 3(2023)
• Page Start: 407
• Page End: 421
• Publication Date: 2022-12-29
• Subjects: cancer prediction -- diagnostic test -- extracellular vesicles -- machine learning -- microflow cytometry -- prostate cancer; Cancer -- Molecular aspects -- Periodicals; 616.994005
• DOI: 10.1002/1878-0261.13362
• Languages: English
• ISSNs: 1574-7891

Abstract

Abstract : Extracellular vesicles (EVs) are highly abundant in human biofluids, containing a repertoire of macromolecules and biomarkers representative of the tissue of origin. EVs released by tumours can communicate key signals both locally and to distant sites to promote growth and survival or impact invasive and metastatic progression. Microscale flow cytometry of circulating EVs is an emerging technology that is a promising alternative to biopsy for disease diagnosis. However, biofluid‐derived EVs are highly heterogeneous in size and composition, making their analysis complex. To address this, we developed a machine learning approach combined with EV microscale cytometry using tissue‐ and disease‐specific biomarkers to generate predictive models. We demonstrate the utility of this novel extracellular vesicle machine learning analysis platform (EVMAP) to predict disease from patient samples by developing a blood test to identify high‐grade prostate cancer and validate its performance in a prospective 215 patient cohort. Models generated using the EVMAP approach significantly improved the prediction of high‐risk prostate cancer, highlighting the clinical utility of this diagnostic platform for improved cancer prediction from a blood test. Abstract : We developed the extracellular vesicle machine learning analysis platform (EVMAP) to improve the prediction of diseases such as cancer. The platform combines extracellular vesicle analysis using microscale cytometry with a machine learning approach to generate predictive models. In this work, we utilized EVMAP to generate a blood test to predict high‐grade prostate cancer in men that was significantly more accurate than the prostate‐specific antigen test. This platform could be applied to many different diseases.