Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learning

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.

Automated summary

Extracellular vesicles (EVs) in human biofluids contain biomarkers representative of tissue of origin and can communicate signals to promote tumour growth and progression. Microscale flow cytometry of circulating EVs is a promising alternative to biopsy for disease diagnosis. We developed a machine learning approach combined with EV microscale cytometry to generate predictive models and demonstrated the utility of this approach in predicting high-grade prostate cancer from patient samples.