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

Towards the Clinical Implementation of Extracellular Vesicle-Based Biomarker Assays for Cancer

BACKGROUND

Substantial research has been devoted to elucidating the role of extracellular vesicles (EVs) in the different hallmarks of cancer. Consequently, EVs are increasingly explored as a source of cancer biomarkers in body fluids. However, the heterogeneity in EVs, the complexity of body fluids, and the diversity in methods available for EV analysis, challenge the development and translation of EV-based biomarker assays.

CONTENT

Essential steps in EV-associated biomarker development are emphasized covering biobanking, biomarker discovery, verification and validation, and clinical implementation. A meticulous study design is essential and ideally results from close interactions between clinicians and EV researchers. A plethora of different EV preparation protocols exists which warrants quality control and transparency to ensure reproducibility and thus enable verification of EV-associated biomarker candidates identified in the discovery phase in subsequent independent cohorts. The development of an EV-associated biomarker assay requires thorough analytical and clinical validation. Finally, regulatory affairs must be considered for clinical implementation of EV-based biomarker assays.

SUMMARY

In this review, the current challenges that prevent us from exploiting the full potential of EV-based biomarker assays are identified. Guidelines and tools to overcome these hurdles are highlighted and are crucial to advance EV-based biomarker assays into clinical use.

Clinical applications and challenges in the field of extracellular vesicles

Body fluids contain cell-derived particles called extracellular vesicles (EVs). EVs are released by cells and are present in all body fluids (i. e. liquid biopsies). EVs contribute to physiology and pathology and offer a plethora of potential clinical applications, ranging from biomarkers to therapeutic applications. In this manuscript we provide an overview of this new and rapidly growing research field, along with its challenges and opportunities.

Clinical analysis of EV-Fingerprint to predict grade group 3 and above prostate cancer and avoid prostate biopsy

BACKGROUND

There is an unmet clinical need for minimally invasive diagnostic tests to improve the detection of grade group (GG) ≥3 prostate cancer relative to prostate antigen-specific risk calculators. We determined the accuracy of the blood-based extracellular vesicle (EV) biomarker assay (EV Fingerprint test) at the point of a prostate biopsy decision to predict GG ≥3 from GG ≤2 and avoid unnecessary biopsies.

METHODS

This study analyzed 415 men referred to urology clinics and scheduled for a prostate biopsy, were recruited to the APCaRI 01 prospective cohort study. The EV machine learning analysis platform was used to generate predictive EV models from microflow data. Logistic regression was then used to analyze the combined EV models and patient clinical data and generate the patients' risk score for GG ≥3 prostate cancer.

RESULTS

The EV-Fingerprint test was evaluated using the area under the curve (AUC) in discrimination of GG ≥3 from GG ≤2 and benign disease on initial biopsy. EV-Fingerprint identified GG ≥3 cancer patients with high accuracy (0.81 AUC) at 95% sensitivity and 97% negative predictive value. Using a 7.85% probability cutoff, 95% of men with GG ≥3 would have been recommended a biopsy while avoiding 144 unnecessary biopsies (35%) and missing four GG ≥3 cancers (5%). Conversely, a 5% cutoff would have avoided 31 unnecessary biopsies (7%), missing no GG ≥3 cancers (0%).

CONCLUSIONS

EV-Fingerprint accurately predicted GG ≥3 prostate cancer and would have significantly reduced unnecessary prostate biopsies.