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

• Published in: Cancer medicine (Malden, MA) MA), 2023-08, Vol.12 (15), p.15797-15808
• Main Authors: Fairey, Adrian, Paproski, Robert J., Pink, Desmond, Sosnowski, Deborah L., Vasquez, Catalina, Donnelly, Bryan, Hyndman, Eric, Aprikian, Armen, Kinnaird, Adam, Beatty, Perrin H., Lewis, John D.
• Format: Article
• Language: English
• Subjects: Antigens; Biomarkers; Biopsy; Blood platelets; Blood tests; Clinical Cancer Research; Lasers; Light; Machine learning; Medical research; Medical screening; Metastasis; Monoclonal antibodies; Patients; Peptides; Plasma; Prostate Cancer; Regression analysis; Risk Model; Screening; Software
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.6216

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. We developed an accurate diagnostic blood test for grade group (GG) ≥3 prostate cancer that comprises the generation of a microflow cytometry dataset of three prostate cancer extracellular vesicle (EV) biomarkers, analysis with a novel machine learning algorithm to generate predictive EV models, and logistic regression analysis of these models with patient clinical data to calculate the risk score for the probability of GG ≥3 prostate cancer.