Abstract
Prostate cancer (PCa) is the most commonly diagnosed solid organ cancer in men worldwide. Current diagnosis of PCa includes use of initial prostate specific antigen assay which has a high false positive rate, low specificity, and low sensitivity. The side effects of unnecessary prostate biopsies that healthy men are subjected to, often result in unintended health complications. New PCa biomarkers are being discovered to address this unmet need. Here, we report on the creation of a composite score (Prostac) based on three recently discovered PCa biomarkers, Plasmacytoma Variant Translocation 1 (PVT1) exons 4A, 4B, and 9. Statistical analysis of copy numbers derived from a real-time quantitative polymerase chain (qPCR) reaction - based assay, showed these PCa biomarkers to be linearly separable and significantly over expressed in PCa epithelial cells. We train a supervised learning algorithm using support vector machines to generate a classification hyperplane from which a user-friendly composite score is developed. Cross validation of Prostac using data from prostate epithelial cells (RWPE1) and PCa cells (MDA PCa 2b) accurately classified 100% of PCa cells. Creation of the Prostac score lays the groundwork for clinical trial of its use in PCa diagnosis.