Using a combination of urine and plasma biomarkers for the development of a scoring sytem that can diagnose and predict prognosis of prostate cancer.

Abstract

163 Background: Relying solely on serum prostate-specific antigen (sPSA) to screen for prostate cancer (PCa) can lead to unnecessary biopsies. Biomarkers from urine and plasma were isolated to develop a detection scoring system for the presence of prostate cancer as well as to better predict aggressiveness. Methods: Urine and plasma specimens were analyzed from 141 patients (61 newly diagnosed PCa patients, 60 benign prostate hyperplasia (BPH) patients, and 20 post-prostatectomy patients) using polymerase chain reaction (PCR) for the levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, and B2M genes. Patient age, sPSA level, and PCR data were entered through multiple algorithms to determine models most useful for detection of cancer and predicting aggressiveness. Results: We developed an algorithm for distinguishing PCa from BPH (area under the receiver operating characteristic curve [AUROC] of 0.78). Another algorithm distinguishes patients with Gleason Score (GS) ≥ 7 from GS < 7 cancer or BPH (AUROC of 0.88). By incorporating the two algorithms into a scoring system, 75% of the analyzed samples showed concordance between the two models (99% specificity and 68% sensitivity for predicting GS ≥ 7), and 25% showed discordance. Conclusions: A scoring system incorporating two algorithms using urine and plasma biomarkers highly predicts the presence of GS ≥ 7 PCa in 75% of patients. In 25% of patients, the system can be used only to distinguish between the presence of cancer and benign pathology. Our algorithms may assist with both biopsy indication as well as patient prognosis. [Table: see text]