A Model to Detect Significant Prostate Cancer Integrating Urinary Peptide and Extracellular Vesicle RNA Data

Simple Summary Prostate cancer is one of the leading causes of cancer-related death in men in the world, but a large proportion of men that are diagnosed with prostate cancer do not have a form of the disease that will cause them long term harm. Therefore, there is a need to accurately predict the aggressiveness of the disease without taking an invasive biopsy. In this study, we develop a test that can predict whether a patient has prostate cancer and how aggressive that cancer is. This test combines clinical measurements, levels of four genes collected from a fraction of the urine, and levels of six peptides found in urine. We found that this test, deemed 'ExoSpec', has the potential to improve the pathway for men with a clinical suspicion of prostate cancer and could reduce the requirement for biopsies by 30%. There is a clinical need to improve assessment of biopsy-naive patients for the presence of clinically significant prostate cancer (PCa). In this study, we investigated whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples collected within the Movember GAP1 Urine Biomarker study (n = 192) were analysed by both mass spectrometry-based urine-proteomics and NanoString gene-expression analysis (167 gene-probes). Cross-validated LASSO penalised regression and Random Forests identified a combination of clinical and urinary biomarkers for predictive modelling of significant disease (Gleason Score (Gs) >= 3 + 4). Four predictive models were developed: 'MassSpec' (CE-MS proteomics), 'EV-RNA', and 'SoC' (standard of care) clinical data models, alongside a fully integrated omics-model, deemed 'ExoSpec'. ExoSpec (incorporating four gene transcripts, six peptides, and two clinical variables) is the best model for predicting Gs >= 3 + 4 at initial biopsy (AUC = 0.83, 95% CI: 0.77-0.88) and is superior to a standard of care (SoC) model utilising clinical data alone (AUC = 0.71, p < 0.001, 1000 resamples). As the ExoSpec Risk Score increases, the likelihood of higher-grade PCa on biopsy is significantly greater (OR = 2.8, 95% CI: 2.1-3.7). The decision curve analyses reveals that ExoSpec provides a net benefit over SoC and could reduce unnecessary biopsies by 30%.