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%.

Automated summary

This study developed a urine test to predict the aggressiveness of prostate cancer and potentially reduce the need for biopsies. By combining clinical measurements, gene levels, and peptides in urine, this test improves the assessment accuracy and reliability for men with suspected prostate cancer.