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

There is a clinical need to improve assessment of biopsy-naïve 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).

Microbiomes of Urine and the Prostate Are Linked to Human Prostate Cancer Risk Groups.

Background: Bacteria play a suspected role in the development of several cancer types, and associations between the presence of particular bacteria and prostate cancer have been reported.

Objective: To provide improved characterisation of the prostate and urine microbiome and to investigate the prognostic potential of the bacteria present.

Design, Setting, And Participants: Microbiome profiles were interrogated in sample collections of patient urine (sediment microscopy: n = 318, 16S ribosomal amplicon sequencing: n = 46; and extracellular vesicle RNA-seq: n = 40) and cancer tissue (n = 204).

Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer.

Background: Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine-derived cell-free messenger RNA (cf-RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients.

Methods: Post-digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf-RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207).

Methodology for the at-home collection of urine samples for prostate cancer detection.

Urine from patients with prostate cancer (PCa) contains gene transcripts that have been used for PCa diagnosis and prognosis. Historically, patient urine samples have been collected after a digital rectal examination of the prostate, which was thought necessary to boost the levels of prostatic secretions in the urine. We herein describe methodology that allows urine to be collected by patients at home and then posted to a laboratory for analysis.

A four-group urine risk classifier for predicting outcomes in patients with prostate cancer.

Objectives: To develop a risk classifier using urine-derived extracellular vesicle (EV)-RNA capable of providing diagnostic information on disease status prior to biopsy, and prognostic information for men on active surveillance (AS).

Patients And Methods: Post-digital rectal examination urine-derived EV-RNA expression profiles (n = 535, multiple centres) were interrogated with a curated NanoString panel. A LASSO-based continuation ratio model was built to generate four prostate urine risk (PUR) signatures for predicting the probability of normal tissue (PUR-1), D'Amico low-risk (PUR-2), intermediate-risk (PUR-3), and high-risk (PUR-4) prostate cancer.