Exosomal RNA as a source of urine biomarkers for prostate cancer

Introduction In this study we exploited the recent development of methods that have enabled the analysis of RNA present in urine exosomes of prostate cancer patients. We report RNA expression patterns that contain diagnostic and prognostic information for prostate cancer, and association with response to hormone treatment. Methods First catch urine following digital rectal examination were collected from 662 men. 3 groups of patients were used: Low, Intermediate, and High-risk according to NICE stratification criteria, and two control groups: benign and advanced disease. 50-gene transcript expression analysis using NanoString technology was performed on 192 samples. Exosomal RNA Next-Generation Sequencing was performed on 18 samples for novel biomarker discovery. Results Expression analysis showed that PCa-specific transcripts such as TMPRSS2/ERG fusion transcripts were identifiable in exosomes from PCa urine samples. LPD analysis highlighted expression levels of 15 transcripts with diagnostic potential (significantly up-regulated in cancer samples in comparison to benign control) and 17 transcripts with prognostic potential (differentialy expressed in high risk and advanced disease in comparison to lower grade disease). I also report two gene transcripts (SERPINB5/Maspin, HPRT) that were significantly differentially expressed in patients who failed to respond to hormone deprivation therapy for high risk/metastatic disease. Three genes (STEAP4, ARexons4_8 and NAALADL2) were significantly differentially expressed in patients who relapsed within 12 months of hormone treatment initiation. Next-Generation Sequencing of twenty samples identified 45 genes to be significantly differentially expressed between non-cancer and cancer samples (28 were up regulated and 17 down regulated). 33 out of the 45 genes showed a significant linear trend in association with cancer risk. Conclusions Urine Exosomal RNA contains PCa specific transcripts. Gene expression analysis and Next Generation Sequencing identified genes that are significantly differentially expressed between cancer and non-cancer cases as well as prognostic genes and genes that can predict response to hormone treatmen

Gene-transcript expression in urine supernatant and urine cell-sediment are different but equally useful for detecting prostate cancer

There is considerable interest in urine as a non-invasive liquid biopsy to detect prostate cancer (PCa). PCa-specific transcripts such as the TMPRSS2:ERG fusion gene can be found in both urine extracellular vesicles (EVs) and urine cell-sediment (Cell) but the relative usefulness of these and other genes in each fraction in PCa detection has not been fully elucidated. Urine samples from 76 men (PCa n = 40, non-cancer n = 36) were analysed by NanoString for 154 PCa-associated genes-probes, 11 tissue-specific, and six housekeeping. Comparison to qRT-PCR data for four genes (PCA3, OR51E2, FOLH1, and RPLP2) was strong (r = 0.51–0.95, Spearman p < 0.00001). Comparing EV to Cells, differential gene expression analysis found 57 gene-probes significantly more highly expressed in 100 ng of amplified cDNA products from the EV fraction, and 26 in Cells (p < 0.05; edgeR). Expression levels of prostate-specific genes (KLK2, KLK3) measured were ~20x higher in EVs, while PTPRC (white-blood Cells) was ~1000× higher in Cells. Boruta analysis identified 11 gene-probes as useful in detecting PCa: two were useful in both fractions (PCA3, HOXC6), five in EVs alone (GJB1, RPS10, TMPRSS2:ERG, ERG_Exons_4-5, HPN) and four from Cell (ERG_Exons_6-7, OR51E2, SPINK1, IMPDH2), suggesting that it is beneficial to fractionate whole urine prior to analysis. The five housekeeping genes were not significantly differentially expressed between PCa and non-cancer samples. Expression signatures from Cell, EV and combined data did not show evidence for one fraction providing superior information over the other

The urine biomarker PUR-4 is positively associated with the amount of Gleason 4 in human prostate cancers

The Prostate Urine Risk (PUR) biomarker is a four-group classifier for predicting outcome in patients prior to biopsy and for men on active surveillance. The four categories correspond to the probabilities of the presence of normal tissue (PUR-1), D’Amico low-risk (PUR-2), intermediate-risk (PUR-3), and high-risk (PUR-4) prostate cancer. In the current study we investigate how the PUR-4 status is linked to Gleason grade, prostate volume, and tumor volume as assessed from biopsy (n = 215) and prostatectomy (n = 9) samples. For biopsy data PUR-4 status alone was linked to Gleason Grade group (GG) (Spearman’s, ρ = 0.58, p < 0.001 trend). To assess the impact of tumor volume each GG was dichotomized into Small and Large volume cancers relative to median volume. For GG1 (Gleason Pattern 3 + 3) cancers volume had no impact on PUR-4 status. In contrast for GG2 (3 + 4) and GG3 (4 + 3) cancers PUR-4 levels increased in large volume cancers with statistical significance observed for GG2 (p = 0.005; Games-Howell). These data indicated that PUR-4 status is linked to the presence of Gleason Pattern 4. To test this observation tumor burden and Gleason Pattern were assessed in nine surgically removed and sectioned prostates allowing reconstruction of 3D maps. PUR-4 was not correlated with Gleason Pattern 3 amount, total tumor volume or prostate size. A strong correlation was observed between amount of Gleason Pattern 4 tumor and PUR-4 signature (r = 0.71, p = 0.034, Pearson’s). These observations shed light on the biological significance of the PUR biomarker and support its use as a non-invasive means of assessing the presence of clinically significant prostate cancer

Mutation detection in formalin-fixed prostate cancer biopsies taken at the time of diagnosis using next generation DNA sequencing

Aims: Assessing whether Next Generation DNA Sequencing (NGS) can be used to screen prostate cancer for multiple gene alterations in men routinely diagnosed with this disease and/or who are entered into clinical trials. Previous studies are limited and have reported only low success rates. Methods: We marked areas of cancer on H&E stained sections from formalin fixed-needle biopsys, and used these as templates to dissect cancer rich tissue from adjacent unstained sections. DNA was prepared using a Qiagen protocol modified to maximise DNA yield. The DNA was screened simultaneously for mutations in 365 cancer-related genes using an Illumina HiSeq 2000 NGS platform. Results: From 63 prostate cancers examined (59/63, 94%) of the samples yielded at least 30ng of DNA, the minimum amount of DNA considered suitable for NGS analysis. Patients in the D’Amico high-risk group yielded an average of 1033ng; intermediate-risk patients 401ng; and low risk patients 97ng. NGS of 8 samples selected from high and intermediate risk groups gave a median exon read depth of 962 and detected TMPRRS2-ERG fusions, as well as a variety of mutations including those in the SPOP, TP53, ATM, MEN1, NBPF10, NCOR2, PIK3CB, and MAP2K5 (MEK5) genes. Conclusions: Using the methods presented here NGS technologies can be used to screen a high proportion of prostate cancer patients for mutations in cancer-related genes in tissue samples opening up its general use in the context of clinical trials or routine diagnosis

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). Outcome measurements and statistical analysis: Microbiomes were assessed using anaerobic culture, population-level 16S analysis, RNA-seq, and whole genome DNA sequencing. Results and limitations: We demonstrate an association between the presence of bacteria in urine sediments and higher D’Amico risk prostate cancer (discovery, n = 215 patients, p < 0.001; validation, n = 103, p < 0.001, χ2 test for trend). Characterisation of the bacterial community led to the (1) identification of four novel bacteria (Porphyromonas sp. nov., Varibaculum sp. nov., Peptoniphilus sp. nov., and Fenollaria sp. nov.) that were frequently found in patient urine, and (2) definition of a patient subgroup associated with metastasis development (p = 0.015, log-rank test). The presence of five specific anaerobic genera, which includes three of the novel isolates, was associated with cancer risk group, in urine sediment (p = 0.045, log-rank test), urine extracellular vesicles (p = 0.039), and cancer tissue (p = 0.035), with a meta-analysis hazard ratio for disease progression of 2.60 (95% confidence interval: 1.39–4.85; p = 0.003; Cox regression). A limitation is that functional links to cancer development are not yet established. Conclusions: This study characterises prostate and urine microbiomes, and indicates that specific anaerobic bacteria genera have prognostic potential

A four-group urine risk classifier for predicting outcome in prostate cancer patients

Objectives: to develop a risk classifier using urine-derived extracellular vesicle RNA (UEV-RNA) capable of providing diagnostic information of disease status prior to biopsy, and prognostic information for men on active surveillance (AS). Patients and methods: post-digital rectal examination UEV-RNA expression profiles from urine (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) PCa. This model was applied to a test cohort (n = 177) for diagnostic evaluation, and to an AS sub-cohort (n = 87) for prognostic evaluation. Results: each PUR signature was significantly associated with its corresponding clinical category (p<0.001). PUR-4 status predicted the presence of clinically significant Intermediate or High-risk disease, AUC = 0.77 (95% CI: 0.70-0.84). Application of PUR provided a net benefit over current clinical practice. In an AS sub-cohort (n=87), groups defined by PUR status and proportion of PUR-4 had a significant association with time to progression (p<0.001; IQR HR = 2.86, 95% CI:1.83-4.47). PUR-4, when utilised continuously, dichotomised patient groups with differential progression rates of 10% and 60% five years post-urine collection (p<0.001, HR = 8.23, 95% CI:3.26-20.81). Conclusion: UEV-RNA can provide diagnostic information of aggressive PCa prior to biopsy, and prognostic information for men on AS. PUR represents a new & versatile biomarker that could result in substantial alterations to current treatment of PCa patients. This article is protected by copyright. All rights reserved