Tag Archive for: aotw 09-10-19


Article of the week: A four‐group urine risk classifier for predicting outcomes in patients with prostate cancer

Every week, the Editor-in-Chief selects an Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an editorial written by a prominent member of the urological community. These are intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation. 

If you only have time to read one article this week, it should be this one.

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

Shea P. Connell*, Marcelino Yazbek-Hanna*, Frank McCarthy, Rachel Hurst*, MartynWebb*, Helen Curley*, Helen Walker, Rob Mills, Richard Y. Ball, Martin G. Sanda§, Kathryn L. Pellegrini§, Dattatraya Patil§, Antoinette S. Perry, Jack Schalken**, Hardev Pandha††, Hayley Whitaker‡‡, Nening Dennis, Christine Stuttle, Ian G. Mills§§¶¶***, Ingrid Guldvik¶¶, Movember GAP1 Urine Biomarker Consortium1, Chris Parker†††, Daniel S. Brewer*‡‡‡, Colin S. Cooper* and Jeremy Clark*

*Norwich Medical School, University of East Anglia, Norwich, Institute of Cancer Research, Sutton, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK, §Department of Urology, Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA, USA , School of Biology and Environmental Science, Science West, University College Dublin, Dublin 4, Ireland, **Nijmegen Medical Centre, Radboud University Medical Centre, Nijmegen, The Netherlands, ††Faculty of Health and Medical Sciences, The University of Surrey, Guildford, ‡‡Molecular Diagnostics and Therapeutics Group, University College London, London, §§School of Medicine, Dentistry and Biomedical Sciences, Institute for Health Sciences, Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, UK, ¶¶Centre for Molecular Medicine, University of Oslo, Oslo, Norway, ***Nuffield Department of Surgical Sciences, University of Oxford, Oxford, †††Royal Marsden Hospital, Sutton and ‡‡‡Earlham Institute, Norwich, UK



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. This model was applied to a test cohort (n = 177) for diagnostic evaluation, and to an AS sub‐cohort (n = 87) for prognostic evaluation.

Table 2. NanoString gene probes incorporated by LASSO regularization in the final optimal model used to produce the prostate urine risk signatures


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 (area under the curve = 0.77, 95% confidence interval [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 (interquartile range hazard ratio [HR] 2.86, 95% CI 1.83–4.47; P < 0.001). PUR‐4, when used continuously, dichotomized patient groups with differential progression rates of 10% and 60% 5 years after urine collection (HR 8.23, 95% CI 3.26–20.81; P < 0.001).


Urine‐derived EV‐RNA can provide diagnostic information on aggressive prostate cancer prior to biopsy, and prognostic information for men on AS. PUR represents a new and versatile biomarker that could result in substantial alterations to current treatment of patients with prostate cancer.


Editorial: Do you need further assistance in diagnosing and risk stratifying prostate cancer?

I would hope the answer to the question posed in the title is a universal ‘yes’; at least that is my experience with this complex and common disease. The concept that in 2019, we have unmet needs in prostate cancer diagnostics is somewhat remarkable, given that we have access to: (i) one of the most widely used biomarkers in oncology (PSA), (ii) a readily accessible organ to examine (DRE), (iii) state of the art imaging (MRI, positron emission tomography), (iv) specialty biopsy systems (fusion/transperineal template), (v) enhanced risk stratification systems (National Comprehensive Cancer Network [NCCN], Cancer of the Prostate Risk Assessment [CAPRA], etc.), (vi) numerous nomograms, (vii) secondary urine/serum biomarkers (Prostate Health Index [PHI], prostate cancer antigen 3 [PCA3], SelectMDx, ExoDx, four‐kallikrein panel [4K]), and (viii) commercially available genomic platforms (Prolaris, OncotypeDx, Decipher).

The paper by Connell et al. [1] in this issue of BJUI asks you to consider adding another diagnostic test to your list. You might correctly assume from the title that the test is in discovery/validation stages, and lacks a fancy commercialised name. Many steps await any promising biomarker to make it to your clinic. So why pay attention to this one? Let me reiterate a few points made by the authors and suggest where new paradigms might emerge if the test delivers on its promises.

First, the test crosses over the current barriers between screening patients and active surveillance (AS). In both populations we care about Gleason Grade Group ≥2. Yet a SelectMDx or similar tests are validated for diagnosis but not for monitoring Grade Group 1 on AS. Genomic profiling tests have strong validation and prognostic value for AS, but require tissue and external laboratory work flows. This marker is being tested for both settings, with potentially meaningful distinctions for both patient groups.

Second, this test is in the urine and does not need imaging or needles to obtain samples. It may have serial use (if cost‐effective) for monitoring AS.

Third, for AS cohorts, the test seems to be able to identify progression well in advance. This would potentially allow for early intervention in the correct patients, and less intense monitoring in the remaining.

Fourth, the test metrics looked favourable in PSA screened and unscreened populations; will we ever see a novel biomarker bold enough to move to primary/independent screening status?

Fifth, some of the secondary biomarkers you may be using now are included in this model: PCA3, transmembrane protease serine 2:v‑ets erythroblastosis virus E26 oncogene homolog (TMPRSS2‐ERG), Homeobox C6 (HOXC6).

To be critical, this biomarker will need significant validation in other cohorts, and we can always hope for head‐to‐head data with existing strategies. I will remain optimistic these authors can move this biomarker strategy along and help bridge some of the gaps that remain in disease detection and risk stratification. I may even attempt to insert some of those lovely new equations in the methods section into future lectures.


  1. Connell SPYazbek‐Hanna MMcCarthy F et al. A four‐group urine risk classifier for predicting outcomes in patients with prostate cancer. BJU Int 2019124609– 20
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