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

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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

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Abstract

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. 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

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 (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).

Conclusion

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.

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

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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.

Reference

  1. Connell SP, Yazbek‐Hanna M, McCarthy F et al. A four‐group urine risk classifier for predicting outcomes in patients with prostate cancer. BJU Int 2019; 124: 609– 20

Article of the week: ‘Dr Google’: trends in online interest in prostate cancer screening, diagnosis and treatment

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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 and a visual abstract written by prominent members 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 month, it should be this one.

‘Dr Google’: trends in online interest in prostate cancer screening, diagnosis and treatment

Michael E. Rezaee*, Briana Goddard†, Einar F. Sverrisson*, John D. Seigne* and Lawrence M. Dagrosa*

*Section of Urology, Department of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, and †Geisel School of Medicine, Hanover

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Abstract

Objectives

To examine trends in online search behaviours related to prostate cancer on a national and regional scale using a dominant major search engine.

Materials and Methods

Google Trends was queried using the terms ‘prostate cancer’, ‘prostate‐specific antigen’ (PSA), and ‘prostate biopsy’ between January 2004 and January 2019. Search volume index (SVI), a measure of relative search volume on Google, was obtained for all terms and examined by region and time period: pre‐US Preventive Services Task Force (USPSTF) Grade D draft recommendation on PSA screening; during the active Grade D recommendation; and after publication of the recent Grade C draft recommendation.

Results

Online interest in PSA screening differed by time period (P < 0.01). The SVI for PSA screening was greater pre‐Grade D draft recommendation (82.7) compared to during the recommendation (74.5), while the SVI for PSA screening was higher post‐Grade C draft recommendation (90.4) compared to both prior time periods. Similar results were observed for prostate biopsy and prostate cancer searches. At the US state level, online interest in prostate cancer was highest in South Carolina (SVI 100) and lowest in Hawaii (SVI 64). For prostate cancer treatment options, online interest in cryotherapy, prostatectomy and prostate cancer surgery overall increased, while searches for active surveillance, external beam radiation, brachytherapy and high‐intensity focused ultrasonography remained stable.

Conclusion

Online interest in prostate cancer has changed over time, particularly in accordance with USPSTF screening guidelines. Google Trends may be a useful tool in tracking public interest in prostate cancer screening, diagnosis and treatment, especially as it relates to major shifts in practice guidelines.

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Editorial: Does Dr Google give good advice about prostate cancer?

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In this issue of BJUI, Rezaee et al. [1] report on Google trends as a barometer of public interest in PSA screening and different types of prostate cancer treatment in the USA. Not surprisingly, they found a decrease in Google searches about PSA screening after the US Preventive Services Task Force (USPSTF) issued a Grade D recommendation against screening. This corresponds with observed trends of decreased PSA screening in the population [2]. Notably, the volume of Google searches about PSA screening rebounded after the USPSTF changed to a Grade C recommendation for shared decision-making about screening. It is unknown whether this actually reflects a greater number of men discussing PSA screening with their doctors, or whether online information had an impact on their decisions.

Meanwhile, the quantity of Google search activity varied between different types of prostate cancer treatment. In the USA, search volume was higher for surgery than for active surveillance, and there was a greater search volume for high intensity focused ultrasonography (HIFU) than for external beam radiation therapy or brachytherapy. Notably, another recent study examined global Google trends in searches on prostate cancer treatment, showing increasing annual relative search volume for focal therapy and active surveillance over time [3]. The underlying reasons for these temporal and geographic differences in ‘public interest’ may be multifactorial, including recommendations from physicians and professional societies, support from policy-makers, public awareness campaigns from healthcare-related organizations and marketing from commercial companies. Whether the change in ‘public interest’ had any impact on treatment selection remains unknown.

As an increasing number of people are going online for health information, digital platforms provide useful barometers for public interest in different topics. For example, another recent study reported that prostate cancer was a topic with high public interest based on the number of video views on YouTube compared to other urological conditions [4]. While interesting, the number of Google searches or views on YouTube do not provide any insights into who is searching for the information, their motivation, and the quality of information that they received.

Concerningly, several recent studies have called into question the accuracy of information about prostate cancer across multiple online platforms. Asafu-Adjei et al. [5] reported that websites on HIFU and cryotherapy had a substantial amount of incomplete or inaccurate information. Alsyouf et al. [6] reported that seven of the 10 most commonly shared articles about prostate cancer on social media were inaccurate or misleading. Finally, our group reported that 77% of the first 150 YouTube videos about prostate cancer had potentially misinformative and/or biased content in the video itself or the comments underneath [7]. Alarmingly, the quality of information was inversely correlated with the number of views. More research is needed to evaluate the impact of exposure to online misinformation on prostate cancer screening and treatment.

Overall, the online environment holds great promise and also great peril in prostate cancer. On one hand, digital networks have opened up new opportunities for global scientific exchange and have the potential to greatly improve patient care. Conversely, there is a substantial amount of misinformation on the internet, and the potential for a negative impact on patients and their families. As a urological community, we should be pro-active about directing our patients to trustworthy online resources, and should actively participate in digital networks to help share high-quality information with the public. More strategic effort should also be made to maximize the degree of reach and engagement upon dissemination of high-quality information.

by Stacy Loeb, Nataliya Byrne and Jeremy Teoh

References

  1. Rezaee ME, Goddard B, Sverrisson EF, Seigne JD, Dagrosa LM. ‘Dr Google’: trends in online interest in prostate cancer screening, diagnosis and treatment. BJU Int 2019; 124: 629–34
  2. Magnani CJ, Li K, Seto T et al. PSA Testing Use and Prostate Cancer Diagnostic Stage After the 2012 U.S. Preventive Services Task Force Guideline Changes. JNCCN 2019; 17: 795–803
  3. Cacciamani GE, Bassi S, Sebben M et al. Consulting “Dr. Google” for prostate cancer treatment options. A contemporary worldwide trend analysis. Eur Urol Oncol 2019; https://doi.org/10.1016/j.euo.2019.07.002
  4. Borgmann H, Salem J, Baunacke M et al. Mapping the landscape of urology: a new media-based cross-sectional analysis of public versus academic interest. Int J Urol 2018; 25: 421–8
  5. Asafu-Adjei D, Mikkilineni N, Sebesta E, Hyams E. Misinformation on the Internet regarding Ablative Therapies for Prostate Cancer. Urology 2019; https://doi.org/10.1016/j.urology.2018.12.050
  6. Alsyouf M, Stokes P, Hur D, Amasyali A, Ruckle H, Hu B. ‘Fake News’ in urology: evaluating the accuracy of articles shared on social media in genitourinary malignancies. BJU Int 2019; 124: 701–6
  7. Loeb S, Sengupta S, Butaney M et al. Dissemination of Misinformative and Biased Information about Prostate Cancer on YouTube. Eur Urol 2019; 27: 564–7

 

Article of the month: Current status of artificial intelligence applications in urology and their potential to influence clinical practice

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Every month, the Editor-in-Chief selects an Article of the Month 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  and a visual abstract produced by prominent members 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 month, it should be this one.

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Jian Chen*, Daphne Remulla*, Jessica H. Nguyen*, D. Aastha†, Yan Liu†, Prokar Dasgupta‡ and Andrew J. Hung*

*Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, University of Southern California Institute of Urology, †Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA, and ‡Division of Transplantation Immunology and Mucosal Biology, Faculty of Life Sciences and Medicine, Kings College London, London, UK

Read the full article

Abstract

Objective

To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome prediction in urologic diseases and evaluate its advantages over traditional models and methods.

Materials and methods

A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and non-urologic studies were excluded.

Results

Initial search yielded 231 articles, but after excluding duplicates and following full‐text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction.

Conclusion

AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence‐based and individualized patient care.

Read more Articles of the week

 

Editorial: Machines in urology: a brief odyssey of the future

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Artificial intelligence (AI) will bring in a new wave of changes in the medical field, likely altering how we practice medicine. In a timely contribution, Chen et al. [1] outline the current landscape of AI and provide us with a glimpse of the future, in which sophisticated computers and algorithms play a front-and-centre role in the daily hospital routine.

Widespread adoption of electronic medical records (EMRs), an ever-increasing amount of radiographic imaging, and the ubiquity of genome sequencing, among other factors, have created an impossibly large body of medical data. This poses obvious challenges for clinicians to remain abreast of new discoveries, but also presents new opportunities for scientific discovery. AI is the inevitable and much-needed tool with which to harness the ‘big data’ of medicine.

Currently, the most immediate and important application of AI appears to be in the field of diagnostics and radiology. In prostate cancer, for example, machine learning algorithms (MLAs) are not only able to automate radiographic detection of prostate cancer but have also been shown to improve diagnostic accuracy compared to standard clinical scoring schemes. MLAs can use clinicopathological data to predict clinically significant prostate cancer and disease recurrence
with a high degree of accuracy. The same has been shown for other urological malignancies, including urothelial cancer and RCC. Implementation of MLAs will lead to improved accuracy and reproducibility, reducing human bias and variability. We also predict that as natural language processing becomes more sophisticated, the troves of nonstructured data that exist in EMRs will be harnessed to deliver improved and more personalized patient care. Patient data and clinical outcomes can be analysed in short time, drawing from a deep body of knowledge, and leading to rapid insights that can guide medical decision-making.

Current AI technology, however, remains experimental and we are still far from the widespread implementation of AI within clinical medicine. A valid criticism of today’s AI is that it functions in the setting of a ‘black box’; the rules that govern the clinical decision-making of an algorithm are often poorly understood or unknowable. We cannot become operators of machines for which we know not how they work, to do so would be to practice medicine blindly.

Another barrier to incorporating AI into common practice is the level of noise in healthcare data. MLAs will use whatever data that are fed to the algorithm, thus running the risk of producing predicative models that include nonsensical variables gleaned from the noise. This concept is similar to multiple hypothesis-testing, where if you feed enough random information into a model, a pattern might emerge. Furthermore, none of the studies described by Chen et al. have been externally validated on large, representative datasets of diverse patients. MLAs trained on a narrow patient population run the risk of creating predictions that
are not generalizable. This problem has already been popularized within genome analysis, where one study found that 81% of all genome-wide studies were taken from individuals of European ancestry [2]. It is easy to imagine situations where risk score calculators or biomarkers are validated using non-representative datasets, leading to less accurate and even inappropriate treatment decisions for underrepresented patient populations. At best, MLAs that are not validated using stringent principles can lead to erroneous disease models. At worst, they can bias the delivery of healthcare to patients, leading to worse patient outcomes and exacerbation of healthcare disparities.

Chen et al. write of the possibility of AI in urology today. What about the future? Imagine a world in which computers with a robotic interface see patients in clinics, design and carry out complex medical treatment plans, and perform surgery without the aid of a human hand. This future may not be far off [3]. Or, even stranger, consider a world in which generalizable AI exists. Estimates of the dawn of this technology range, however the most optimistic projections put the timeline on the order of 20–30 years. Not far behind could be the ‘singularity’, a moment when technological advancement occurs at such an exponential rate that improbable scientific discoveries happen almost instantaneously, setting off a feed-forward cycle leading to an inconceivable superintelligence.

The future is, of course, hard to predict. Nevertheless, AI and the ensuing technology will certainly transform the practice of urology, albeit not without significant challenges and growing pains along the way. The urologist of the future may look very different indeed.

by Stephen W. Reese, Emily Ji, Aliya Sahraoui and Quoc-Dien Trinh

 

References

  1. Chen J, Remulla D, Nguyen JH et al. Current status of artificial intelligence applications in Urology and its potential to influence clinical practice. BJU Int 2019; 124: 567–77
  2. Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature 2016; 538: 161–4
  3. Grace K, Salvatier J, Dafoe A, Zhang B, Evans O. When Will AI Exceed Human Performance? Evidence from AI Experts, 2017

 

Video: Current status of artificial intelligence applications in urology

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Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Read the full article

Abstract

Objective

To investigate the applications of artificial intelligence (AI) in diagnosis, treatment and outcome prediction in urologic diseases and evaluate its advantages over traditional models and methods.

Materials and methods

A literature search was performed after PROSPERO registration (CRD42018103701) and in compliance with Preferred Reported Items for Systematic Reviews and Meta‐Analyses (PRISMA) methods. Articles between 1994 and 2018 using the search terms “urology”, “artificial intelligence”, “machine learning” were included and categorized by the application of AI in urology. Review articles, editorial comments, articles with no full‐text access, and non-urologic studies were excluded.

Results

Initial search yielded 231 articles, but after excluding duplicates and following full‐text review and examination of article references, only 111 articles were included in the final analysis. AI applications in urology include: utilizing radiomic imaging or ultrasonic echo data to improve or automate cancer detection or outcome prediction, utilizing digitized tissue specimen images to automate detection of cancer on pathology slides, and combining patient clinical data, biomarkers, or gene expression to assist disease diagnosis or outcome prediction. Some studies employed AI to plan brachytherapy and radiation treatments while others used video based or robotic automated performance metrics to objectively evaluate surgical skill. Compared to conventional statistical analysis, 71.8% of studies concluded that AI is superior in diagnosis and outcome prediction.

Conclusion

AI has been widely adopted in urology. Compared to conventional statistics AI approaches are more accurate in prediction and more explorative for analyzing large data cohorts. With an increasing library of patient data accessible to clinicians, AI may help facilitate evidence‐based and individualized patient care.

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Visual abstract: Current status of artificial intelligence applications in urology and their potential to influence clinical practice

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Article of the week: Targeted deep sequencing of urothelial bladder cancers and associated urinary DNA: a 23‐gene panel with utility for non‐invasive diagnosis and risk stratification

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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 and a video prepared by the authors. 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.

Targeted deep sequencing of urothelial bladder cancers and associated urinary DNA: a 23‐gene panel with utility for non‐invasive diagnosis and risk stratification

Douglas G. Ward*, Naheema S. Gordon*, Rebecca H. Boucher*, Sarah J. Pirrie*, Laura Baxter†, Sascha Ott†, Lee Silcock‡, Celina M. Whalley*, Joanne D. Stockton*, Andrew D. Beggs*, Mike Griffiths§, Ben Abbotts*, Hanieh Ijakipour*, Fathimath N.Latheef*, Robert A. Robinson*, Andrew J. White*, Nicholas D. James*, Maurice P.Zeegers, K. K. Cheng** and Richard T. Bryan*

 

*Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, †Department of Computer Science, University of Warwick, Coventry, ‡Nonacus Limited, Birmingham Research Park, §West Midlands Regional Genetics Laboratory, Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK, NUTRIM School for Nutrition and Translational Research in Metabolism and CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands and **Institute of Applied Health Research, University of Birmingham, Birmingham, UK

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Abstract

Objectives

To develop a focused panel of somatic mutations (SMs) present in the majority of urothelial bladder cancers (UBCs), to investigate the diagnostic and prognostic utility of this panel, and to compare the identification of SMs in urinary cell‐pellet (cp) DNA and cell‐free (cf) DNA as part of the development of a non‐invasive clinical assay.

Patients and Methods

A panel of SMs was validated by targeted deep‐sequencing of tumour DNA from 956 patients with UBC. In addition, amplicon and capture‐based targeted sequencing measured mutant allele frequencies (MAFs) of SMs in 314 urine cpDNAs and 153 urine cfDNAs. The association of SMs with grade, stage and clinical outcomes was investigated by univariate and multivariate Cox models. Concordance between SMs detected in tumour tissue and cpDNA and cfDNA was assessed.

 

Results

The panel comprised SMs in 23 genes: TERT (promoter), FGFR3, PIK3CA, TP53, ERCC2, RHOB, ERBB2, HRAS, RXRA, ELF3, CDKN1A, KRAS, KDM6A, AKT1, FBXW7, ERBB3, SF3B1, CTNNB1, BRAF, C3orf70, CREBBP, CDKN2A and NRAS; 93.5–98.3% of UBCs of all grades and stages harboured ≥1 SM (mean: 2.5 SMs/tumour). RAS mutations were associated with better overall survival (P = 0.04). Mutations in RXRA, RHOB and TERT (promoter) were associated with shorter time to recurrence (P < 0.05). MAFs in urinary cfDNA and cpDNA were highly correlated; using a capture‐based approach, >94% of tumour SMs were detected in both cpDNA and cfDNA.

Conclusions

SMs are reliably detected in urinary cpDNA and cfDNA. The technical capability to identify very low MAFs is essential to reliably detect UBC, regardless of the use of cpDNA or cfDNA. This 23‐gene panel shows promise for the non‐invasive diagnosis and risk stratification of UBC.

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