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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

Read the full article

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.

Read more Articles of the week

 

Editorial: Non‐invasive diagnosis and monitoring of urothelial bladder cancer: are we there yet?

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In this issue of BJUI, Ward et al. [1] describe the development of DNA‐based urinary biomarkers for urothelial carcinoma (UC). The genomics of UC have been well characterized through interrogation of tumour issues in institutional series (e.g. the Memorial Sloan Kettering Cancer Center [MSKCC] experience), multi‐institutional collaborations (e.g. The Cancer Genome Atlas [TCGA]) and commercial platforms (e.g. the Foundation Medicine experience) [2]. Until recently, these have been largely academic pursuits, with possible impact on prognostication but limited clinical applicability and utility for therapy selection and monitoring of response; however, with the US Food and Drug Administration approval of erdafitinib several weeks ago, patients with advanced UC will routinely receive genomic assessment for FGFR2/3 mutation or fusion, the targets for this therapy [3]. In due time, it is anticipated that multiple other putative targets with associated therapies (e.g. ERBB2, CDKN2A), as well as potential predictive biomarkers, may also warrant testing.

The evolving landscape in advanced UC makes a non‐invasive biomarker particularly attractive. The authors of the present commentary have previously reported results from a series of 369 patients with advanced UC, demonstrating that genomic alterations in ctDNA could be identified in 91% of patients using a commercially available 73-gene panel [4]. More recently, Christensen et al. [5] assessed a cohort of 68 patients receiving neoadjuvant chemotherapy for muscle‐invasive disease, demonstrating 100% sensitivity and 98% specificity for the detection of relapsed disease with a patient‐specific ctDNA assessment (sequenced to a median target coverage of 105 000×) after cystectomy. Impressively, the data also showed that the dynamics of ctDNA appeared to be more useful than pathological downstaging in predicting relapse.

In contrast to these studies, Ward et al. have developed a 23‐gene panel based on frequently expressed genes in a cohort of 916 UC tissue specimens, largely derived from patients with non‐muscle‐invasive disease. Ultimately, with a cohort of 314 patients with DNA derived from a urinary cell pellet, sequencing identified 645 (71.4%) of 903 mutations detected in tumour. Using urinary supernatant, 353 (80.7%) of 437 mutations were detected. These relatively high sensitivities, if they can be interpreted as such, are promising but do not rise to the level of replacing existing strategies for UC detection, staging and monitoring. Notably, another study demonstrated that urinary ctDNA can be detected with high sensitivity and specificity in patients with localized early‐stage bladder cancer and for after‐treatment surveillance, providing the foundation for further studies evaluating the role of ctDNA in non‐invasive detection, genotyping and monitoring [6].

Beyond its use as a diagnostic tool, it is hoped that urinary ctDNA may also find applications in the selection of therapeutics. To this end, Ward et al. identified FGFR3, PIK3CA, ERCC2 and ERBB2 mutations in 45%, 32%, 14% and 7% of patients, respectively. The frequency of FGFR3 alteration decreased with increasing stage and grade, ranging from 72% in pTaG1 disease to just 13% in ≥pT2 disease, consistent with other reports [7]. These results may guide forthcoming studies evaluating FGFR inhibitors in non‐muscle‐invasive, muscle‐invasive and metastatic disease, where studies are ongoing. In reviewing the potential link between genomic alterations and clinical outcomes, perhaps the most curious finding is that between RAS mutations and improved overall survival (P = 0.04), the only such association found in multivariate analysis. These results stand in sharp contrast to reports in lung cancer, colorectal cancer and multiple other tumour types [8]. A closer look at the deleterious nature and functional impact of NRAS and KRAS mutations seen in this series is certainly warranted, along with further external validation in a more homogenous and larger patient population. There is also the potential application of monitoring treatment response by assessing eradication of urinary ctDNA, a hypothesis that is being evaluated in ongoing studies [9].

How will the results of this and other emerging urinary biomarker studies eventually make their way to the clinic? The answer is simple: incorporation of these biomarkers in prospective therapeutic trials. As the bladder cancer investigative community formulates novel trials for non‐muscle‐invasive and muscle‐invasive disease using targeted therapies, an excellent opportunity exists to correlate urinary, blood and tissue‐based biomarkers and to assess their relative predictive capabilities and clinical utility. Furthermore, with clinical surrogate endpoints likely to drive regulatory approval (e.g. landmark complete response rates for non‐muscle‐invasive disease, or pT0N0 rate for muscle‐invasive disease), a validated urinary biomarker could ultimately offer an alternative biological surrogate endpoint [10]. In an era of genomic revolution, prospective validation can help establish the potential clinical utility of promising biomarkers and help realize the dream of ‘precision oncology’.

by Rohit K. Jain, Petros Grivas and Sumanta K. Pal

References

  1. Ward DG, Gordon NS, Boucher RH et al. Targeted deep sequencing of urothelial bladder cancers and associated urinary DNA: a 23‐gene panel with utility for non‐invasive diagnosis and risk stratification. BJU Int 2019
  2. Schiff JP, Barata PC, Yu EY, Grivas P. Precision therapy in advanced urothelial cancer. Expert Rev Precis Med Drug Dev 2019; 4: 81– 93
  3. FDA grants accelerated approval to erdafitinib for metastatic urothelial carcinoma [press release] 2019.
  4. Agarwal N, Pal SK, Hahn AW et al. Characterization of metastatic urothelial carcinoma via comprehensive genomic profiling of circulating tumor DNA. Cancer 2018; 124: 2115– 24
  5. Christensen E, Birkenkamp‐Demtroder K, Sethi H et al. Early detection of metastatic relapse and monitoring of therapeutic efficacy by ultra‐deep sequencing of plasma cell‐free DNA in patients with urothelial bladder carcinoma. J Clin Oncol 2019; 37: 1547– 57
  6. Dudley JC, Schroers‐Martin J, Lazzareschi DV et al. Detection and surveillance of bladder cancer using urine tumor DNA. Cancer Discov 2019; 9: 500– 9
  7. Tomlinson DC, Baldo O, Harnden P, Knowles MA. FGFR3 protein expression and its relationship to mutation status and prognostic variables in bladder cancer. J Pathol 2007; 213: 91– 8
  8. Zhuang R, Li S, Li Q et al. The prognostic value of KRAS mutation by cell‐free DNA in cancer patients: a systematic review and meta‐analysis. PLoS One 2017; 12: e0182562
  9. Abbosh PH, Plimack ER. Molecular and clinical insights into the role and significance of mutated DNA repair genes in bladder cancer. Bladder Cancer 2018; 4: 9– 18
  10. Jarow JP, Lerner SP, Kluetz PG et al. Clinical trial design for the development of new therapies for nonmuscle‐invasive bladder cancer: report of a Food and Drug Administration and American Urological Association public workshop. Urology 2014; 83: 262– 4

 

 

Video: Targeted deep sequencing of urothelial bladder cancers and associated urinary DNA

/by

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

Read the full article

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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