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dc.contributor.authorLuca, Bogdan-Alexandruen
dc.contributor.authorBrewer, Daniel Sen
dc.contributor.authorEdwards, Dylan Ren
dc.contributor.authorEdwards, Sandraen
dc.contributor.authorWhitaker, Hayleyen
dc.contributor.authorMerson, Sueen
dc.contributor.authorDennis, Neningen
dc.contributor.authorCooper, Rosalin Aen
dc.contributor.authorHazell, Stevenen
dc.contributor.authorWarren, Anneen
dc.contributor.authorCancerMap, Groupen
dc.contributor.authorEeles, Rosalinden
dc.contributor.authorLynch, Andyen
dc.contributor.authorRoss-Adams, Helenen
dc.contributor.authorLamb, Alastairen
dc.contributor.authorNeal, Daviden
dc.contributor.authorSethia, Krishnaen
dc.contributor.authorMills, Robert Den
dc.contributor.authorBall, Richard Yen
dc.contributor.authorCurley, Helenen
dc.contributor.authorClark, Jeremyen
dc.contributor.authorMoulton, Vincenten
dc.contributor.authorCooper, Colin Sen
dc.date.accessioned2017-04-21T08:53:53Z
dc.date.available2017-04-21T08:53:53Z
dc.date.issued2017-03-06en
dc.identifier.issn2405-4569
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/263740
dc.description.abstract$\textbf{Background}$: A critical problem in the clinical management of prostate cancer is that it is highly heterogeneous. Accurate prediction of individual cancer behaviour is therefore not achievable at the time of diagnosis leading to substantial overtreatment. It remains an enigma that, in contrast to breast cancer, unsupervised analyses of global expression profiles have not currently defined robust categories of prostate cancer with distinct clinical outcomes. $\textbf{Objective}$: To devise a novel classification framework for human prostate cancer based on unsupervised mathematical approaches. $\textbf{Design, setting, and participants}$: Our analyses are based on the hypothesis that previous attempts to classify prostate cancer have been unsuccessful because individual samples of prostate cancer frequently have heterogeneous compositions. To address this issue, we applied an unsupervised Bayesian procedure called Latent Process Decomposition to four independent prostate cancer transcriptome datasets obtained using samples from prostatectomy patients and containing between 78 and 182 participants. $\textbf{Outcome measurements and statistical analysis: }$ Biochemical failure was assessed using log-rank analysis and Cox regression analysis. $\textbf{Results and limitations:}$ Application of Latent Process Decomposition identified a common process in all four independent datasets examined. Cancers assigned to this process (designated DESNT cancers) are characterized by low expression of a core set of 45 genes, many encoding proteins involved in the cytoskeleton machinery, ion transport, and cell adhesion. For the three datasets with linked prostate-specific antigen failure data following prostatectomy, patients with DESNT cancer exhibited poor outcome relative to other patients (p =2.65×10$^{-5}$, p =4.28×10$^{-5}$, and $\textit{p}$ =2.98×10$^{-8}$). When these three datasets were combined the independent predictive value of DESNT membership was $\textit{p}$ =1.61×10$^{-7}$ compared with $\textit{p}$ =1.00×10$^{-5}$ for Gleason sum. A limitation of the study is that only prediction of prostate-specific antigen failure was examined. $\textbf{Conclusions:}$ Our results demonstrate the existence of a novel poor prognosis category of human prostate cancer and will assist in the targeting of therapy, helping avoid treatment-associated morbidity in men with indolent disease. $\textbf{Patient summary:}$ Prostate cancer, unlike breast cancer, does not have a robust classification framework. We propose that this failure has occurred because prostate cancer samples selected for analysis frequently have heterozygous compositions (individual samples are made up of many different parts that each have different characteristics). Applying a mathematical approach that can overcome this problem we identify a novel poor prognosis category of human prostate cancer called DESNT. The study identifies a novel poor prognostic category of human prostate cancer designed DESNT.
dc.description.sponsorshipThis work was funded by the Bob Champion Cancer Trust, The Masonic Charitable Foundation successor to The Grand Charity, The King Family, and The University of East Anglia. We acknowledge support from Movember, from Prostate Cancer UK, Callum Barton, and from The Andy Ripley Memorial Fund. The research presented in this paper was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia. Cancer Research UK Grant 10047 funded the generation of the prostate CancerMap expression microarray dataset. We would like to acknowledge the support of the National Institute for Health Research which funds the Cambridge Bio-medical Research Centre, Cambridge UK.
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.language.isoenen
dc.publisherElsevier
dc.subjectCancerMap Groupen
dc.titleDESNT: A Poor Prognosis Category of Human Prostate Cancer.en
dc.typeArticle
prism.publicationDate2017en
prism.publicationNameEuropean urology focusen
dc.identifier.doi10.17863/CAM.9106
dcterms.dateAccepted2017-01-28en
rioxxterms.versionofrecord10.1016/j.euf.2017.01.016en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-03-06en
dc.contributor.orcidWarren, Anne [0000-0002-1170-7867]
dc.contributor.orcidLynch, Andy [0000-0002-7876-7338]
dc.identifier.eissn2405-4569
rioxxterms.typeJournal Article/Reviewen
rioxxterms.freetoread.startdate2018-03-06


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