Bogdan-Alexandru Luca1, Daniel S Brewer2, Dylan R Edwards3, Sandra Edwards4, Hayley C Whitaker5, Sue Merson4, Nening Dennis4, Rosalin A Cooper6, Steven Hazell7, Anne Y Warren8, Rosalind Eeles9, Andy G Lynch5, Helen Ross-Adams5, Alastair D Lamb10, David E Neal10, Krishna Sethia11, Robert D Mills11, Richard Y Ball12, Helen Curley3, Jeremy Clark3, Vincent Moulton13, Colin S Cooper14. 1. School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk, UK; Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK. 2. Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK; The Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK. Electronic address: d.brewer@uea.ac.uk. 3. Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK. 4. Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK. 5. Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, University of Cambridge, Cambridge, UK. 6. Department of Pathology, University Hospital Southampton NHS Foundation Trust, Southampton, UK. 7. Royal Marsden NHS Foundation Trust, London and Sutton, UK. 8. Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. 9. Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK; Royal Marsden NHS Foundation Trust, London and Sutton, UK. 10. Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, University of Cambridge, Cambridge, UK; Department of Surgical Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK. 11. Department of Urology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK. 12. Department of Histopathology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK. 13. School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk, UK. 14. Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK. Electronic address: Colin.Cooper@uea.ac.uk.
Abstract
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. OBJECTIVE: To devise a novel classification framework for human prostate cancer based on unsupervised mathematical approaches. 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. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Biochemical failure was assessed using log-rank analysis and Cox regression analysis. 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 p=2.98×10-8). When these three datasets were combined the independent predictive value of DESNT membership was p=1.61×10-7 compared with p=1.00×10-5 for Gleason sum. A limitation of the study is that only prediction of prostate-specific antigen failure was examined. 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. 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.
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. OBJECTIVE: To devise a novel classification framework for human prostate cancer based on unsupervised mathematical approaches. 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. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Biochemical failure was assessed using log-rank analysis and Cox regression analysis. 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 p=2.98×10-8). When these three datasets were combined the independent predictive value of DESNT membership was p=1.61×10-7 compared with p=1.00×10-5 for Gleason sum. A limitation of the study is that only prediction of prostate-specific antigen failure was examined. 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. 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.
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