Literature DB >> 28753852

DESNT: A Poor Prognosis Category of Human Prostate Cancer.

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.   

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.
Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  DESNT prostate cancer; Latent Process Decomposition; Novel prostate cancer classification; Poor prognosis category

Mesh:

Substances:

Year:  2017        PMID: 28753852      PMCID: PMC5669460          DOI: 10.1016/j.euf.2017.01.016

Source DB:  PubMed          Journal:  Eur Urol Focus        ISSN: 2405-4569


  38 in total

1.  A molecular signature of metastasis in primary solid tumors.

Authors:  Sridhar Ramaswamy; Ken N Ross; Eric S Lander; Todd R Golub
Journal:  Nat Genet       Date:  2002-12-09       Impact factor: 38.330

2.  Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy.

Authors:  Andrew J Stephenson; Alex Smith; Michael W Kattan; Jaya Satagopan; Victor E Reuter; Peter T Scardino; William L Gerald
Journal:  Cancer       Date:  2005-07-15       Impact factor: 6.860

3.  The latent process decomposition of cDNA microarray data sets.

Authors:  Simon Rogers; Mark Girolami; Colin Campbell; Rainer Breitling
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2005 Apr-Jun       Impact factor: 3.710

4.  Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study.

Authors:  Jack Cuzick; Gregory P Swanson; Gabrielle Fisher; Arthur R Brothman; Daniel M Berney; Julia E Reid; David Mesher; V O Speights; Elzbieta Stankiewicz; Christopher S Foster; Henrik Møller; Peter Scardino; Jorja D Warren; Jimmy Park; Adib Younus; Darl D Flake; Susanne Wagner; Alexander Gutin; Jerry S Lanchbury; Steven Stone
Journal:  Lancet Oncol       Date:  2011-03       Impact factor: 41.316

5.  Method for sampling tissue for research which preserves pathological data in radical prostatectomy.

Authors:  Anne Y Warren; Hayley C Whitaker; Beverley Haynes; Trogon Sangan; Leigh-Anne McDuffus; Jonathan D Kay; David E Neal
Journal:  Prostate       Date:  2012-07-16       Impact factor: 4.104

6.  Regularization Paths for Generalized Linear Models via Coordinate Descent.

Authors:  Jerome Friedman; Trevor Hastie; Rob Tibshirani
Journal:  J Stat Softw       Date:  2010       Impact factor: 6.440

7.  TMPRSS2:ERG gene fusion predicts subsequent detection of prostate cancer in patients with high-grade prostatic intraepithelial neoplasia.

Authors:  Kyung Park; James T Dalton; Ramesh Narayanan; Christopher E Barbieri; Michael L Hancock; David G Bostwick; Mitchell S Steiner; Mark A Rubin
Journal:  J Clin Oncol       Date:  2013-12-02       Impact factor: 44.544

8.  Cancer-specific mortality after surgery or radiation for patients with clinically localized prostate cancer managed during the prostate-specific antigen era.

Authors:  Anthony V D'Amico; Judd Moul; Peter R Carroll; Leon Sun; Deborah Lubeck; Ming-Hui Chen
Journal:  J Clin Oncol       Date:  2003-06-01       Impact factor: 44.544

9.  Complex patterns of ETS gene alteration arise during cancer development in the human prostate.

Authors:  J Clark; G Attard; S Jhavar; P Flohr; A Reid; J De-Bono; R Eeles; P Scardino; J Cuzick; G Fisher; M D Parker; C S Foster; D Berney; G Kovacs; C S Cooper
Journal:  Oncogene       Date:  2007-10-08       Impact factor: 9.867

10.  Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy.

Authors:  Nicholas Erho; Anamaria Crisan; Ismael A Vergara; Anirban P Mitra; Mercedeh Ghadessi; Christine Buerki; Eric J Bergstralh; Thomas Kollmeyer; Stephanie Fink; Zaid Haddad; Benedikt Zimmermann; Thomas Sierocinski; Karla V Ballman; Timothy J Triche; Peter C Black; R Jeffrey Karnes; George Klee; Elai Davicioni; Robert B Jenkins
Journal:  PLoS One       Date:  2013-06-24       Impact factor: 3.240

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  8 in total

1.  Histone methyltransferase DOT1L coordinates AR and MYC stability in prostate cancer.

Authors:  R Vatapalli; V Sagar; Y Rodriguez; J C Zhao; K Unno; S Pamarthy; B Lysy; J Anker; H Han; Y A Yoo; M Truica; Z R Chalmers; F Giles; J Yu; D Chakravarti; B Carneiro; S A Abdulkadir
Journal:  Nat Commun       Date:  2020-08-19       Impact factor: 14.919

2.  Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy.

Authors:  Shea P Connell; Robert Mills; Hardev Pandha; Richard Morgan; Colin S Cooper; Jeremy Clark; Daniel S Brewer
Journal:  Cancers (Basel)       Date:  2021-04-27       Impact factor: 6.639

3.  Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression.

Authors:  Bogdan-Alexandru Luca; Vincent Moulton; Christopher Ellis; Shea P Connell; Daniel S Brewer; Colin S Cooper
Journal:  Genes (Basel)       Date:  2020-07-16       Impact factor: 4.096

4.  IRE1α-XBP1s pathway promotes prostate cancer by activating c-MYC signaling.

Authors:  Xia Sheng; Hatice Zeynep Nenseth; Su Qu; Omer F Kuzu; Turid Frahnow; Lukas Simon; Stephanie Greene; Qingping Zeng; Ladan Fazli; Paul S Rennie; Ian G Mills; Håvard Danielsen; Fabian Theis; John B Patterson; Yang Jin; Fahri Saatcioglu
Journal:  Nat Commun       Date:  2019-01-24       Impact factor: 14.919

Review 5.  Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges.

Authors:  Dimitrios Doultsinos; Ian G Mills
Journal:  Cancers (Basel)       Date:  2021-01-28       Impact factor: 6.639

6.  Validation of diagnostic nomograms based on CE-MS urinary biomarkers to detect clinically significant prostate cancer.

Authors:  Maria Frantzi; Isabel Heidegger; Marie C Roesch; Enrique Gomez-Gomez; Eberhard Steiner; Antonia Vlahou; William Mullen; Ipek Guler; Axel S Merseburger; Harald Mischak; Zoran Culig
Journal:  World J Urol       Date:  2022-07-16       Impact factor: 3.661

7.  A Four-Group Urine Risk Classifier for Predicting Outcome in Prostate Cancer Patients.

Authors:  Shea P Connell; Marcel Hanna; Frank McCarthy; Rachel Hurst; Martyn Webb; 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; Chris Parker; Daniel S Brewer; Colin S Cooper; Jeremy Clark
Journal:  BJU Int       Date:  2019-05-20       Impact factor: 5.588

8.  A comparative study of PCS and PAM50 prostate cancer classification schemes.

Authors:  Junhee Yoon; Minhyung Kim; Edwin M Posadas; Stephen J Freedland; Yang Liu; Elai Davicioni; Robert B Den; Bruce J Trock; R Jeffrey Karnes; Eric A Klein; Michael R Freeman; Sungyong You
Journal:  Prostate Cancer Prostatic Dis       Date:  2021-02-02       Impact factor: 5.554

  8 in total

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