Evidence map›Paper›PMID 28753852›Full record

ArticleEuropean urology focus2018

DESNT: A Poor Prognosis Category of Human Prostate Cancer.

Bogdan-Alexandru Luca, Daniel S Brewer, Dylan R Edwards, Sandra Edwards, Hayley C Whitaker, Sue Merson, Nening Dennis, Rosalin A Cooper, Steven Hazell, Anne Y Warren and 13 more

Abstract read
In one paragraph

Article in European urology focus, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Bogdan-Alexandru LucaSchool 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.
Daniel S BrewerNorwich 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.
Dylan R EdwardsNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK.
Sandra EdwardsDivision of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK.
Hayley C WhitakerUrological Research Laboratory, Cancer Research UK Cambridge Research Institute, University of Cambridge, Cambridge, UK.
Sue MersonDivision of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK.
Nening DennisDivision of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK.
Rosalin A CooperDepartment of Pathology, University Hospital Southampton NHS Foundation Trust, Southampton, UK.
Steven HazellRoyal Marsden NHS Foundation Trust, London and Sutton, UK.
Anne Y WarrenDepartment of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
CancerMap Group
Rosalind EelesDivision of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK; Royal Marsden NHS Foundation Trust, London and Sutton, UK.
Andy G LynchUrological Research Laboratory, Cancer Research UK Cambridge Research Institute, University of Cambridge, Cambridge, UK.
Helen Ross-AdamsUrological Research Laboratory, Cancer Research UK Cambridge Research Institute, University of Cambridge, Cambridge, UK.
Alastair D LambUrological 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.
David E NealUrological 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.
Krishna SethiaDepartment of Urology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK.
Robert D MillsDepartment of Urology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK.
Richard Y BallDepartment of Histopathology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK.
Helen CurleyNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK.
Jeremy ClarkNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK.
Vincent MoultonSchool of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk, UK.
Colin S CooperNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK. Electronic address: Colin.Cooper@uea.ac.uk.

Funding

Cancer Research UK 20406Cancer Research UK A10047
6 · The paper itself

Abstract

backgroundA 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.

objectiveTo devise a novel classification framework for human prostate cancer based on unsupervised mathematical approaches. DESIGN, SETTING, AND

participantsOur 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

conclusionsOur 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.

Indexed as

Bayes TheoremCell AdhesionCytoskeletonGene Expression ProfilingHumansIon TransportKallikreinsMaleNeoplasm Recurrence, LocalPrognosisProportional Hazards ModelsProstatectomyProstate-Specific AntigenProstatic NeoplasmsKallikreinsKLK3 protein, humanProstate-Specific AntigenDESNT prostate cancerLatent Process DecompositionNovel prostate cancer classificationPoor prognosis category

Identifiers

PMID28753852
PMCPMC5669460

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.