Evidence map›Paper›PMID 25047176›Full record

ArticleEuropean urology2014

Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers.

Sakshi Gulati, Pierre Martinez, Tejal Joshi, Nicolai Juul Birkbak, Claudio R Santos, Andrew J Rowan, Lisa Pickering, Martin Gore, James Larkin, Zoltan Szallasi and 3 more

Open access · hybridAbstract readValidation Study
In one paragraph

Article in European urology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
99citing papers in PubMed, 2 pooled it
13.0field-weighted citation impact, top 1% of its field
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

99 citing papers in PubMed, 2 syntheses or guidelines pooled it, 169 citations in OpenAlex.

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39 more citing papers are in PubMed but not listed here.

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

13 authors at 6 institutions in 3 countries.

Sakshi GulatiBiomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, UK.
Pierre MartinezTranslational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, London, UK.
Tejal JoshiCentre for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
Nicolai Juul BirkbakCentre for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
Claudio R SantosTranslational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, London, UK.
Andrew J RowanTranslational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, London, UK.
Lisa PickeringThe Royal Marsden Hospital, London, UK.
Martin GoreThe Royal Marsden Hospital, London, UK.
James LarkinThe Royal Marsden Hospital, London, UK.
Zoltan SzallasiCentre for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark; Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA.
Paul A BatesBiomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, UK. Electronic address: paul.batest@cancer.org.uk.
Charles SwantonTranslational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, London, UK; UCL Cancer Institute, London, UK. Electronic address: charles.swanton@cancer.org.uk.
Marco GerlingerTranslational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, London, UK; Present address: Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
Cancer Research UK · GBRoyal Marsden Hospital · GBTechnical University of Denmark · DKCRUK Lung Cancer Centre of Excellence · GBHarvard University · USInstitute of Cancer Research · GB

Funding

Cancer Research UK 10748Cancer Research UK 17786Cancer Research UK 19310Medical Research Council G0902275
6 · The paper itself

Abstract

backgroundCandidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated.

objectiveTo validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation. DESIGN, SETTING, AND

participantsCancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Biomarker association with CSS was analysed by univariate and multivariate analyses. RESULTS AND LIMITATIONS: A total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers.

conclusionsThe ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker. PATIENT SUMMARY: We evaluated the ability of published biomarkers to predict the survival of patients with clear cell kidney cancer in an independent patient cohort. Only one molecular test adds prognostic information to routine clinical assessments. This marker showed good and poor prognosis results within most individual cancers. Future biomarkers need to consider variation within tumours to improve accuracy.

Indexed as

AgedBiomarkers, TumorBiopsyCarcinoma, Renal CellDNA Mutational AnalysisFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGenetic Predisposition to DiseaseHumansKaplan-Meier EstimateKidney NeoplasmsMaleMiddle AgedMultivariate AnalysisMutationBiomarkers, TumorBiomarkerIntratumour heterogeneityKidney cancerPersonalised medicinePrognostic marker

Identifiers

PMID25047176
PMCPMC4410302
OpenAlexW2018248081

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.