Evidence map›Paper›PMID 27259534›Full record

ArticleBMC cancer2016

Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models.

E López de Maturana, A Picornell, A Masson-Lecomte, M Kogevinas, M Márquez, A Carrato, A Tardón, J Lloreta, M García-Closas, D Silverman and 6 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 20% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
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

16 authors at 6 institutions in 4 countries.

E López de MaturanaGenetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain.
A PicornellGenetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain.
A Masson-LecomteGenetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain.
M KogevinasCentre for Research in Environmental Epidemiology (CREAL), Parc de Salut Mar, Barcelona, Spain.
M MárquezGenetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain.
A CarratoServicio de Oncología, Hospital Universitario Ramon y Cajal, Madrid, and Servicio de Oncología, Hospital Universitario de Elche, Elche, Spain.
A TardónDepartment of Preventive Medicine Universidad de Oviedo, Oviedo, Spain.
J LloretaParc de Salut Mar and Departament of Pathology, Hospital del Mar - IMAS, Barcelona, Spain.
M García-ClosasDivision of Genetics and Epidemiology, Institute of Cancer Research, London, UK.
D SilvermanDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland, USA.
N RothmanDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland, USA.
S ChanockDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland, USA.
F X RealEpithelial Carcinogenesis Group, Spanish National Cancer Research Centre (CNIO), Madrid, and Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.
M E GoddardBiosciences Research Division, Department of Environment and Primary Industries, Agribio, and Department of Food and Agricultural Systems, University of Melbourne, Melbourne, Australia.
N MalatsGenetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain. nmalats@cnio.es.
SBC/EPICURO Study Investigators
Spanish National Cancer Research Centre · ESInstitute of Cancer Research · GBNational Cancer Institute · USThe University of Melbourne · AUUniversidad de Oviedo · ESUniversitat Pompeu Fabra · ES

Funding

NCI NIH HHS N02 CP011015
6 · The paper itself

Abstract

backgroundWe adapted Bayesian statistical learning strategies to the prognosis field to investigate if genome-wide common SNP improve the prediction ability of clinico-pathological prognosticators and applied it to non-muscle invasive bladder cancer (NMIBC) patients.

methodsAdapted Bayesian sequential threshold models in combination with LASSO were applied to consider the time-to-event and the censoring nature of data. We studied 822 NMIBC patients followed-up >10 years. The study outcomes were time-to-first-recurrence and time-to-progression. The predictive ability of the models including up to 171,304 SNP and/or 6 clinico-pathological prognosticators was evaluated using AUC-ROC and determination coefficient.

resultsClinico-pathological prognosticators explained a larger proportion of the time-to-first-recurrence (3.1 %) and time-to-progression (5.4 %) phenotypic variances than SNPs (1 and 0.01 %, respectively). Adding SNPs to the clinico-pathological-parameters model slightly improved the prediction of time-to-first-recurrence (up to 4 %). The prediction of time-to-progression using both clinico-pathological prognosticators and SNP did not improve. Heritability (ĥ (2)) of both outcomes was <1 % in NMIBC.

conclusionsWe adapted a Bayesian statistical learning method to deal with a large number of parameters in prognostic studies. Common SNPs showed a limited role in predicting NMIBC outcomes yielding a very low heritability for both outcomes. We report for the first time a heritability estimate for a disease outcome. Our method can be extended to other disease models.

Indexed as

Bayes TheoremAdultAgedAged, 80 and overArea Under CurveBiomarkers, TumorCarcinoma, Transitional CellDisease ProgressionGenotypeHumansMaleMiddle AgedNeoplasm Recurrence, LocalPolymorphism, Single NucleotidePredictive Value of TestsPrognosisBiomarkers, TumorAUC-ROCBayesian LASSOBayesian regressionBayesian statistical learning methodBladder cancer outcomeDetermination coefficientGenome-wide common SNPheritabilityIllumina Infinium HumanHap 1 M arrayMultimarker modelsPredictive abilityPrognosisProgressionRecurrence

Identifiers

PMID27259534
PMCPMC4893282
OpenAlexW2410143861

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.