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PREDICTION OF NON-MUSCLE INVASIVE BLADDER CANCER OUTCOMES ASSESSED BY INNOVATIVE MULTIMARKER PROGNOSTIC MODELS

Autoři: Maturana E. López De1, Picornell A.1, Masson-Lecomte A.1, Kogevinas M.2,10, Márquez M.1, Carrato A.3, Tardón A.4,10, Lloreta J.5, García-Closas M.6, Silverman D.7, Rothman N.7, Chanock S.7, Real F. X.8, Goddard M. E.9, Malats N.1*, Investigators and On behalf Of The Sbc/epicuro Study

Autoři - působiště: 1Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro 3, 28029, Madrid, Spain, 2Centre for Research in Environmental Epidemiology (CREAL), Parc de Salut Mar, Barcelona, Spain, 3Servicio de Oncología, Hospital Universitario Ramon y Cajal, Madrid, and Servicio de Oncología, Hospital Universitario de Elche, Elche, Spain, 4Department of Preventive Medicine Universidad de Oviedo, Oviedo, Spain, 5Parc de Salut Mar and Departament of Pathology, Hospital del Mar - IMAS, Barcelona, Spain, 6Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK, 7Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland, USA, 8Epithelial Carcinogenesis Group, Spanish National Cancer Research Centre (CNIO), Madrid, and Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain, 9Biosciences Research Division, Department of Environment and Primary Industries, Agribio, and Department of Food and Agricultural Systems, University of Melbourne, Melbourne, Australia, 10CIBERESP, Madrid, Spain.

Článek: BMC Cancer 2016, 351:16
doi: 10.1186/s12885-016-2361-7
Kategorie: Research Article
Počet zobrazení článku: 21x

Specializace: dětská onkologie urologie onkologie
uzamčeno uzamčeno

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

© 2016 de Maturana et al.

Open access
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
The electronic version of this article is the complete one and can be found online at: http://bmccancer.biomedcentral.com/articles/10.1186/s12885-016-2361-7

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© 2016 de Maturana et al.

Open access
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
The electronic version of this article is the complete one and can be found online at: http://bmccancer.biomedcentral.com/articles/10.1186/s12885-016-2361-7

Summary

Background:
We 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.

Methods:
Adapted 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.

Results:
Clinico-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.

Conclusions:
We 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.

Keywords:
Multimarker models Bayesian statistical learning method Bayesian regression Bayesian LASSO AUC-ROC Determination coefficient heritability Bladder cancer outcome Prognosis Recurrence Progression Genome-wide common SNP Illumina Infinium HumanHap 1 M array Predictive ability

 

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