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Título:	Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models.
Autor/a:	López De Maturana, Evangelina; Picornell, Antoni; Masson-Lecomte, Alexandra; Kogevinas, Manolis; Márquez, Mirari; Carrato, Alfredo; Tardón, Adonina; Lloreta Trull, Josep, 1958-; García Closas, Montserrat; Silverman, Debra T.; Rothman, Nathaniel; Chanock, Stephen J.; Real, Francisco X.; Goddard, M. E.; Malats i Riera, Núria; SBC/EPICURO Study Investigators
Abstract:	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.
Abstract:	The work was partially supported by Red Temática de Investigación Cooperativa en Cáncer (#RD12/0036/0050), Fondo de Investigaciones Sanitarias (FIS), Instituto de Salud Carlos III, (Grant numbers #PI00–0745, #PI05–1436, and #PI06–1614), and Asociación Española Contra el Cáncer (AECC), Spain; the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA (Contract NCI NO2-CP-11015); and EU-FP7-HEALTH-F2–2008–201663-UROMOL and EU-7FP-HEALTH-TransBioBC #601933. ELM was funded by a Sara Borrell fellowship, Instituto de Salud Carlos III, Spain; and AML by a fellowship of the European Urological Scholarship Program for Research (EUSP Scholarship S-01–2013).
Materia(s):	-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
Derechos:	https://creativecommons.org/licenses/by/4.0/ © 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.
Tipo de documento:	Artículo Artículo - Versión publicada
Editor:	BioMed Central
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