López De Maturana, EvangelinaPicornell, AntoniMasson-Lecomte, AlexandraKogevinas, ManolisMárquez, MirariCarrato, AlfredoTardón, AdoninaLloreta, Josep, 1958-García Closas, MontserratSilverman, Debra T.Rothman, NathanielChanock, Stephen J.Real, Francisco X.Goddard, M. E.Malats i Riera, NúriaSBC/EPICURO Study Investigators2016-06-292016-06-292016López de Maturana E, Picornell A, Masson-Lecomte A, Kogevinas M, Márquez M, Carrato A. et al. Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models. BMC Cancer. 2016 Jun 3;16(1):351. doi: 10.1186/s12885-016-2361-71471-2407http://hdl.handle.net/10230/26986BACKGROUND: 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.application/pdfeng© 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.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s12885-016-2361-7Multimarker modelsBayesian statistical learning methodBayesian regressionBayesian LASSOAUC-ROCDetermination coefficientHeritabilityBladder cancer outcomePrognosisRecurrenceProgressionGenome-wide common SNPIllumina Infinium HumanHap 1 M arrayPredictive abilityinfo:eu-repo/semantics/openAccess