Journal article
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, N Rothman, S Chanock, FX Real, ME Goddard, N Malats, M Sala, G Castaño, M Torà, D Puente, C Villanueva Show all
BMC Cancer | BMC | Published : 2016
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 >10years. 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 dete..
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Awarded by European Commission
Funding Acknowledgements
The work was partially supported by Red Tematica de Investigacion Cooperativa en Cancer (#RD12/0036/0050), Fondo de Investigaciones Sanitarias (FIS), Instituto de Salud Carlos III, (Grant numbers #PI00-0745, #PI05-1436, and #PI06-1614), and Asociacion Espanola Contra el Cancer (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).