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, F. X. Real, M. E. Goddard, N. Malats, M. Kogevinas, N. Malats, M. Sala, G. Castaño, M. ToràD. Puente, C. Villanueva, C. Murta-Nascimento, J. Fortuny, E. López, S. Hernández, R. Jaramillo, G. Vellalta, L. Palencia, F. Fermández, A. Amorós, A. Alfaro, G. Carretero, S. Serrano, L. Ferrer, A. Gelabert, J. Carles, O. Bielsa, K. Villadiego, L. Cecchini, J. M. Saladié, L. Ibarz, M. Céspedes, C. Serra, D. García, J. Pujadas, R. Hernando, A. Cabezuelo, C. Abad, A. Prera, J. Prat, M. Domènech, J. Badal, J. Malet, R. García-Closas, J. Rodríguez de Vera, A. I. Martín, J. Taño, F. Cáceres, A. Carrato, F. García-López, M. Ull, A. Teruel, E. Andrada, A. Bustos, A. Castillejo, J. L. Soto, A. Tardón, J. L. Guate, J. M. Lanzas, J. Velasco, J. M. Fernández, J. J. Rodríguez, A. Herrero, R. Abascal, C. Manzano, T. Miralles, M. Rivas, M. Arguelles, M. Díaz, J. Sánchez, O. González, A. Mateos, V. Frade, P. Muntañola, C. Pravia, A. M. Huescar, F. Huergo, J. Mosquera

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8 Citations (Scopus)


© 2016 de Maturana et al. 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 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 (ĥ2) 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.
Original languageEnglish
Article number351
JournalBMC Cancer
Issue number1
Publication statusPublished - 3 Jun 2016


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


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