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

López De Maturana, Evangelina; Picornell, Antoni; Masson-Lecomte, Alexandra; Kogevinas, Manolis; Márquez, Mirari; Carrato, Alfredo; Tardón, Adonina; Lloreta, 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

doi:http://dx.doi.org/10.1186/s12885-016-2361-7

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

Citation

López 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/s12885-016-2361-7

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.