Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models
Web of Science
AuthorLopez de Maturana, E; Picornell, A; Masson-Lecomte, A; Kogevinas, M; Marquez, M; Carrato, A; Tardon, A; Lloreta, J; Garcia-Closas, M; Silverman, D; ...
Source TitleBMC Cancer
University of Melbourne Author/sGoddard, Michael
AffiliationAgriculture and Food Systems
Document TypeJournal Article
CitationsLopez de Maturana, E., Picornell, A., Masson-Lecomte, A., Kogevinas, M., Marquez, M., Carrato, A., Tardon, A., Lloreta, J., Garcia-Closas, M., Silverman, D., Rothman, N., Chanock, S., Real, F. X., Goddard, M. E. & Malats, N. (2016). Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models. BMC CANCER, 16 (1), https://doi.org/10.1186/s12885-016-2361-7.
Access StatusOpen Access
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.
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