Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: López de Maturana, E.; Picornell, A.; Masson-Lecomte, A.; Kogevinas, M.; Márquez, M.; Carrato, A.; Tardón, A.; Lloreta, J.; García-Closas, M.; Silverman, D.; Rothman, N.; Chanock, S.; Real, F.; Goddard, M.; Malats, N.
• Is Part Of: BMC cancer. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-12
• Subjects: Multimarker models -- Bayesian statistical learning method -- Bayesian regression -- Bayesian LASSO -- AUC-ROC -- Determination coefficient -- heritability -- Bladder cancer outcome -- Prognosis -- Recurrence -- Progression -- Genome-wide common SNP -- Illumina Infinium HumanHap 1 M array -- Predictive ability; Cancer -- Periodicals; 616.994005
• DOI: 10.1186/s12885-016-2361-7
• Languages: English
• ISSNs: 1471-2407

Abstract

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.