Abstract
BackgroundWe 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.MethodsAdapted 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.ResultsClinico-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.ConclusionsWe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2361-7) contains supplementary material, which is available to authorized users.
Highlights
We adapted Bayesian statistical learning strategies to the prognosis field to investigate if genome-wide common single nucleotide polymorphism (SNP) improve the prediction ability of clinico-pathological prognosticators and applied it to non-muscle invasive bladder cancer (NMIBC) patients
Time to first recurrence 33 % of the patients with a primary NMIBC suffered a recurrence of the primary tumor
Our results showed that common genome-wide SNPs though poorly, classified patients regarding both to first recurrence (TFR) and to progression (TP) in the whole series and in the high risk (HiR) and low risk” (LR) subcohorts, Area under the ROC curve (AUC) ranging from 0.55 to 0.58
Summary
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. Urothelial bladder cancer (UBC) is among the most common malignant tumors of the urological system and one of the most prevalent cancers due to its chronic nature [1]. As a consequence, it poses an enormous burden on health care systems [2]. 75 % of newly diagnosed UBCs do not invade the muscle (non-muscle invasive bladder cancer, NMIBC) at the time of diagnosis. Most of these cancers remain stable over the time after a transurethral resection (TUR); a high proportion relapse without invading the muscle (recurrence) while a lower proportion progress as a muscle invasive bladder cancer (MIBC). Mainly stage and grade, NMIBC are subsequently
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