Title |
Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models
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Published in |
BMC Cancer, June 2016
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DOI | 10.1186/s12885-016-2361-7 |
Pubmed ID | |
Authors |
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, On behalf of the SBC/EPICURO Study Investigators |
Abstract |
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. 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. 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. 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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Portugal | 1 | 3% |
Unknown | 37 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 10 | 26% |
Student > Master | 7 | 18% |
Student > Ph. D. Student | 6 | 16% |
Student > Doctoral Student | 2 | 5% |
Other | 2 | 5% |
Other | 6 | 16% |
Unknown | 5 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 11 | 29% |
Biochemistry, Genetics and Molecular Biology | 6 | 16% |
Agricultural and Biological Sciences | 4 | 11% |
Nursing and Health Professions | 2 | 5% |
Social Sciences | 2 | 5% |
Other | 5 | 13% |
Unknown | 8 | 21% |