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

BMC Cancer

Research Article

M. E. Goddard¹  M. Kogevinas²  A. Tardón³  N. Rothman⁴  D. Silverman⁴  S. Chanock⁴  M. García-Closas⁵  F. X. Real⁶  N. Malats⁷  A. Masson-Lecomte⁷  A. Picornell⁷  E. López de Maturana⁷  M. Márquez⁷  J. Lloreta⁸  A. Carrato⁹ 

[1] Biosciences Research Division, Department of Environment and Primary Industries, Agribio, and Department of Food and Agricultural Systems, University of Melbourne, Melbourne, Australia;Centre for Research in Environmental Epidemiology (CREAL), Parc de Salut Mar, Barcelona, Spain;CIBERESP, Madrid, Spain;Department of Preventive Medicine Universidad de Oviedo, Oviedo, Spain;CIBERESP, Madrid, Spain;Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland, USA;Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK;Epithelial Carcinogenesis Group, Spanish National Cancer Research Centre (CNIO), Madrid, and Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain;Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, 3, 28029, Almagro, Madrid, Spain;Parc de Salut Mar and Departament of Pathology, Hospital del Mar - IMAS, Barcelona, Spain;Servicio de Oncología, Hospital Universitario Ramon y Cajal, Madrid, and Servicio de Oncología, Hospital Universitario de Elche, Elche, Spain;

关键词: 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;   

DOI  :  10.1186/s12885-016-2361-7

 received in 2015-11-03, accepted in 2016-05-12,  发布年份 2016

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© de Maturana et al. 2016