Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models.
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.
Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain.
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López de Maturana
Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain.
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Picornell
Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain.
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Masson-Lecomte
Centre for Research in Environmental Epidemiology (CREAL), Parc de Salut Mar, Barcelona, Spain.|CIBERESP, Madrid, Spain.
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M
Kogevinas
Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain.
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M
Márquez
Servicio de Oncología, Hospital Universitario Ramon y Cajal, Madrid, and Servicio de Oncología, Hospital Universitario de Elche, Elche, Spain.
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Carrato
Department of Preventive Medicine Universidad de Oviedo, Oviedo, Spain.|CIBERESP, Madrid, Spain.
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Tardón
Parc de Salut Mar and Departament of Pathology, Hospital del Mar - IMAS, Barcelona, Spain.
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J
Lloreta
Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK.
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García-Closas
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland, USA.
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Silverman
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland, USA.
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Rothman
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services, Bethesda, Maryland, USA.
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Chanock
Epithelial Carcinogenesis Group, Spanish National Cancer Research Centre (CNIO), Madrid, and Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.
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Real
Biosciences Research Division, Department of Environment and Primary Industries, Agribio, and Department of Food and Agricultural Systems, University of Melbourne, Melbourne, Australia.
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Goddard
Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernández, Almagro, 3, 28029, Madrid, Spain. nmalats@cnio.es.
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Malats
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000032:analysis; D002295:Carcinoma, Transitional Cell / Q000235:genetics / Q000473:pathology*; D018450:Disease Progression; D005838:Genotype; D006801:Humans; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000235:genetics / Q000473:pathology; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012680:Sensitivity and Specificity; D001749:Urinary Bladder Neoplasms / Q000235:genetics / Q000473:pathology*
D014408:Biomarkers, Tumor
AUC-ROC; Bayesian LASSO; Bayesian regression; Bayesian statistical learning method; Bladder cancer outcome; Determination coefficient; Genome-wide common SNP; Illumina Infinium HumanHap 1 M array; Multimarker models; Predictive ability; Prognosis; Progression; Recurrence; heritability