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4 results on '"SBC/EPICURO Study Investigators"'

2. Inflammatory-Related Genetic Variants in Non-Muscle-Invasive Bladder Cancer Prognosis: A Multimarker Bayesian Assessment

Author

Masson-Lecomte A, López de Maturana E, Goddard ME, Picornell A, Rava M, González-Neira A, Márquez M, Carrato A, Tardon A, Lloreta J, Garcia-Closas M, Silverman D, Rothman N, Kogevinas M, Allory Y, Chanock SJ, Real FX, Malats N, and SBC/EPICURO Study Investigators

Abstract

Increasing evidence points to the role of tumor immunologic environment on urothelial bladder cancer prognosis. This effect might be partly dependent on the host genetic context. We evaluated the association of SNPs in inflammation-related genes with non-muscle-invasive bladder cancer (NMIBC) risk-of-recurrence and risk-of-progression.

Published
2016

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

Author

de Maturana, E. López, 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. X., Goddard, M. E., Malats, N., López de Maturana, E, and SBC/EPICURO Study Investigators

Subjects
BLADDER tumors, CANCER relapse, GENETIC polymorphisms, PHARMACOKINETICS, PROBABILITY theory, PROGNOSIS, RESEARCH funding, PREDICTIVE tests, RECEIVER operating characteristic curves, DISEASE progression, TRANSITIONAL cell carcinoma, GENOTYPES
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. [ABSTRACT FROM AUTHOR]

Published
2016
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4. Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models

Author

López De Maturana, Evangelina, Picornell, Antoni, Masson-Lecomte, Alexandra, Kogevinas, Manolis, Márquez, Mirari, Carrato, Alfredo, Tardón, Adonina, Lloreta Trull, Josep, 1958, García Closas, Montserrat, Silverman, Debra T., Rothman, Nathaniel, Chanock, Stephen J., Real, Francisco X., Goddard, M. E., Malats i Riera, Núria, and SBC/EPICURO Study Investigators

Subjects
Male, Oncology, Cancer Research, 030232 urology & nephrology, Multimarker models, heritability, Predictive ability, Bioinformatics, 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, Bayes' theorem, 0302 clinical medicine, Lasso (statistics), Recurrence, Aged, 80 and over, Illumina Infinium HumanHap 1 M array, Progression, Genome-wide common SNP, Middle Aged, Prognosis, 3. Good health, Bayesian regression, Area Under Curve, 030220 oncology & carcinogenesis, Predictive value of tests, Censoring (clinical trials), Disease Progression, Bayesian linear regression, Research Article, Bayesian LASSO, Adult, medicine.medical_specialty, Genotype, Bladder cancer outcome, Polymorphism, Single Nucleotide, Sensitivity and Specificity, Heritability, 03 medical and health sciences, Predictive Value of Tests, Internal medicine, Biomarkers, Tumor, Genetics, medicine, Humans, SNP, Determination coefficient, Aged, Carcinoma, Transitional Cell, Bladder cancer, Bayesian statistical learning method, business.industry, AUC-ROC, Bayes Theorem, medicine.disease, ROC Curve, Urinary Bladder Neoplasms, Neoplasm Recurrence, Local, business
Abstract

The work was partially supported by Red Temática de Investigación Cooperativa en Cáncer (RD12/0036/0050), Fondo de Investigaciones Sanitarias (FIS), Instituto de Salud Carlos III, (Grant numbers PI00–0745, PI05–1436, and PI06–1614), and Asociación Española Contra el Cáncer (AECC), Spain; the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, USA (Contract NCI NO2-CP-11015); and EU-FP7-HEALTH-F2–2008–201663-UROMOL and EU-7FP-HEALTH-TransBioBC 601933. ELM was funded by a Sara Borrell fellowship, Instituto de Salud Carlos III, Spain; and AML by a fellowship of the European Urological Scholarship Program for Research (EUSP Scholarship S-01–2013)., 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.X., Goddard, M.E., Malats, N., Kogevinas, M., Malats, N., Sala, M., Castaño, G., Torà, M., Puente, D., Villanueva, C., Murta-Nascimento, C., Fortuny, J., López, E., Hernández, S., Jaramillo, R., Vellalta, G., Palencia, L., Fermández, F., Amorós, A., Alfaro, A., Carretero, G., Serrano, S., Ferrer, L., Gelabert, A., Carles, J., Bielsa, O., Villadiego, K., Cecchini, L., Saladié, J.M., Ibarz, L., Céspedes, M., Serra, C., García, D., Pujadas, J., Hernando, R., Cabezuelo, A., Abad, C., Prera, A., Prat, J., Domènech, M., Badal, J., Malet, J., García-Closas, R., Rodríguez de Vera, J., Martín, A.I., Taño, J., Cáceres, F., Carrato, A., García-López, F., Ull, M., Teruel, A., Andrada, E., Bustos, A., Castillejo, A., Soto, J.L., Tardón, A., Guate, J.L., Lanzas, J.M., Velasco, J., Fernández, J.M., Rodríguez, J.J., Herrero, A., Abascal, R., Manzano, C., Miralles, T., Rivas, M., Arguelles, M., Díaz, M., Sánchez, J., González, O., Mateos, A., Frade, V., Muntañola, P., Pravia, C., Huescar, A.M., Huergo, F., Mosquera, J.

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