title: Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles.
abstract: Whole-genome multiomic profiles hold valuable information for the analysis and prediction of disease risk and progression. However, integrating high-dimensional multilayer omic data into risk-assessment models is statistically and computationally challenging. We describe a statistical framework, the Bayesian generalized additive model ((BGAM), and present software for integrating multilayer high-dimensional inputs into risk-assessment models. We used BGAM and data from The Cancer Genome Atlas for the analysis and prediction of survival after diagnosis of breast cancer. We developed a sequence of studies to (1) compare predictions based on single omics with those based on clinical covariates commonly used for the assessment of breast cancer patients (COV), (2) evaluate the benefits of combining COV and omics, (3) compare models based on (a) COV and gene expression profiles from oncogenes with (b) COV and whole-genome gene expression (WGGE) profiles, and (4) evaluate the impacts of combining multiple omics and their interactions. We report that (1) WGGE profiles and whole-genome methylation (METH) profiles offer more predictive power than any of the COV commonly used in clinical practice (e.g., subtype and stage), (2) adding WGGE or METH profiles to COV increases prediction accuracy, (3) the predictive power of WGGE profiles is considerably higher than that based on expression from large-effect oncogenes, and (4) the gain in prediction accuracy when combining multiple omics is consistent. Our results show the feasibility of omic integration and highlight the importance of WGGE and METH profiles in breast cancer, achieving gains of up to 7 points area under the curve (AUC) over the COV in some cases.
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824 avazquez@msu.edu.
authors.forename: Ana I
authors.initials: AI
authors.lastname: Vazquez
authors.affiliation: Biostatistics Department, University of Alabama at Birmingham, Alabama 35294.
authors.forename: Yogasudha
authors.initials: Y
authors.lastname: Veturi
authors.affiliation: Comprehensive Cancer Center, University of Alabama at Birmingham, Alabama 35294 Department of Epidemiology, University of Alabama at Birmingham, Alabama 35294.
authors.forename: Michael
authors.initials: M
authors.lastname: Behring
authors.affiliation: Department of Epidemiology, University of Alabama at Birmingham, Alabama 35294.
authors.forename: Sadeep
authors.initials: S
authors.lastname: Shrestha
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611 University of Florida Genetics Institute, University of Florida, Gainesville, Florida 32611.
authors.forename: Matias
authors.initials: M
authors.lastname: Kirst
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611 University of Florida Genetics Institute, University of Florida, Gainesville, Florida 32611.
authors.forename: Marcio F R
authors.initials: MF
authors.lastname: Resende
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824 Statistics Department, Michigan State University, East Lansing, Michigan 48824.
authors.forename: Gustavo
authors.initials: G
authors.lastname: de Los Campos
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology; D019175:DNA Methylation / Q000235:genetics*; D018450:Disease Progression; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D008957:Models, Genetic; D009363:Neoplasm Proteins / Q000096:biosynthesis* / Q000235:genetics; D040901:Proteomics; D012984:Software; D016019:Survival Analysis
chemical_list: D009363:Neoplasm Proteins
keywords: GenPred; Shared data resource; diseases risk; genomic selection; omics integration; prediction of complex traits