title: Robust selection of cancer survival signatures from high-throughput genomic data using two-fold subsampling.
abstract: Identifying relevant signatures for clinical patient outcome is a fundamental task in high-throughput studies. Signatures, composed of features such as mRNAs, miRNAs, SNPs or other molecular variables, are often non-overlapping, even though they have been identified from similar experiments considering samples with the same type of disease. The lack of a consensus is mostly due to the fact that sample sizes are far smaller than the numbers of candidate features to be considered, and therefore signature selection suffers from large variation. We propose a robust signature selection method that enhances the selection stability of penalized regression algorithms for predicting survival risk. Our method is based on an aggregation of multiple, possibly unstable, signatures obtained with the preconditioned lasso algorithm applied to random (internal) subsamples of a given cohort data, where the aggregated signature is shrunken by a simple thresholding strategy. The resulting method, RS-PL, is conceptually simple and easy to apply, relying on parameters automatically tuned by cross validation. Robust signature selection using RS-PL operates within an (external) subsampling framework to estimate the selection probabilities of features in multiple trials of RS-PL. These probabilities are used for identifying reliable features to be included in a signature. Our method was evaluated on microarray data sets from neuroblastoma, lung adenocarcinoma, and breast cancer patients, extracting robust and relevant signatures for predicting survival risk. Signatures obtained by our method achieved high prediction performance and robustness, consistently over the three data sets. Genes with high selection probability in our robust signatures have been reported as cancer-relevant. The ordering of predictor coefficients associated with signatures was well-preserved across multiple trials of RS-PL, demonstrating the capability of our method for identifying a transferable consensus signature. The software is available as an R package rsig at CRAN (http://cran.r-project.org).
authors.affiliation: Department of Computer Sciences, TU Dortmund University, Dortmund, Germany.
authors.forename: Sangkyun
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Statistics, TU Dortmund University, Dortmund, Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Rahnenführer
authors.affiliation: Department of Statistics, TU Dortmund University, Dortmund, Germany.
authors.forename: Michel
authors.initials: M
authors.lastname: Lang
authors.affiliation: Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium.
authors.forename: Katleen
authors.initials: K
authors.lastname: De Preter
authors.affiliation: Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium.
authors.forename: Pieter
authors.initials: P
authors.lastname: Mestdagh
authors.affiliation: Department of Oncogenomics, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: Koster
authors.affiliation: Department of Oncogenomics, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Rogier
authors.initials: R
authors.lastname: Versteeg
authors.affiliation: Cancer Genetics, Royal College of Surgeons, Dublin, Ireland.
authors.forename: Raymond L
authors.initials: RL
authors.lastname: Stallings
authors.affiliation: Laboratory of Molecular Biology, Giannina Gaslini Institute, Genova, Italy.
authors.forename: Luigi
authors.initials: L
authors.lastname: Varesio
authors.affiliation: Hematology/Oncology, Children's Hospital Los Angeles, Los Angeles, California, United States of America.
authors.forename: Shahab
authors.initials: S
authors.lastname: Asgharzadeh
authors.affiliation: Department of Pediatric Oncology and Hematology, University Children's Hospital Essen, Essen, Germany; Centre for Medical Biotechnology, University Duisburg-Essen, Essen, Germany; Translational Neuro-Oncology, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany.
authors.forename: Johannes H
authors.initials: JH
authors.lastname: Schulte
authors.affiliation: Department of Pediatric Oncology and Hematology, University Children's Hospital Essen, Essen, Germany.
authors.forename: Kathrin
authors.initials: K
authors.lastname: Fielitz
authors.affiliation: Department of Pediatric Oncology and Hematology, University Children's Hospital Essen, Essen, Germany.
authors.forename: Melanie
authors.initials: M
authors.lastname: Schwermer
authors.affiliation: Department of Computer Sciences, TU Dortmund University, Dortmund, Germany.
authors.forename: Katharina
authors.initials: K
authors.lastname: Morik
authors.affiliation: Department of Pediatric Oncology and Hematology, University Children's Hospital Essen, Essen, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Schramm
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000401:mortality; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000401:mortality*; D009447:Neuroblastoma / Q000401:mortality; D016016:Proportional Hazards Models