title: Stratifying tumour subtypes based on copy number alteration profiles using next-generation sequence data.
abstract: BACKGROUND
  The role of personalized medicine and target treatment in the clinical management of cancer patients has become increasingly important in recent years. This has made the task of precise histological substratification of cancers crucial. Increasingly, genomic data are being seen as a valuable classifier. Specifically, copy number alteration (CNA) profiles generated by next-generation sequencing (NGS) can become a determinant for tumours subtyping. The principle purpose of this study is to devise a model with good prediction capability for the tumours histological subtypes as a function of both the patients covariates and their genome-wide CNA profiles from NGS data.
  
  
  RESULTS
  We investigate a logistic regression for modelling tumour histological subtypes as a function of the patients' covariates and their CNA profiles, in a mixed model framework. The covariates, such as age and gender, are considered as fixed predictors and the genome-wide CNA profiles are considered as random predictors. We illustrate the application of this model in lung and oral cancer datasets, and the results indicate that the tumour histological subtypes can be modelled with a good fit. Our cross-validation indicates that the logistic regression exhibits the best prediction relative to other classification methods we considered in this study. The model also exhibits the best agreement in the prediction between smooth-segmented and circular binary-segmented CNA profiles.
  
  
  METHODS
  An R package to run a logistic regression is available in http://www1.maths.leeds.ac.uk/~arief/R/CNALR/.
  
  
  BACKGROUND
  a.gusnanto@leeds.ac.uk
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, University of Leeds, Leeds, LS2 9JT, UK.
authors.forename: Arief
authors.initials: A
authors.lastname: Gusnanto
authors.affiliation: Department of Thoracic Surgery, St. James Hospital, Leeds, LS9 7TF, UK.
authors.forename: Peter
authors.initials: P
authors.lastname: Tcherveniakov
authors.affiliation: Department of Statistics, University of Leeds, Leeds, LS2 9JT, UK.
authors.forename: Farag
authors.initials: F
authors.lastname: Shuweihdi
authors.affiliation: Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, UK and King Fahad Medical City, Riyadh, Saudi Arabia.
authors.forename: Manar
authors.initials: M
authors.lastname: Samman
authors.affiliation: Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, UK and.
authors.forename: Pamela
authors.initials: P
authors.lastname: Rabbitts
authors.affiliation: Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, LS9 7TF, UK and.
authors.forename: Henry M
authors.initials: HM
authors.lastname: Wood
mesh_terms: D056915:DNA Copy Number Variations / Q000235:genetics*; D030541:Databases, Genetic; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000235:genetics; D008954:Models, Biological; D009062:Mouth Neoplasms / Q000235:genetics; D009369:Neoplasms / Q000145:classification* / Q000235:genetics*; D015203:Reproducibility of Results