title: A metadata approach for clinical data management in translational genomics studies in breast cancer.
abstract: BACKGROUND
  In molecular profiling studies of cancer patients, experimental and clinical data are combined in order to understand the clinical heterogeneity of the disease: clinical information for each subject needs to be linked to tumour samples, macromolecules extracted, and experimental results. This may involve the integration of clinical data sets from several different sources: these data sets may employ different data definitions and some may be incomplete.
  
  
  METHODS
  In this work we employ semantic web techniques developed within the CancerGrid project, in particular the use of metadata elements and logic-based inference to annotate heterogeneous clinical information, integrate and query it.
  
  
  RESULTS
  We show how this integration can be achieved automatically, following the declaration of appropriate metadata elements for each clinical data set; we demonstrate the practicality of this approach through application to experimental results and clinical data from five hospitals in the UK and Canada, undertaken as part of the METABRIC project (Molecular Taxonomy of Breast Cancer International Consortium).
  
  
  CONCLUSIONS
  We describe a metadata approach for managing similarities and differences in clinical datasets in a standardized way that uses Common Data Elements (CDEs). We apply and evaluate the approach by integrating the five different clinical datasets of METABRIC.
authors: Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge CB20RE, UK. ivp21@cam.ac.uk
authors: Irene
authors: I
authors: Papatheodorou
authors: Charles
authors: C
authors: Crichton
authors: Lorna
authors: L
authors: Morris
authors: Peter
authors: P
authors: Maccallum
authors: Jim
authors: J
authors: Davies
authors: James D
authors: JD
authors: Brenton
authors: Carlos
authors: C
authors: Caldas
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D019295:Computational Biology / Q000379:methods; D003628:Database Management Systems / Q000592:standards*; D005260:Female; D023281:Genomics / Q000379:methods*; D006801:Humans