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).
CONCLUSION: 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.