Integrated analysis of DNA copy number and gene expression microarray data using gene sets

We propose to analyse copy number and expression array data using gene sets, rather than individual genes. The proposed model is robust and sensitive. We re-analysed two publicly available datasets as illustration. These two independent breast cancer datasets yielded similar patterns of association between gene dosage and gene expression levels, in spite of different platforms having been used. Our comparisons show a clear advantage to using sets of genes' expressions to detect associations with long-spanning, low-amplitude copy number aberrations. In addition, our model allows for using additional explanatory variables and does not require mapping between copy number and expression probes.We developed a general and flexible tool for integration of multiple microarray data sets, and showed how the identification of genes whose expression is affected by copy number aberrations provides a powerful approach to prioritize putative targets for functional validation.Tumor cells accumulate genetic damage, including changes in DNA copy number, sequence and methylation, resulting in the dysfunctioning of key regulators [1]. The advent of microarray technology has allowed genome-wide monitoring of these molecular changes at the DNA and RNA level. Gene expression profiling has facilitated classification of cancers into biologically and clinically distinct categories [2-7]. High-resolution array-based comparative genomic hybridization (array-CGH) has allowed the delineation of recurrent DNA copy number alterations in tumors [8-10]. Gene dosage changes play an important role in tumor development; oncogenes may be enhanced by DNA amplification and tumor suppressor genes may be inactivated by a physical deletion. Therefore, integrated analysis of both copy number and gene expression microarray data could give additional information about the role of copy number alterations in the development of cancer.Combined analysis of DNA copy number and gene expression microarrays of the same