Authors:
K Jong, E Marchiori, A van der Vaart, S-F Chin, B Carvalho, M Tijssen, PP Eijk, P van den Ijssel, H Grabsch, P Quirke, JJ Oudejans, GA Meijer, C Caldas, B Ylstra
Journal name: 
Oncogene
Citation info: 
26(10):1499-1506
Abstract: 
A series of studies have been published that evaluate the chromosomal copy number changes of different tumor classes using array comparative genomic hybridization (array CGH); however, the chromosomal aberrations that distinguish the different tumor classes have not been fully characterized. Therefore, we performed a meta-analysis of different array CGH data sets in an attempt to classify samples tested across different platforms. As opposed to RNA expression, a common reference is used in dual channel CGH arrays: normal human DNA, theoretically facilitating cross-platform analysis. To this aim, cell line and primary cancer data sets from three different dual channel array CGH platforms obtained by four different institutes were integrated. The cell line data were used to develop preprocessing methods, which performed noise reduction and transformed samples into a common format. The transformed array CGH profiles allowed perfect clustering by cell line, but importantly not by platform or institute. The same preprocessing procedures used for the cell line data were applied to data from 373 primary tumors profiled by array CGH, including controls. Results indicated that there is no apparent feature related to the institute or platform and that array CGH allows for unambiguous cross-platform meta-analysis. Major clusters with common tissue origin were identified. Interestingly, tumors of hematopoietic and mesenchymal origins cluster separately from tumors of epithelial origin. Therefore, it can be concluded that chromosomal aberrations of tumors from hematopoietic and mesenchymal origin versus tumors of epithelial origin are distinct, and these differences can be picked up by meta-analysis of array CGH data. This suggests the possibility of prospectively using combined analysis of diverse copy number data sets for cancer subtype classification.
DOI: 
http://doi.org/10.1038/sj.onc.1209919
Research group: 
Caldas Group
E-pub date: 
01 Mar 2007