A new look towards BAC-based array CGH through a comprehensive comparison with oligo-based array CGH
1 Laboratoire de Bioinformatique et de Génomique lntégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire,1, rue Laurent Fries, BP 10142, 67404 Illkirch CEDEX, France
2 Uro-Oncology Research Group, Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK
3 Medical Biotechnology, VTT Technical Research Centre of Finland and University of Turku, FIN-20520 Turku, Finland
4 Cancer Research UK Uro-Oncology Research Group, Department of Oncology, University of Cambridge, Hutchison/Medical Research Council Cancer Research Centre, Cambridge CB2 2XZ, England, UK
5 Human Pathology, Institut de Génétique et de Biologie Moléculaire et Cellulaire,1, rue Laurent Fries, BP 10142, 67404 Illkirch CEDEX, France
6 Department of Urology (G4-105.1), Academic Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
BMC Genomics 2007, 8:84 doi:10.1186/1471-2164-8-84Published: 29 March 2007
Currently, two main technologies are used for screening of DNA copy number; the BAC (Bacterial Artificial Chromosome) and the recently developed oligonucleotide-based CGH (Chromosomal Comparative Genomic Hybridization) arrays which are capable of detecting small genomic regions with amplification or deletion. The correlation as well as the discriminative power of these platforms has never been compared statistically on a significant set of human patient samples.
In this paper, we present an exhaustive comparison between the two CGH platforms, undertaken at two independent sites using the same batch of DNA from 19 advanced prostate cancers. The comparison was performed directly on the raw data and a significant correlation was found between the two platforms. The correlation was greatly improved when the data were averaged over large chromosomic regions using a segmentation algorithm. In addition, this analysis has enabled the development of a statistical model to discriminate BAC outliers that might indicate microevents. These microevents were validated by the oligo platform results.
This article presents a genome-wide statistical validation of the oligo array platform on a large set of patient samples and demonstrates statistically its superiority over the BAC platform for the Identification of chromosomic events. Taking advantage of a large set of human samples treated by the two technologies, a statistical model has been developed to show that the BAC platform could also detect microevents.