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Abstract

Recurrent genomic amplications and deletions characterize cancer genomes and contribute to disease evolution. Array Comparative Genomic Hybridization (aCGH) technology allows detection of chromosomal copy number aberrations in the genomic DNA of tumors with high resolution. The association of consistent copy number aberrations with particular types of cancer facilitates the understanding of the pathogenesis of the disease, and contributes towards the improvement of diagnosis, prognosis and the development of drugs. However, distinguishing aberrations that are relevant to cancer from random background aberrations is a dicult task, due to the high dimensionality of the aCGH data. Different statistical methods have been developed to identify non-random gains and losses across multiple samples. Their approaches vary in several aspects: requirements necessary for an aberration to be recurrent, preprocessing of the input data, statistical approaches used for assessing signicance of a recurrent aberration and other biological considerations they use. So far, multiple-sample analysis methods have only been evaluated qualitatively and their relative merits remain unknown. In this work we propose an approach for quantitative evaluation of the performance of four selected methods. We use simulated data with known aberrations to validate each method and we interpret the different outcomes. We also compare the performance of the methods on a collection of neuroblastoma tumors by quantifying the agreement between methods. We select appropriate techniques to combine the outputs of the methods into a meaningful aggregation in order to obtain a high condence lists of signicant copy number aberrations.