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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.
