TY - GEN
T1 - Framework for identifying common aberrations in DNA copy number data
AU - Ben-Dor, Amir
AU - Lipson, Doron
AU - Tsalenko, Anya
AU - Reimers, Mark
AU - Baumbusch, Lars Oliver
AU - Barrett, Michael T.
AU - Weinstein, John N.
AU - Borresen-Dale, Anne-Lise
AU - Yakhini, Zohar
PY - 2007
Y1 - 2007
N2 - High-resolution array comparative genomic hybridization (aCGH) provides exon-level mapping of DNA aberrations in cells or tissues. Such aberrations are central to carcinogenesis and, in many cases, central to targeted therapy of the cancers. Some of the aberrations are sporadic, one-of-a-kind changes in particular tumor samples; others occur frequently and reflect common themes in cancer biology that have interpretable, causal ramifications. Hence, the difficult task of identifying and mapping common, overlapping genomic aberrations (including amplifications and deletions) across a sample set is an important one; it can provide insight for the discovery of oncogenes, tumor suppressors, and the mechanisms by which they drive cancer development. In this paper we present an efficient computational framework for identification and statistical characterization of genomic aberrations that are common to multiple cancer samples in a CGH data set. We present and compare three different algorithmic approaches within the context of that framework. Finally, we apply our methods to two datasets - a collection of 20 breast cancer samples and a panel of 60 diverse human tumor cell lines (the NCI-60). Those analyses identified both known and novel common aberrations containing cancer-related genes. The potential impact of the analytical methods is well demonstrated by new insights into the patterns of deletion of CDKN2A (p16), a tumor suppressor gene crucial for the genesis of many types of cancer.
AB - High-resolution array comparative genomic hybridization (aCGH) provides exon-level mapping of DNA aberrations in cells or tissues. Such aberrations are central to carcinogenesis and, in many cases, central to targeted therapy of the cancers. Some of the aberrations are sporadic, one-of-a-kind changes in particular tumor samples; others occur frequently and reflect common themes in cancer biology that have interpretable, causal ramifications. Hence, the difficult task of identifying and mapping common, overlapping genomic aberrations (including amplifications and deletions) across a sample set is an important one; it can provide insight for the discovery of oncogenes, tumor suppressors, and the mechanisms by which they drive cancer development. In this paper we present an efficient computational framework for identification and statistical characterization of genomic aberrations that are common to multiple cancer samples in a CGH data set. We present and compare three different algorithmic approaches within the context of that framework. Finally, we apply our methods to two datasets - a collection of 20 breast cancer samples and a panel of 60 diverse human tumor cell lines (the NCI-60). Those analyses identified both known and novel common aberrations containing cancer-related genes. The potential impact of the analytical methods is well demonstrated by new insights into the patterns of deletion of CDKN2A (p16), a tumor suppressor gene crucial for the genesis of many types of cancer.
KW - Breast cancer
KW - CGH
KW - Cancer
KW - Common aberrations
KW - Microarray data analysis
KW - NCI-60
UR - http://www.scopus.com/inward/record.url?scp=34547408267&partnerID=8YFLogxK
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U2 - 10.1007/978-3-540-71681-5_9
DO - 10.1007/978-3-540-71681-5_9
M3 - Conference contribution
AN - SCOPUS:34547408267
SN - 3540716807
SN - 9783540716808
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 122
EP - 136
BT - Research in Computational Molecular Biology - 11th Annual International Conference, RECOMB 2007, Proceedings
PB - Springer Verlag
T2 - 11th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2007
Y2 - 21 April 2007 through 25 April 2007
ER -