TY - JOUR
T1 - Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data
AU - Klijn, Christiaan
AU - Holstege, Henne
AU - de Ridder, Jeroen
AU - Liu, Xiaoling
AU - Reinders, Marcel
AU - Jonkers, Jos
AU - Wessels, Lodewyk
PY - 2008
Y1 - 2008
N2 - Tumor formation is in part driven by DNA copy number alterations (CNAs), which can be measured using microarray-based Comparative Genomic Hybridization (aCGH). Multiexperiment analysis of aCGH data from tumors allows discovery of recurrent CNAs that are potentially causal to cancer development. Until now, multiexperiment aCGH data analysis has been dependent on discretization of measurement data to a gain, loss or no-change state. Valuable biological information is lost when a heterogeneous system such as a solid tumor is reduced to these states. We have developed a new approach which inputs nondiscretized aCGH data to identify regions that are significantly aberrant across an entire tumor set. Our method is based on kernel regression and accounts for the strength of a probe's signal, its local genomic environment and the signal distribution across multiple tumors. In an analysis of 89 human breast tumors, our method showed enrichment for known cancer genes in the detected regions and identified aberrations that are strongly associated with breast cancer subtypes and clinical parameters. Furthermore, we identified 18 recurrent aberrant regions in a new dataset of 19 p53-deficient mouse mammary tumors. These regions, combined with gene expression microarray data, point to known cancer genes and novel candidate cancer genes. © 2008 The Author(s).
AB - Tumor formation is in part driven by DNA copy number alterations (CNAs), which can be measured using microarray-based Comparative Genomic Hybridization (aCGH). Multiexperiment analysis of aCGH data from tumors allows discovery of recurrent CNAs that are potentially causal to cancer development. Until now, multiexperiment aCGH data analysis has been dependent on discretization of measurement data to a gain, loss or no-change state. Valuable biological information is lost when a heterogeneous system such as a solid tumor is reduced to these states. We have developed a new approach which inputs nondiscretized aCGH data to identify regions that are significantly aberrant across an entire tumor set. Our method is based on kernel regression and accounts for the strength of a probe's signal, its local genomic environment and the signal distribution across multiple tumors. In an analysis of 89 human breast tumors, our method showed enrichment for known cancer genes in the detected regions and identified aberrations that are strongly associated with breast cancer subtypes and clinical parameters. Furthermore, we identified 18 recurrent aberrant regions in a new dataset of 19 p53-deficient mouse mammary tumors. These regions, combined with gene expression microarray data, point to known cancer genes and novel candidate cancer genes. © 2008 The Author(s).
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=39149107114&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/18187509
U2 - https://doi.org/10.1093/nar/gkm1143
DO - https://doi.org/10.1093/nar/gkm1143
M3 - Article
C2 - 18187509
SN - 0305-1048
VL - 36
JO - Nucleic Acids Research
JF - Nucleic Acids Research
IS - 2
M1 - e13
ER -