Prediction of chromosomal aneuploidy from gene expression data Export

@article{citeulike:909997, abstract = {Chromosomal aneuploidy is commonly observed in neoplastic diseases and is an important prognostic marker. Here we examine how gene expression profiles reflect aneuploidy and whether these profiles can be used to detect changes in chromosome copy number. We developed two methods for detecting such changes in the gene expression profile of a single sample. The first method, fold-change analysis, relies on the availability of gene expression data from a large cohort of patients with the same disease. The expression profile of the sample is compared with that of the dataset. The second method, chromosomal relative expression analysis, is more general and requires the expression data from the tested sample only. We found that the relative expression values are stable among different chromosomes and exhibit little variation between different normal tissues. We exploited this novel finding to establish the set of reference values needed to detect changes in the copy number of chromosomes in a single sample on the basis of gene expression levels. We measured the accuracy of the performance of each method by applying them to two independent leukemia datasets. The second method was also applied to two solid tumor datasets. We conclude that chromosomal aneuploidy can be detected and predicted by analysis of gene expression profiles. This article contains Supplementary Material available at . {\copyright} 2006 Wiley-Liss, Inc.}, address = {Department of Pediatric Hemato-Oncology, The Sheba Cancer Research Center, Sheba Medical Center, Tel Hashomer, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Oncology, University Children's Hospital, CH-8032 Z\"{u}rich, Switzerland; Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN; Department of Pediatrics, Robert Wood Johnson Medical School, New Brunswick, NJ; Department of Molecular Genetics, Robert Wood Johnson Medical School, New Brunswick, NJ}, author = {Hertzberg, Libi and Betts, David R. and Raimondi, Susana C. and Sch\"{a}fer, Beat W. and Notterman, Daniel A. and Domany, Eytan and Izraeli, Shai}, citeulike-article-id = {909997}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/gcc.20391}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17044051}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17044051}, citeulike-linkout-3 = {http://www3.interscience.wiley.com/cgi-bin/abstract/113399493/ABSTRACT}, doi = {10.1002/gcc.20391}, journal = {Genes, Chromosomes and Cancer}, keywords = {aneuploidy, expression, gene, microarray}, number = {9999}, pages = {NA+}, posted-at = {2008-11-21 11:31:38}, priority = {2}, title = {Prediction of chromosomal aneuploidy from gene expression data}, url = {http://dx.doi.org/10.1002/gcc.20391}, volume = {9999}, year = {2006} }

TY - JOUR ID - citeulike:909997 L3 - citeulike-article-id:909997 N2 - Chromosomal aneuploidy is commonly observed in neoplastic diseases and is an important prognostic marker. Here we examine how gene expression profiles reflect aneuploidy and whether these profiles can be used to detect changes in chromosome copy number. We developed two methods for detecting such changes in the gene expression profile of a single sample. The first method, fold-change analysis, relies on the availability of gene expression data from a large cohort of patients with the same disease. The expression profile of the sample is compared with that of the dataset. The second method, chromosomal relative expression analysis, is more general and requires the expression data from the tested sample only. We found that the relative expression values are stable among different chromosomes and exhibit little variation between different normal tissues. We exploited this novel finding to establish the set of reference values needed to detect changes in the copy number of chromosomes in a single sample on the basis of gene expression levels. We measured the accuracy of the performance of each method by applying them to two independent leukemia datasets. The second method was also applied to two solid tumor datasets. We conclude that chromosomal aneuploidy can be detected and predicted by analysis of gene expression profiles. This article contains Supplementary Material available at . © 2006 Wiley-Liss, Inc. IS - 9999 JF - Genes, Chromosomes and Cancer AD - Department of Pediatric Hemato-Oncology, The Sheba Cancer Research Center, Sheba Medical Center, Tel Hashomer, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Oncology, University Children's Hospital, CH-8032 Zürich, Switzerland; Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN; Department of Pediatrics, Robert Wood Johnson Medical School, New Brunswick, NJ; Department of Molecular Genetics, Robert Wood Johnson Medical School, New Brunswick, NJ TI - Prediction of chromosomal aneuploidy from gene expression data VL - 9999 SP - NA KW - aneuploidy KW - expression KW - gene KW - microarray AU - Hertzberg, Libi AU - Betts, David AU - Raimondi, Susana AU - Schäfer, Beat AU - Notterman, Daniel AU - Domany, Eytan AU - Izraeli, Shai PY - 2006/// UR - http://dx.doi.org/10.1002/gcc.20391 ER -