Overlay analysis of the oligonucleotide array gene expression profiles and copy number abnormalities as determined by array comparative genomic hybridization in medulloblastomas

Abstract

Combined analysis of gene expression array data and array-based comparative genomic hybridization data have been used in a series of 26 pediatric brain tumors to define up- and downregulated genes that coincide with losses, gains, and amplifications involving specific chromosome regions. Frequent losses were defined in chromosome arms 3q, 6q, 8p, 10q, 16q, 17p, and gains were identified in chromosome 7, and chromosome arms 9p and 17q. Amplification of a 2p region was seen in only one tumor, which corresponded to increased expression of the MYCN and DDX1 genes. To facilitate the analysis of the two data sets, we have developed a custom overlay tool that defines genes that are underexpressed in regions of deletions and overexpressed in regions of gain, across the genome and specifically within regions showing recurrent involvement in medulloblastomas.

Gain of 1q Is a Potential Univariate Negative Prognostic Marker for Survival in Medulloblastoma

“…The number of patients in many of these studies, however, was still relatively small, and the technique used to identify chromosome 17 abnormalities may have been suboptimal. In our series, the aCGH profiles unequivocally identify 17p -/17q + (16,22,28). However, despite 22% of tumors in this series showing involvement of chromosome 17, no statistically significant correlation between these abnormalities and overall survival was detected.…”

Candidate glioblastoma development gene identification using concordance between copy number abnormalities and gene expression level changes

“…This approach provides a more focused analysis which is less affected by random changes between tumors. We have recently developed a software tool which can perform this association between expression and CNAs on a genome-wide level (Lo et al, 2007a) and, when applied to a survey of gliomas, we have readily identified potential candidate genes for involvement in tumor development. As shown in frequent amplification events, not only are previously implicated genes over expressed, but in many cases the adjacent genes are equally, and often more prominently, over expressed as we described previously for amplicons involving the EGFR locus, for example (Rossi et al, 2005a).…”

“…The characterization of the aCGH karyotypes described above provided a detailed survey of chromosome losses, gains and amplifications in the current series of GBM, and provided the substrate for the superimposition of the expression data using the RPCI Overlay Tool developed by Lo et al (2007a). Gene expression levels were established using the U133Plus2.0 arrays with RNA from the same 30 tumor samples used for the aCGH analysis.…”

“…In this way we are assuming that all tumors are derived from a similar stem cell and that amplification is responsible for the increased expression of driver genes. The Overlay Tool (Lo et al, 2007a) generates categorized data by combining results from aCGH with gene expression changes measured using probe set summary differences between tumors and controls from the Affymetrix software. Using the comparison with normal astrocytes as a control, the expression levels of specific genes are designated as either increased, no change or decreased, based on a static threshold of a log 2 ratio of 1.5.…”

“…Although we have demonstrated that it is possible to correlate gene expression changes in these tumors with specific events on the short arm of chromosome 7 associated with high level amplification (Rossi et al, 2005a), it was possible at that time to perform only highly specific correlations between copy number abnormalities (CNA) and gene expression, since a systematic way of overlaying the data sets was not available. Since then, we have developed an overlay tool which makes it possible to computationally combine data sets from the various different platforms and efficiently correlate losses defined using circular binary segmentation analysis (CBS), with up and downregulation of genes derived from the Affymetrix oligonucleotide expression arrays (Lo et al, 2007a). Using this approach we have characterized the changes in expression of genes located within regions of the genome that show CNAs to define specific events that occur in GBM.…”