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This article is part of the supplement: Proceedings of the International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-meeting 2011)

Open Access Research

Combined analysis of genome-wide expression and copy number profiles to identify key altered genomic regions in cancer

Celia Fontanillo1, Sara Aibar1, Jose Manuel Sanchez-Santos2 and Javier De Las Rivas1*

Author Affiliations

1 Cancer Research Center (CIC-IBMCC), Consejo Superior de Investigaciones Científicas (CSIC), Campus Miguel de Unamuno, Salamanca, Spain

2 Department of Statistics, University of Salamanca (USAL), Salamanca, Spain

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BMC Genomics 2012, 13(Suppl 5):S5  doi:10.1186/1471-2164-13-S5-S5

Published: 19 October 2012

Abstract

Background

Analysis of DNA copy number alterations and gene expression changes in human samples have been used to find potential target genes in complex diseases. Recent studies have combined these two types of data using different strategies, but focusing on finding gene-based relationships. However, it has been proposed that these data can be used to identify key genomic regions, which may enclose causal genes under the assumption that disease-associated gene expression changes are caused by genomic alterations.

Results

Following this proposal, we undertake a new integrative analysis of genome-wide expression and copy number datasets. The analysis is based on the combined location of both types of signals along the genome. Our approach takes into account the genomic location in the copy number (CN) analysis and also in the gene expression (GE) analysis. To achieve this we apply a segmentation algorithm to both types of data using paired samples. Then, we perform a correlation analysis and a frequency analysis of the gene loci in the segmented CN regions and the segmented GE regions; selecting in both cases the statistically significant loci. In this way, we find CN alterations that show strong correspondence with GE changes. We applied our method to a human dataset of 64 Glioblastoma Multiforme samples finding key loci and hotspots that correspond to major alterations previously described for this type of tumors.

Conclusions

Identification of key altered genomic loci constitutes a first step to find the genes that drive the alteration in a malignant state. These driver genes can be found in regions that show high correlation in copy number alterations and expression changes.