Analysis of diffuse large B cell lymphomas (DLBCL) transcriptional profiles at chromosomal level

Introduction: Transcriptional profiling of whole genomes using cDNA or oligonucleotide high-density arrays is becoming increasingly popular among the biomedical research community. The efficient exploitation of gene expression databases requires not only computational tools for management, analysis, and functional annotation of primary data, but also integrating lists of modulated genes with of other sources of genomic information, such as gene sequence, locus or structural characteristics. In particular, integration between expression profiles and chromosomal localizations could be effective in detecting gene structural abnormalities such as genomic gains and losses and/or translocations. The aim of the present study is to apply bioinformatic tools for mapping transcriptional data at chromosomal level and detecting clusters of regionally modulated genes in DLBCL lymphoma datasets.Methods: A computational procedure has been designed to integrate expression profiles and gene sequence information for the identification of chromosomal regions with altered gene expression. The method is based on the application of a smoothing, coordinate-dependent function (e.g., cubic splines) to a standard transcriptional specificity statistic (e.g., standard F-statistic), commonly used to detect differentially expressed genes. Results: The analysis at chromosomal level of DLBCL transcriptional data presented by Shipp et al (Shipp et al., 2002) and Klein et al (Klein et al., 2001) allowed the detection of regional signals corresponding to known as well as putative loci with high frequent genomic losses and gains or marking translocation events: 1q, 3p, 6p, 9q, 11p, 1q, 12q, 17p, 17q, 18q, 19q. Data validated by real-time PCR on DLBCL samples will be presented Regions will also be correlated with survival data.Conclusions: The proposed analysis could help to identify genomic regions involved in the pathogenesis of DLBCL.