title: Chromosomal patterns of gene expression from microarray data: methodology, validation and clinical relevance in gliomas.
abstract: BACKGROUND
  Expression microarrays represent a powerful technique for the simultaneous investigation of thousands of genes. The evidence that genes are not randomly distributed in the genome and that their coordinated expression depends on their position on chromosomes has highlighted the need for mathematical approaches to exploit this dependency for the analysis of expression data-sets.
  
  
  RESULTS
  We have devised a novel mathematical technique (CHROMOWAVE) based on the Haar wavelet transform and applied it to a dataset obtained with the Affymetrix HG-U133_Plus_2 array in 27 gliomas. CHROMOWAVE generated multi-chromosomal pattern featuring low expression in chromosomes 1p, 4, 9q, 13, 18, and 19q. This pattern was not only statistically robust but also clinically relevant as it was predictive of favourable outcome. This finding was replicated on a data-set independently acquired by another laboratory. FISH analysis indicated that monosomy 1p and 19q was a frequent feature of tumours displaying the CHROMOWAVE pattern but that allelic loss on chromosomes 4, 9q, 13 and 18 was much less common.
  
  
  CONCLUSIONS
  The ability to detect expression changes of spatially related genes and to map their position on chromosomes makes CHROMOWAVE a valuable screening method for the identification and display of regional gene expression changes of clinical relevance. In this study, FISH data showed that monosomy was frequently associated with diffuse low gene expression on chromosome 1p and 19q but not on chromosomes 4, 9q, 13 and 18. Comparative genomic hybridisation, allelic polymorphism analysis and methylation studies are in progress in order to identify the various mechanisms involved in this multi-chromosomal expression pattern.
authors.affiliation: Department of Clinical Neuroscience, Division of Neuroscience, Imperial College London, UK. federico.turkheimer@imperial.ac.uk
authors.forename: Federico E
authors.initials: FE
authors.lastname: Turkheimer
authors.forename: Federico
authors.initials: F
authors.lastname: Roncaroli
authors.forename: Benoit
authors.initials: B
authors.lastname: Hennuy
authors.forename: Christian
authors.initials: C
authors.lastname: Herens
authors.forename: Minh
authors.initials: M
authors.lastname: Nguyen
authors.forename: Didier
authors.initials: D
authors.lastname: Martin
authors.forename: Annick
authors.initials: A
authors.lastname: Evrard
authors.forename: Vincent
authors.initials: V
authors.lastname: Bours
authors.forename: Jacques
authors.initials: J
authors.lastname: Boniver
authors.forename: Manuel
authors.initials: M
authors.lastname: Deprez
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D001932:Brain Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D002874:Chromosome Mapping / Q000379:methods*; D002877:Chromosomes, Human / Q000235:genetics*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005910:Glioma / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins