Identification of discrete chromosomal deletion by binary recursive partitioning of microarray differential expression data
- 1Laboratory of Head and Neck Cancer Research, Dental Research Institute, School of Dentistry, University of California at Los Angeles, Los Angeles, CA, USA
- 2Division of Hematology-Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
- 3Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA, USA
- 4Molecular Biology Institute, University of California at Los Angeles, Los Angeles, CA, USA
- 5Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
- 6Department of Human Genetics, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
- Correspondence to: Dr D Wong UCLA School of Dentistry, PO Box 951668, Los Angeles, CA 90095–1668, USA; dtwwucla.edu
- Received 15 July 2004
- Accepted 10 September 2004
- Revised 15 July 2004
Abstract
DNA copy number abnormalities (CNA) are characteristic of tumours, and are also found in association with congenital anomalies and mental retardation. The ultimate impact of copy number abnormalities is manifested by the altered expression of the encoded genes. We previously developed a statistical method for the detection of simple chromosomal amplification using microarray expression data. In this study, we significantly advanced those analytical techniques to allow detection of localised chromosomal deletions based on differential gene expression data. Using three cell lines with known chromosomal deletions as model system, mRNA expression in those cells was compared with that observed in diploid cell lines of matched tissue origin. Results show that genes from deleted chromosomal regions are substantially over-represented (p<0.000001 by χ2) among genes identified as underexpressed in deletion cell lines relative to normal matching cells. Using a likelihood based statistical model, we were able to identify the breakpoint of the chromosomal deletion and match with the karyotype data in each cell line. In one such cell line, our analyses refined a previously identified 10p chromosomal deletion region. The deletion region was mapped to between 10p14 and 10p12, which was further confirmed by subtelomeric fluorescence in situ hybridisation. These data show that microarray differential expression data can be used to detect and map the boundaries of submicroscopic chromosomal deletions.
- CGH, comparative genomic hybridisation
- CNA, copy number abnormality
- FISH, fluorescence in situ hybridisation
- LOH, loss of heterozygosity
- binary recursive partitioning
- chromosomal deletion
- copy-number abnormalities
- differential mRNA expression
- microarray expression analysis
Footnotes
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Competing interests: none declared
