Gene Expression in Cancer Cells : Detection of Splice Variants, Allele-specific Expression and DNA Methylation

Abstract: The human genome sequencing project has provided a wealth of information on sequence variation between individuals. The surprisingly low number of genes in the human genome is compensated for by a complex regulation of gene expression. New methods are now being developed for the discovery and analysis of the regulatory regions of the genome to elucidate factors that affect both normal and disease-associated human genetic variation. In parallel with identification of DNA sequence variation, efforts are being made to unravel the next layer of information - epigenetic modifications of the genome. The studies in this thesis describe the application of methods for genotyping single nucleotide polymorphisms (SNPs) in DNA for the analysis of gene transcripts in cancer cells. We performed quantitative analysis of splice variants and screened for allele-specific gene expression (ASE) in cancer cells using the tag-microarray based minisequencing system. This analysis revealed transcript isoforms that were differentially spliced in leukemia cell lines and normal endothelial cell lines. We detected wide-spread allele-specific gene expression in cancer cells that were sensitive or resistant to anti-cancer drugs. In regulatory regions of the genes with ASE we identified putative regulatory SNPs. Using technology developed for large-scale SNP genotyping, we screened for ASE in an internationally unique collection of childhood acute lymphoblastic leukemia (ALL) samples. Analysis of DNA methylation in promoter regions of genes displaying ASE revealed genes, whose expression is regulated by allele-specific DNA methylation. For a subset of these genes we found a correlation between DNA methylation levels and probability of disease-free survival in ALL patients with different chromosomal aberrations. The methylation patterns that we identified constitute excellent candidate markers for subtyping of ALL patients and for stratification of ALL patients based on their probability of disease-free survival and response to drug treatment. The results of this study have increased our understanding of epigenetic changes in ALL cells and will hopefully help to design better treatment plans for the patients to avoid over-treatment and unnecessary side effects.