| Neuroblastoma is a pediatric cancer of the developing nervous system that commonly affects young children, and has a very high mortality rate. The etiology of initiation and transformation of this malignancy is still incompletely understood despite recent advances in neuroblastoma research. Early studies of tumor biology have enabled the utilization of somatically acquired genetic and genomic alterations in tumor classification and treatment stratification. Later research of familial neuroblastoma, which comprises of 1% of neuroblastoma, has yielded robust understanding of the biology of these hereditary cases, and has resulted in several translational research achievements such as genetic testing and new therapies. Recent research has focused on understanding the genomic and genetic landscape of sporadic neuroblastoma through utilizing a breadth of computational prowess to understand massive high-throughput genomic data alongside with traditional cellular and molecular investigation techniques. This thesis is an integral part of this ongoing effort that combines, bridges, and balances the aspects of computational analysis and biological experiments. Here, we present a statistical method in the field of genome wide association studies (GWAS) with an emphasis on integrating association signals at the gene level. This method is beneficial in the way that it focuses on protein coding genes with the aim of making prediction for immediate molecular follow-up studies. We also present another novel statistical method that searches the genome for other interacting partners with the genes already identified by the previous method. Sharing the same philosophy, the interaction method also aims at identifying genes and haplotype blocks as interacting partners. In the last part of this thesis, we present a cellular and molecular study of one of the genes identified by our genome wide association method, designed to define this gene's roles in neuroblastoma cells. |
PDF Full Text Request