We examine the relationship of the expression levels of selected genes from their location in 3D space. We better define the function of selected genomic regions that are important in the context of personalized medicine by i) determining the statistical significance of the observed number of copies of genomic regions in selected cohorts of patients ii) evaluating their uniqueness comparing the observed changes with typical and natural genomic diversity iii) inferring the biological function of these genomic regions using publicly available databases iv) identifying unique local 3D environment for selected sites, eg. regulatory ones v) analyzing the impact of structural re-arrangements of those local neighborhoods on the gene expression profiles.