Introduction: Integration of genetics and epigenetics has emerged as a powerful approach to studying cellular differentiation (Mikkelsen et al, 2009) and tumorigenesis (Shen et al, 2007). The study of DNA methylation is of particular importance in cancer, as causal involvement has been demonstrated and it is the most stable of all epigenetic modifications, making it a desirable marker for both early detection and treatment of tumors. Hypermethylation of CpG sites in gene promoter regions leads to decreased gene expression; if such a gene is a tumor suppressor, this leads to carcinogenesis. To date, there have been no studies of genome-wide DNA methylation profiling of NETs. This study sets out to determine the DNA methylation profiles of low, intermediate and high grade pancreatic NET liver metastases with the intention of identifying dysregulated biological pathways in the development of these tumors. A protocol for the analysis formalin-fixed paraffin embedded tissue (FFPE) has also been developed in order to study these tumors in significant numbers following this pilot study.
Aim(s): To perform genome-wide DNA methylation anaylsis on 10 fresh frozen pancreatic neuroendocrine tumors of low, intermediate and high grade tumors in order to determine whether these tumors have distinct DNA methylation profiles, and the development of a protocol to analyse formalin-fixed paraffin embedded tissue on the Infinium HumMeth27 array platform in order to study a significant number if tumors.
Materials and methods: Ten fresh frozen sporadic pancreatic NET liver tumors (three low grade, three intermediate grade and four high grade) were analysed using the Illumina HumMeth27 beadarray (which interrogates 27,500 genome-wide CpG sites relating to promoter regions of 14,000 genes and 100 micro-RNAs). DNA was extracted from fresh frozen tumors and 1µg of DNA bisulphite converted following standard protocols before running on the HumMeth27 array. Data analysis: Following inter- and intra-array normalization, features are identified correlating with phenotypes of interest using logistic regression analysis.
Conference: 7th Annual ENETS Conference (2010)
Presenting Author: Dr Christina Thirlwell
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