Massively Parallel Sequencing Technology for Cancer Epigenome.

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DESCRIPTION (provided by applicant): Methylated CpG island amplification (MCA) is a DNA library construction technique that permits amplification of 146,148 methylated CpG rich regions throughout the whole genome, covering 76% of all genes and 70% of all bona fide CpG islands. When coupled with microarrays, this technique has proven to be highly specific and very useful for the high-throughput analysis of genome-wide methylation in normal and cancer cells. Problems such as non-uniform probe performance, cross-reactivity, difficulties of normalization, and issues of relevant controls, however, prevent the full quantitative potential of MCA from being realized. We propose to develop and validate a high-resolution tool for DNA methylation profiling by coupling MCA with 'next generation' Solexa 1G sequencing technology (MCA-Seq). During the R21 phase of the project, we will apply various strategies to optimize MCA-Seq to improve coverage and minimize the quantity of initial DNA required. Additionally, we will build quality controls for MCA-Seq and develop optimized algorithms for data analysis. We will then validate our optimized MCA-Seq protocols, and evaluate the sensitivity, specificity, and quantitative accuracy using an innovative approach that simulates biological variation in methylation. Our proposed research will result in the development of a simple, robust, and reliable genome-wide assay for DNA methylation which will have broad utility in cancer research. Our long term goal is to utilize this technology to test biological hypotheses. Therefore, in an R33 phase of this project, we plan to fully implement this emerging technology by applying MCA-Seq in a set of well characterized tumor cell lines and primary cancer patient samples. These future studies will further validate the ability of MCA-Seq to accurately profile highly variable and aberrant cancer epigenomes, and generate preliminary biological data. The development and validation of MCA-Seq will enable major advances in our understanding of the basic biological mechanisms that contribute to cancer, and likely contribute to the design of future cancer therapies.