Aberrant DNA methylation frequently occurs in CpG islands, which are 0.2 to 2-kb GC-rich sequences located in the 5 ends of approximately 60 percent of all genes, in neoplasia. This type of epigenetic mutation is associated with the silencing of tumor suppressor genes, and plays an important role in promoting tumor development. Until recently, most methylation assays have been limited to analyzing a few CpG islands of known genes at a time, and suffer limited throughput for a genome-based study and for clinical applications. Therefore, the development of more efficient technology designed to detect CpG island hypermethylation has long been needed to dissect complex methylation changes during cancer development. With this in mind, in our exploratory phase (equivalent to R21) we have developed a novel DNA array-based technique, called differential methylation hybridization (DMH), that provides for the first time an opportunity to conduct a genome-based methylation analysis. The first part of this innovation is the generation of multiple CpG island tags as templates arrayed onto solid supports (e.g., nylon membranes). The second part involves preparation of amplicons, representing a pool of methylated DNA from the tumor and reference (control) genomes. Amplicons are used as probes in array-hybridization. Positive signals identified by the tumor amplicon, but not by the reference amplicon, indicate the presence of hypermethylated CpG island loci in cancer cells. We successfully applied DMH to identify multiple hypermethylated sequences in breast tumors. In this R33 application, we propose to upgrade DMH by reconfiguring it into a microarray-based assay and to develop an advanced information system in support of this high-throughput methylation analysis. The critical features of this full-scale development are 1) generation of a standard panel of approximately 15,000 CpG island tags broadly applicable in methylation analysis of solid tumors and leukemias, 2) improvement of amplicon generation from multiple clinical specimens, 3) implementation of sophisticated instruments (DNA arrayer and imaging device) for microarray assays, and 4) development of a robust data warehouse and computational tools for analyzing large-scale methylation data. We will catalog methylation changes of CpG islands in the clinical samples proposed for study, and the methylation data will be used to correlate with clinicopathological parameters of the patients. This type of analysis will provide a new tool for molecular staging of cancer. It is expected that high-throughput DMH will have a broad application in detecting DNA methylation changes in cancer. Moreover, the characterization of the corresponding biological relevant cDNAs will open unforeseen avenues for investigating the pathology of cancer and for better treatment of the disease.
