Microarray Technology to Profile CpG Island Methylation

描述（由申请人提供）： 该研究项目的长期目标是适应基于微阵列的 技术来测量人类癌细胞中的CpG岛甲基化。CPG 岛是具有高CG含量的大约1 kb的DNA片段， 在二核苷酸5 '-CG-3'中富集，发现在约 50%的人类基因，并参与转录调控， 这些基因。CpG岛的CpG二核苷酸中的胞嘧啶是 在正常组织中未甲基化;然而，CpG岛变得异常， 在肿瘤发生过程中甲基化，并已被链接到转录 抑制相关基因。此外，从有限数量的CpG 迄今为止，已经分析了岛屿和肿瘤，似乎模式 异常甲基化以肿瘤特异性和阶段特异性的方式发生， 这表明CpG岛甲基化谱可能是有用的肿瘤- 监测疾病活动和负担的特定指纹。因此 其中数千个CpG的胞嘧啶甲基化状态 岛可以同时确定将是有用的分子 分析人类肿瘤，并可能提供生物学的见解， 癌症。为此，我们组建了一个多学科团队， CpG岛微阵列（CGI阵列）作为确定CpG岛的工具 癌症中的甲基化谱，并从这些谱中识别 CpG岛甲基化的特征性模式与 肿瘤的临床表型。以下四个综合具体目标是 旨在实现我们的目标。 1)构建CpG岛基因芯片用于CpG岛甲基化研究 分析. 2)使用CpG岛微阵列优化甲基化分析 3)确定AML细胞系和AML中的CpG岛甲基化特征 从具有已知临床结果的患者中获得的样本。 4)开发和实施一个公共数据库，以传播和挖掘 CGI数组数据

DESCRIPTION (provided by applicant): The long-term objective of this research project is to adapt microarray-based technology to measure CpG island methylation in human cancer cells. CpG islands are approximately 1kb stretches of DNA that have a high CG content, are enriched in the dinucleotide 5'-CG -3', are found at the 5' end of about 50% of all human genes, and participate in the transcriptional regulation of these genes. The cytosines in the CpG dinucleotides of CpG islands are unmethylated in normal tissue; however, CpG islands become aberrantly methylated during oncogenesis and has been linked to the transcriptional repression of the associated gene. In addition, from the limited number of CpG islands and tumors that have been analyzed to date, it appears that patterns of aberrant methylation occur in a tumor-specific and stage-specific fashion, suggesting that CpG island methylation profiles may be useful as a tumor- specific fingerprint to monitor disease activity and burden. Thus, a multiplexed assay where the cytosine methylation status of thousands of CpG islands can be determined simultaneously would be useful in the molecular profiling of human tumors, and will likely provide insights into the biology of cancer. To this end we have formed a multidisciplinary team to use human CpG island microarrays (CGI arrays) as a tool for determining CpG island methylation profiles in cancer, and from these profiles identify characteristic patterns of CpG island methylation that correlate with the tumor's clinical phenotype. The 4 integrated specific aims that follow are designed to reach our objective. 1) Construct CpG island microarrays for use in CpG island methylation analysis. 2) Optimize methylation analysis using CpG island microarrays 3) Determine CpG island methylation signatures in AML cell lines and in AML samples obtained from patients with known clinical outcome. 4) Develop and implement a public database for the dissemination and mining of the CGI array data