DNA Methylation and Ovarian Cancer

DNA 甲基化与卵巢癌

• 批准号: 6545450
• 负责人: Kenneth P Nephew
• 金额: $ 30.8万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6545450
• 关键词: CpG islands; DNA methylation; biomarker; biotechnology; clinical research; diagnosis design /evaluation; diagnostic tests; female; gene induction /repression; genetic screening; human tissue; microarray technology; molecular oncology; neoplasm /cancer diagnosis; neoplasm /cancer genetics; neoplastic cell; ovary neoplasms; prognosis; women's health

Ovarian cancer has the highest mortality rates of female cancers. The underlying biology of this disease is poorly understood and the existing approaches for diagnosis and prognosis are inadequate. Based on the hypothesis that methylation changes at CpG islands play a prominent role in cancer, we developed a microarray-based technique called differential methylation hybridization (DMH). DMH allows for the global analysis of CpG island methylation in tumor genomes, a method to determine methylation profiles of cancer cells, and has the potential use as a site specific diagnostic approach to test whether demethylation at specific loci can be identified. Previously, we used DMH on a small group of patients to perform methylation profiling of ovarian cancer, establish proof-of- concept and lay the foundation for genome wide screening of methylation to examine epigenotype-phenotype relationships in ovarian cancer. In the present study, we will perform methylation profiling of ovarian cancer using DMH arrays containing 21,000 CpG islands. Methylation profiles of ovarian tumors from patients diagnosed with early and advanced ovarian cancer will be compared to normal ovarian surface epithelium. Overall methylation patterns will be used as an "epigenetic signature" to characterize specific types and stages of ovarian cancer, and these molecular signatures will be correlated with clinicopathological parameters of the patients. Specific methylation patterns identified can later be applied to predict patients' outcome in clinical settings. The resulting array data will be used to identify the specific CpG island sequences frequently hypermethylated in ovarian cancer. As these are not normally methylated in adult tissues, CpG island methylation represents one of the most prevalent tumor specific markers yet identified. When associated with specific genes, CpG island methylation may have consequences for ovarian tumor types. In addition, we have further refined DMH by using expressed CpG island sequence tags (ECISTs) for dual detection of CpG hypermethylation and gene expression/silencing in cancer cells. ECISTs exist in the genome, and their GC-rich fragments can be used to screen aberrantly methylated CpG sites in cancer cells. The exon-containing portions can be employed to measure levels of gene expression simultaneously. Using an ECIST panel we have recently developed, we will identify hypermethylated loci and at the same time confirm their association with gene silencing in the ovarian cancer samples. This approach will also allow us to study gene promoter activity in ovarian cancer and assess the importance of screening for gene promoter functions in this disease. In summary, this study will address the clear need for developing better tools for the screening and staging of ovarian cancer, as well as the need to identify new markers that adequately address the complexity of this disease. Methylation profiling of ovarian tumors could provide a more focused test for reactivation of methylation-silenced genes as therapeutic targets and thus play a role in the rational basis for new clinical strategies designed to alter this fundamental process in ovarian cancer.

卵巢癌是女性癌症中死亡率最高的。 人们对这种疾病的潜在生物学知之甚少，现有的诊断和预后方法也不足。 基于 CpG 岛甲基化变化在癌症中发挥重要作用的假设，我们开发了一种基于微阵列的技术，称为差异甲基化杂交 (DMH)。 DMH 允许对肿瘤基因组中的 CpG 岛甲基化进行全局分析，这是一种确定癌细胞甲基化谱的方法，并且有可能用作位点特异性诊断方法来测试是否可以识别特定位点的去甲基化。此前，我们使用DMH对一小群患者进行卵巢癌甲基化分析，建立概念验证，并为全基因组甲基化筛查奠定基础，以检查卵巢癌的表观基因型-表型关系。 在本研究中，我们将使用包含 21,000 个 CpG 岛的 DMH 阵列对卵巢癌进行甲基化分析。 将诊断为早期和晚期卵巢癌的患者的卵巢肿瘤的甲基化谱与正常卵巢表面上皮进行比较。总体甲基化模式将用作“表观遗传特征”来表征卵巢癌的特定类型和阶段，并且这些分子特征将与患者的临床病理参数相关。 确定的特定甲基化模式随后可用于预测临床环境中患者的结果。 由此产生的阵列数据将用于识别卵巢癌中经常高度甲基化的特定 CpG 岛序列。 由于这些在成人组织中通常不会甲基化，因此 CpG 岛甲基化代表了迄今为止已发现的最普遍的肿瘤特异性标记物之一。 当与特定基因相关时，CpG 岛甲基化可能会对卵巢肿瘤类型产生影响。 此外，我们还通过使用表达的 CpG 岛序列标签 (ECIST) 来双重检测癌细胞中的 CpG 高甲基化和基因表达/沉默，进一步完善了 DMH。 ECIST 存在于基因组中，其富含 GC 的片段可用于筛选癌细胞中异常甲基化的 CpG 位点。含有外显子的部分可用于同时测量基因表达水平。 使用我们最近开发的 ECIST panel，我们将识别高甲基化位点，同时确认它们与卵巢癌样本中基因沉默的关联。 这种方法还将使我们能够研究卵巢癌中基因启动子的活性，并评估筛选该疾病中基因启动子功能的重要性。 总之，这项研究将解决开发更好的卵巢癌筛查和分期工具的明确需求，以及确定能够充分解决这种疾病复杂性的新标记物的需求。 卵巢肿瘤的甲基化分析可以为作为治疗靶点的甲基化沉默基因的重新激活提供更有针对性的测试，从而为旨在改变卵巢癌这一基本过程的新临床策略的合理基础发挥作用。