Massively Parallel Sequencing Technology for Cancer Epigenome.

癌症表观基因组大规模并行测序技术。

• 批准号: 7796888
• 负责人: Lanlan Shen
• 金额: $ 17.34万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7796888
• 关键词: Aberrant DNA Methylation; Address; Algorithms; Biological; Biological Assay; Biological Testing; Cancer Detection; Cancer Patient; Cancer Prognosis; Cancer cell line; Colon; Coupled; Coupling; CpG Islands; DNA; DNA Library; DNA Methylation; Data; Data Analyses; Development; Emerging Technologies; Environmental Exposure; Epigenetic Process; Future; Gene Mutation; Gene Silencing; Genes; Genome; Goals; Hematologic Neoplasms; Human; Libraries; Liver; Malignant Neoplasms; Measures; Methods; Methylation; National Cancer Institute; Neoplasms; Patients; Performance; Phase; Primary Neoplasm; Protocols documentation; Quality Control; Research; Resolution; Sampling; Sensitivity and Specificity; Simulate; Specificity; Specimen; Techniques; Technology; Time; Tumor Cell Line; Validation; Variant; anticancer research; cancer cell; cancer therapy; cost; cross reactivity; design; drug discovery; genome-wide; high throughput analysis; improved; innovation; insight; next generation; prevent; promoter; response; single molecule; tool; tumor; tumorigenesis

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.

描述（由申请人提供）：甲基化CpG岛扩增（MCA）是一种DNA文库构建技术，允许扩增整个基因组中146，148个甲基化CpG富集区，覆盖所有基因的76%和所有真正CpG岛的70%。当与微阵列结合时，该技术已被证明是高度特异性的，并且对于正常和癌细胞中全基因组甲基化的高通量分析非常有用。然而，诸如不均匀的探针性能、交叉反应性、标准化的困难和相关控制的问题等问题阻碍了MCA的全部定量潜力的实现。我们建议通过将MCA与“下一代”Solexa 1G测序技术（MCA-Seq）相结合，开发和验证用于DNA甲基化分析的高分辨率工具。在项目的R21阶段，我们将应用各种策略来优化MCA-Seq，以提高覆盖率并最大限度地减少所需的初始DNA数量。此外，我们将建立MCA-Seq的质量控制，并开发优化的数据分析算法。然后，我们将验证我们优化的MCA-Seq方案，并使用模拟甲基化生物变异的创新方法评估灵敏度，特异性和定量准确性。我们提出的研究将导致开发一种简单，稳健和可靠的全基因组DNA甲基化检测方法，这将在癌症研究中具有广泛的实用性。我们的长期目标是利用这项技术来测试生物学假设。因此，在该项目的R33阶段，我们计划通过在一组充分表征的肿瘤细胞系和原发性癌症患者样本中应用MCA-Seq来全面实施这项新兴技术。这些未来的研究将进一步验证MCA-Seq准确分析高度可变和异常癌症表观基因组的能力，并生成初步的生物学数据。MCA-Seq的开发和验证将使我们对导致癌症的基本生物学机制的理解取得重大进展，并可能有助于未来癌症疗法的设计。