Massively Parallel Sequencing Technology for Cancer Epigenome.

癌症表观基因组大规模并行测序技术。

基本信息

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
项目类别：
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

项目摘要

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 丰富区域，覆盖所有基因的 76% 和所有真正的 CpG 岛的 70%。当与微阵列结合时，该技术已被证明具有高度特异性，对于正常细胞和癌细胞中全基因组甲基化的高通量分析非常有用。然而，探针性能不均匀、交叉反应性、标准化困难以及相关控制问题等问题阻碍了 MCA 充分发挥定量潜力。我们建议通过将 MCA 与“下一代”Solexa 1G 测序技术 (MCA-Seq) 结合来开发和验证用于 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，我们将为 MCA-Seq 建立质量控制并开发数据分析的优化算法。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。我们提出的研究将开发出一种简单、稳健且可靠的 DNA 甲基化全基因组检测方法，这将在癌症研究中具有广泛的用途。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特征明确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 准确分析高度变异和异常的癌症表观基因组并生成初步生物学数据的能力。 MCA-Seq 的开发和验证将使我们对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。