Statistical Models in Epigenomics

描述（由申请人提供）：我们的主要目标是利用“分子钟假说”来开发数学模型，使我们能够研究癌症如何生长和扩散。人类癌症生长无法直接观察，总体目标是开发一种方法，该方法可以通过“阅读”复制错误在基因组内秘密写入的祖先来回顾性重建肿瘤进展。序列通常用于重建物种和个体的谱系，我们建议将这种一般的分子遗传学方法用于人类癌症。我们将使用DNA甲基化数据，这是一种在细胞分裂时复制的DNA表观遗传修饰。由于直接计算可能不切实际或不可行，我们建议使用拒绝算法，一种基于模拟的方法。这个总体框架将使我们能够估计肿瘤的年龄、转移的年龄、甲基化错误率以及转移是否来自原发性癌症的特定细胞群。我们的目标是在南加州大学诺里斯综合癌症中心正在进行的研究的动机。具体而言，我们建议：1.开发方法，使我们能够使用5'到3' DNA甲基化模式估计表征癌症生长的参数，并使用患者的临床数据和癌细胞系的实验数据验证这些模型。这些模型将解决以下生物学问题：a.基于从甲基化模式推断的祖先树的类型估计癌症干细胞的数目; B.评估肿瘤异质性，例如鉴定肿瘤左侧和右侧的不同细胞亚群; c.估计肿瘤年龄和甲基化错误发生的比率。2.扩展目标1中开发的模型，以包括额外的复杂性。我们建议解决以下问题：a.在单个祖先树内建模多个基因区域; B.对常染色体基因（二倍体基因组）建模; c.对多个组织（原发性肿瘤和转移）进行建模，以回答有关细胞群是否相同年龄，或者一个细胞群是否更年轻并来自另一个细胞群的问题;我们将把目标1-2中开发的方法应用于在人类结肠原发性肿瘤和远处转移中观察到的DNA甲基化模式。公共卫生相关性：根据国家健康统计中心的报告，癌症是2005年美国第二大死亡原因。其治疗依赖于了解癌症如何生长和扩散。我们建议开发数学模型，可以回顾性地重建肿瘤历史，使我们能够解决有关癌症生长和扩散的重要生物学问题。

DESCRIPTION (provided by applicant): Our primary objective is to utilize the "molecular clock hypothesis" to develop mathematical models that will allow us to study how cancers grow and spread. Human cancer growth cannot be directly observed and the overall goal is to develop an approach that can retrospectively reconstruct tumor progression by "reading" the ancestry surreptitiously written within genomes by replication errors. Sequences are commonly used to reconstruct the genealogy of species and individuals, and we propose to translate this general molecular phylogeny approach to human cancers. We will use DNA methylation data, an epigenetic modification of DNA that is replicated at cell division. As direct calculation can be either impractical or infeasible, we propose to use rejection algorithms, a simulation-based approach. This general framework will allow us to estimate the age of a tumor, the age of a metastasis, the methylation error rate, and whether the metastasis is derived from a specific population of cells from the primary cancer. Our aims are motivated by ongoing studies at the Norris Comprehensive Cancer Center at the University of Southern California. Specifically, we propose to: 1. Develop methods that will allow us to estimate parameters characterizing the growth of cancer using 5' to 3' DNA methylation patterns and validate these models using clinical data from patients and experimental data from cancer cell lines. The models will address the following biological problems: a. Estimate the number of cancer stem cells based on the types of ancestral trees inferred from the methylation patterns; b. Evaluate tumor heterogeneity, e.g. identify different subpopulations of cells in the left and right side of the tumor; c. Estimate tumor age and the rate at which methylation errors occur. 2. Extend the models developed in Aim 1 to include additional complexities. We propose to address the following: a. Modeling multiple gene regions within a single ancestral tree; b. Modeling autosomal genes (diploid genomes); c. Modeling multiple tissues (primary tumor and metastasis), to answer questions about whether the cell populations are the same age, or if one is younger and derived from the other; We will apply the methods developed in Aims 1-2 to DNA methylation patterns observed in primary tumors of the colon and distant metastasis in humans. PUBLIC HEALTH RELEVANCE: Cancer is the second leading cause of death in the United States in 2005, as reported by the National Center for Health Statistics. Its treatment relies on understanding how cancers grow and spread. We propose to develop mathematical models that can retrospectively reconstruct tumor histories, allowing us to address important biological questions about the growth and spread of cancer.