Characterization and modeling of m6A RNA methylation in cancer

摘要 最丰富的内部mRNA修饰是N6-甲基腺苷（m6 A），越来越多的证据表明， 表明了它在癌症中的重要作用。然而，m6 A RNA修饰及其调控因子的全球模式 无法获得过大的患者队列。目前尚不清楚m6 A RNA修饰如何导致癌症 它可以用于治疗癌症的起始/进展以及如何在癌症治疗中使用。目的是系统地描述 使用表征良好的癌症研究方法，研究m6 A RNA修饰及其调节因子的全基因组模式。 基因组图谱（TCGA）患者队列，阐明其与其他分子畸变的相互作用，并评估 潜在的临床应用。目前的假设是m6 A RNA甲基化的失调发挥了 在癌症发展中起关键作用，可能代表潜在的生物标志物和治疗靶点。我们将 三个具体目标：目标1。使用以下方法生成m6 A RNA甲基化的全基因组图谱： TCGA样本队列。作为NCI功能蛋白质组学中心的一部分，我们的团队拥有独特的访问这些 样品我们已经开发了一种灵敏、强大的m6 A-seq方案，并将其应用于约1,000例患者样本。 从不同的癌症类型，并生成高质量，标准化的m6 A全基因组谱数据。目标2。 使用TCGA样品组生成m6 A调节剂的蛋白质表达谱。使用MD 安德森反相蛋白阵列平台，我们将表征~30个蛋白标记物的表达水平 （包括总蛋白和磷酸化蛋白）的15个m6 A调节剂（五个作者，两个读者，和八个 擦除器）超过31种癌症类型的约8，000个样本以及约400种常见癌细胞系。目标3。执行 在丰富的TCGA背景下对m6 A RNA甲基化数据进行综合分析和建模。使用TCGA 多维分子数据，我们将开发预测模型，量化各种因素的影响 通过深度学习参与m6 A RNA修饰。我们将进行分析，以确定基于m6 A的肿瘤 亚型，评估m6 A相关标志物的临床效用，并研究m6 A与其他分子的相互作用， 在不同的肿瘤环境中的畸变。最后，我们将建立一个公开的、用户友好的数据库， 包含通过目标#1和目标#2生成的m6 A数据的综合信息。预期 该项目的成果是（i）建立m6 A相关基因组和蛋白质组的综合资源， 基于最广泛使用的癌症患者队列的数据，以便可以进一步研究这些数据。 由癌症研究界流利地进行;和（ii）评估的生物和临床效用， m6 A RNA甲基化以综合方式用于癌症治疗。这个项目是创新的，因为它将 系统地评估目前被认为是重要的一类RNA修饰的临床相关性和功能， 在癌症研究中被低估了这些结果将产生重要的积极影响，因为知识 这不仅将大大推进我们对m6 A RNA甲基化在癌症发展中作用的理解， 而且还直接促进了一类新的癌症生物标志物和治疗靶点的开发。

ABSTRACT The most abundant internal mRNA modification is N6-methyladenosine (m6A), and growing evidence has suggested its critical roles in cancer. However, the global patterns of m6A RNA modification and its regulators over large patient cohorts are not available. It remains unclear how m6A RNA modification contributes to cancer initiation/progression and how it may be used in cancer therapy. The objective is to systematically characterize the genome-wide patterns of m6A RNA modification and its regulators using well-characterized The Cancer Genome Atlas (TCGA) patient cohorts, elucidate their interactions with other molecular aberrations, and assess their potential clinical utility. The working hypothesis is that the dysregulation of m6A RNA methylation plays critical roles in cancer development and may represent potential biomarkers and therapeutic targets. We will pursue three specific aims: Aim #1. Generate the genome-wide profiles of m6A RNA methylation using TCGA sample cohorts. As part of an NCI Functional Proteomic Center, our team has unique access to these samples. We have developed a sensitive, robust m6A-seq protocol, and will apply it to ~1,000 patient samples from diverse cancer types, and generate high-quality, standardized m6A genome-wide profile data. Aim #2. Generate the protein expression profiles of m6A regulators using TCGA sample cohorts. Using the MD Anderson reverse-phase protein array platform, we will characterize the expression levels of ~30 protein markers (including both total and phosphorylated proteins) of 15 m6A regulators (five writers, two readers, and eight erasers) over ~8,000 samples of 31 cancer types as well as ~400 common cancer cell lines. Aim #3. Perform the integrative analysis and modeling of m6A RNA methylation data in a rich TCGA context. Using TCGA multi-dimensional molecular data, we will develop predictive models that quantify the effects of various factors involved in m6A RNA modification by deep learning. We will perform analyses to define m6A-based tumor subtypes, assess the clinical utility of m6A-related markers, and study the interactions of m6A with other molecular aberrations in diverse tumor contexts. Finally, we will build a publicly available, user-friendly database that will contain comprehensive information of the m6A data generated through Aim #1 and Aim #2. The expected outcome of this project is (i) the establishment of an integrated resource of m6A-related genomic and proteomic data based on the most widely used cancer patient cohorts, so that further investigation of such data can be conducted by the cancer research community fluently; and (ii) assessment of the biological and clinical utility of m6A RNA methylation for cancer therapy in a comprehensive way. This project is innovative because it will systematically assess the clinical relevance and functions of a key class of RNA modifications that are currently understudied in cancer research. These results will have an important positive impact because the knowledge gained will not only greatly advance our understanding of the role of m6A RNA methylation in cancer development, but also directly facilitate the development of a novel class of cancer biomarkers and therapeutic targets.