Characterization and modeling of m6A RNA methylation in cancer

点击翻译按钮获取中文摘要

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.