An integrated approach to dissect the functional network of large non-coding RNA

描述（由申请人提供）：最近的研究表明，人类基因组编码了数千种蛋白质编码能力很小的lncrna。LncRNAs已被证明在癌症中发挥重要作用，并可能成为一类新的癌症治疗靶点。然而，绝大多数lncrna在癌症中的功能仍然未知。LncRNA的功能通常取决于其与蛋白质复合物的物理相互作用。它们还可以通过竞争microRNA结合，即作为竞争的内源性RNA (ceRNA)，影响被相同microRNA靶向的其他mrna的丰度。基因组技术的进步，特别是基于下一代测序（NGS）的技术，为表征lncRNA在癌症中的功能网络提供了无与伦比的机会。然而，分析和整合不同类型的基因组数据集以产生可测试的假设是具有挑战性的，并且缺乏表征lncRNA在癌症中的功能的系统方法。本应用程序描述了计算方法和综合基因组策略的发展，用于系统地解剖lncRNA在癌症中的功能网络，以及计算和实验方法的结合，以揭示lncRNA在前列腺癌中的几个重要功能网络。具体而言，它将(1)开发一种计算方法，重新利用公开可用的基于阵列的数据来询问肿瘤样本中的lncRNA表达，并利用整合基因组策略通过分析肿瘤样本的lncRNA表达谱、临床信息和体细胞基因组改变谱来预测可能对前列腺癌肿瘤发生/肿瘤抑制重要的lncRNA；(2)鉴定与EZH2相关或EZH2抑制的直接转录靶点在前列腺肿瘤发生或肿瘤抑制中起重要作用的lncrna；(3)鉴定AR和PTEN介导前列腺肿瘤发生或肿瘤抑制的cerna。除了科学提案之外，本申请还提出了一个综合培训计划，以培养一名在计算基因组学，非编码RNA和癌症领域的独立研究者，开发尖端的计算方法，并使用计算和实验相结合的方法来理解非编码RNA的结构-功能关系以及非编码RNA的功能和RNA-蛋白质相互作用在癌症中的作用。虽然本申请的候选人在生物物理学、统计学、机器学习和计算基因组学方面接受了广泛的培训，但这个职业发展奖项将使他能够发展他的实验技能，特别是那些基于下一代测序的技术和人类细胞系的分子生物学实验。生物统计学和计算生物学教授刘博士和医学教授布朗博士将在哈佛医学院社区的丹娜-法伯癌症研究所的优秀培训环境中指导候选人。一个由经验丰富的计算生物学家和癌症生物学家组成的委员会也将在科学研究和职业发展方面为他提供建议。

DESCRIPTION (provided by applicant): Recent studies revealed that the human genome encodes thousands of lncRNAs with little protein-coding capacity. LncRNAs were shown to play important roles in cancer and are potentially a new class of therapeutic targets for cancer. However, the function of the vast majority of lncRNAs in cancer remains unknown. LncRNA function often depends on its physical interactions with protein complexes. They can also influence the abundance of other mRNAs that are targeted by the same microRNAs by competing for microRNA binding, i.e., serving as competing endogenous RNA (ceRNA). Advances in genomic technologies, especially those based on next generation sequencing (NGS), provide unparalleled opportunities to characterize the functional networks of lncRNA in cancer. However, analysis and integration of different types of genomic datasets to generate testable hypotheses is challenging, and systematic approaches to characterize lncRNA function in cancer are lacking. This application describes the development of computational methods and integrative genomic strategies for systematically dissecting the functional network of lncRNA in cancer, and a combination of computational and experimental approaches to unravel several important functional networks of lncRNA in prostate cancer. Specifically, it will (1) develop a computational method for repurposing the publically available array-based data to interrogate lncRNA expression in tumor samples and utilize an integrative genomic strategy to predict lncRNAs that may be important for tumorigenesis/tumor suppression in prostate cancer via analysis of lncRNA expression profiles, clinical information and somatic genomic alteration profiles of tumor samples, (2) identify the lncRNAs that are associated with EZH2 or direct transcriptional targets of EZH2 repression that are important for prostate tumorigenesis or tumor suppression, and (3) identify the ceRNAs of AR and PTEN that mediate prostate tumorigenesis or tumor suppression. In addition to its scientific proposal, this application proposes a comprehensive training program for preparing an independent investigator in the fields of computational genomics, non-coding RNA and cancer, who develops cutting-edge computational methods, and uses a combination of computational and experimental approaches to understand structure- function relationship of non-coding RNA and the function of non-coding RNA and RNA-protein interaction in cancer. While the candidate of this application has received extensive training in biophysics, statistics, machine learning and computational genomics, this career development award will allow him to develop his experimental skills, especially those next-generation sequencing-based techniques and molecular biology experiments in human cell lines. Dr. Liu, Professor of Biostatistics and Computational Biology and Dr. Brown, Professor of Medicine will mentor the candidate in the excellent training environment of Dana-Farber Cancer Institute, a part of Harvard Medical School community. A committee of experienced computational and cancer biologists will also advise him on both scientific research and career development.