In Silico Screening of Alternative Polyadenylation Regulators in Cancers

项目摘要 选择性多聚腺苷酸化（阿帕）使同一基因具有多个3 'UTR末端，并影响多个3' UTR末端。 70%的人类基因通过改变多聚腺苷酸化位点，阿帕可以产生具有不同顺式调节的转录物， 影响稳定性和翻译的因素。越来越多的证据表明阿帕 在癌症中的重要作用。例如，CCND 1，白血病中的癌基因，被发现使用较短的3 'UTR， 逃避增殖和转化细胞中的miRNA抑制。我们的研究（Xia，Nature Communications） 在数百个肿瘤样品中观察到3 'UTR的整体缩短。因此，阿帕监管机构 癌症中广泛的3 'UTR缩短可能导致癌症治疗的药物靶点发现。为此， 我们的另一项研究（Masamha Nature）将CFIm 25鉴定为GBM中的主阿帕调节剂。然而，阿帕 其他癌症中的调节因子仍需要探索。近年来，随着生物信息学工具的发展， 来自RNA-seq的阿帕使用量化以及大型癌症基因组对RNA-seq的广泛使用 财团，可以通过计算大数据分析来识别阿帕监管机构。我们的初步 分析已经确定DNMT 3A，一种急性髓细胞白血病（AML）中高度突变的基因，作为潜在的阿帕 AML中的调节剂。因此，我们假设，一个强大的和专门的计算筛选模型可以 通过阿帕与其他分子特征的整合用于揭示癌症的阿帕调节因子， 包括基因表达和DNA突变。这项提案的目的是发展这样一部小说 计算方法，并应用该方法从33个国家的约15，000个肿瘤样本中推断阿帕调节因子。 癌症类型。这些被鉴定的阿帕主调节基因可能作为新的癌症驱动/阻遏基因 从而为治疗靶点的发现提供了新的方向。我的职业目标是开发和应用新的 用于复杂和大规模临床数据分析的计算和系统建模方法，通过这样做， 为癌症和其他疾病提供新的分子诊断和潜在的治疗方法。亚当医生 Margolin是俄勒冈州健康与科学大学（OHSU）计算生物学项目的主任。 和OHSU骑士癌症研究所所长Brian Druker博士将组成一个多学科指导小组， 团队提供了大量的教育机会，以进一步提高我的研究知识，在这两个 计算生物学和癌症生物学。这个K 01补助金将为我提供受保护的时间来开发必要的 独立研究的技能和成功的未来赠款申请，如NIH R 01，因此有一个 这对我在计算癌症生物学领域的职业生涯产生了长期的影响。

PROJECT SUMMARY Alternative polyadenylation (APA) enables the same gene to have multiple 3'UTR ends and affects more than 70% of human genes. By altering polyadenylation sites, APA can create transcripts with different cis-regulatory elements to influence stability and translation. Accumulating evidence has indicated that APA is playing important roles in cancers. For example, CCND1, an oncogene in leukemia, was found to use shorter 3'UTR to escape miRNA repression in proliferating and transformed cells. Our study (Xia, Nature Communications) observed global shortening of 3'UTR in hundreds of tumor samples. Therefore, APA regulators governing widespread 3'UTR shortening in cancer may lead to drug target discoveries for cancer therapy. To this end, our another study (Masamha Nature) identified CFIm25 as a master APA regulator in GBM. However, APA regulators in other cancers still need to be explored. Recently, with the development of bioinformatics tools for APA usage quantification from RNA-seq and wide employment of RNA-seq by large cancer genome consortiums, it is possible to identify APA regulators through computational big data analysis. Our preliminary analyses have identified DNMT3A, a highly mutated gene in acute myeloid leukemia (AML), as a potential APA regulator in AML. Therefore, we hypothesize that a powerful and dedicated computational screening model can be used to reveal APA regulators for cancers through integration APA usage with other molecular features, including gene expression and DNA mutation. The objective of this proposal is to develop such a novel computational method, and apply this method to infer APA regulators from ~15,000 tumor samples across 33 cancer types. These identified master APA regulator genes may sever as novel cancer driver/repressor genes and thus provide new directions for therapeutic target discovery. My career goal is to develop and apply novel computational and systems modeling methods for complex and large-scale clinical data analysis, by doing so, provide novel molecular diagnosis and potential therapeutics for cancer and other diseases. Dr. Adam Margolin, the director of the Computational Biology Program at Oregon Health & Science University (OHSU) and Dr. Brian Druker, the director of OHSU's Knight Cancer Institute, will form a multidisciplinary mentoring team to provided numerous educational opportunities to further enhance my research knowledge in both computational biology and cancer biology. This K01 grant will offer me the protected time to develop essential skills for independent research and the successful future grants application like NIH R01, and thus have a long-term impact on my ability to sustain a career in computational cancer biology field.