In Silico Screening of Alternative Polyadenylation Regulators in Cancers

项目总结 交替多聚腺苷酸化(APA)使同一基因有多个3‘非编码区末端，并影响超过 人类70%的基因。通过改变多聚腺苷酸化位点，APA可以产生不同顺式调控的转录本 影响稳定性和平移的因素。越来越多的证据表明，APA正在发挥作用 在癌症中扮演重要角色。例如，白血病的癌基因CCND1被发现使用较短的3‘非编码区来 在增殖和转化细胞中逃脱miRNA抑制。我们的研究(夏，自然通讯) 在数百例肿瘤标本中观察到3‘非编码区全局性缩短。因此，APA监管机构管理 癌症中普遍存在的3‘非编码区缩短可能会导致癌症治疗药物靶点的发现。为此， 我们的另一项研究(Masamha Nature)确定CFIm25是GBM中APA的主要调节因子。然而，APA 其他癌症的调控机制仍需探索。最近，随着生物信息学工具的发展， 从rna-seq中量化apa的使用和大的癌症基因组广泛使用rna-seq 通过计算大数据分析，有可能确定《行政程序法》监管机构。我们的预赛 分析表明，急性髓系白血病(AML)的高度突变基因DNMT3A是一种潜在的APA AML中的监管机构。因此，我们假设一个强大而专门的计算筛选模型可以 用于通过将APA的用途与其他分子特征相结合来揭示APA对癌症的调节作用， 包括基因表达和DNA突变。这项提议的目的就是开发这样一部小说 计算方法，并应用该方法从33个约15,000个肿瘤样本中推断出APA调节子 癌症类型。这些已鉴定的主要APA调节基因可能作为新的癌症驱动/抑制基因 从而为发现治疗靶点提供了新的方向。我的职业目标是开发和应用小说 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。亚当博士 马戈林，俄勒冈健康与科学大学(OHSU)计算生物学项目主任 俄亥俄州立大学奈特癌症研究所所长布莱恩·德鲁克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，进一步提高了我在这两个领域的研究知识 计算生物学和癌症生物学。这项K01奖励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拨款申请，如NIH R01，因此具有 对我在计算癌症生物学领域维持职业生涯的长期影响。

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.