Dr. Keiko Akagi, A Bioinformatics Specialist in Cancer Genomics Research at OSU Comprehensive Cancer Center

Keiko Akagi 博士，俄勒冈州立大学综合癌症中心癌症基因组学研究生物信息学专家

• 批准号: 9769670
• 负责人: Keiko Akagi
• 金额: $ 12.25万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-20 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9769670
• 关键词: Algorithms; Animals; Area; Bioinformatics; Biological; Biological Models; Cancer Model; Catalogs; Complex; Comprehensive Cancer Center; Computational algorithm; Computer Analysis; DNA Sequence Alteration; DNA Sequence Analysis; Data; Data Set; Detection; Development; Diagnosis; Generations; Genes; Genome; Genomics; Germ-Line Mutation; Goals; Human; Informatics; Light; Malignant Neoplasms; Methods; Minority; Molecular; Molecular Target; Mus; Mutation; Ohio; Oncogenes; Output; Patients; Play; Predisposition; Recording of previous events; Research; Risk stratification; Role; Specialist; Structure; Susceptibility Gene; Technology; Testing; Variant; Work; actionable mutation; analytical method; anticancer research; bioinformatics tool; cancer genome; cancer genomics; cancer therapy; computerized tools; deep sequencing; disease-causing mutation; exome sequencing; genome sequencing; human data; improved; innovation; novel therapeutic intervention; sequencing platform; tumor progression; whole genome

Project Summary/Abstract The recent development and implementation of second-generation, deep sequencing technologies has provided an unprecedented opportunity to characterize genomic changes in cancer. However, the enormous data output from these sequencing platforms also presents a formidable statistical and computational challenge to separate and validate the minority of cancer-causing driver mutations from the overwhelming majority of irrelevant bystander passenger mutations. Computational analysis plays a critically important role in making biological sense out of the mountains of genomic sequencing data. In this proposal, I propose to continue my research work as an expert in cancer bioinformatics, to develop computational tools to narrow down the candidate cancer-causing disease mutations involved in the development and progression of cancer. My bioinformatics work will focus on three research areas: (i) identification of genes causing familial disposition to cancer susceptibility, (ii) detection and characterization of large structural alterations in cancer, and (iii) identification of cancer genes using mouse cancer model system. My short term goal is to develop bioinformatics tools to develop bioinformatics tools to identify cancer-causing disease mutations using genome sequencing data from human patients and mouse cancer models. My long term goal is to characterize these driver mutations further, generating molecular targets to improve diagnosis, risk stratification and treatment of cancer. In my first aim, I propose to develop a bioinformatics pipeline to identify cancer predisposing germline mutations from patients with strong familial history of cancer using whole-genome or whole-exome sequencing data. This aim tests the hypothesis that cancer predisposing mutations can be weighted and validated from enormous sequencing data sets using statistical and bioinformatics methods. In my second specific aim, I will develop bioinformatics algorithms to detect and characterize large structural variants in human cancer. This aim tests the hypothesis that integrative analysis of different genome sequencing platforms can be further refined and validated the full structure of complex genomic alteration. In my third aim, I will develop algorithms to identify the genes that accelerate the development of cancer in mouse cancer models. This aim tests the hypothesis that the computational algorithms and statistical approaches can identify genes predisposing animals to develop cancer and can predict their relevance to human cancer. Successful completion of this groundbreaking new informatics research as a Research Specialist will shed new light on the molecular basis of many cancers, will contribute to active cancer research at Ohio State, and will continue significant recent progress in developing new genomics technologies and analytical methods in studies of human cancers.

项目摘要/摘要 第二代深度测序技术的最新发展和实施已经 为描述癌症的基因组变化提供了一个前所未有的机会。然而，巨大的 从这些测序平台输出的数据也呈现出强大的统计和计算能力 从压倒性的致癌基因突变中分离和验证少数致癌基因突变的挑战 大多数无关的旁观者乘客突变。计算分析在以下方面发挥着至关重要的作用 从堆积如山的基因组测序数据中获得生物学意义。在这项建议中，我建议 继续我作为癌症生物信息学专家的研究工作，开发计算工具来缩小 下调与癌症发生和发展有关的候选致癌疾病突变。 我的生物信息学工作将集中在三个研究领域：(I)识别导致家族倾向的基因 癌症易感性，(Ii)检测和表征癌症中的大的结构变化，以及(Iii) 利用小鼠肿瘤模型系统进行肿瘤基因的鉴定。我的短期目标是发展 生物信息学工具开发生物信息学工具以使用基因组识别致癌疾病突变 对来自人类患者和小鼠癌症模型的数据进行测序。我的长期目标是描述这些 进一步推动突变，产生分子靶点以改善诊断、风险分层和治疗 癌症。在我的第一个目标中，我建议开发一条生物信息学管道来识别癌症易感生殖系 利用全基因组或全外显子组测序技术检测有癌症家族史患者的突变 数据。这一目标测试了一种假设，即癌症易感基因突变可以加权并从 使用统计和生物信息学方法的海量测序数据集。在我的第二个具体目标中，我将 开发生物信息学算法来检测和表征人类癌症中的大型结构变异。这 AIM验证了不同基因组测序平台的整合分析可以进一步 提炼和验证了复杂基因组改变的完整结构。在我的第三个目标中，我将开发算法 在小鼠癌症模型中识别加速癌症发展的基因。这一目标考验着 假设计算算法和统计方法可以识别易感基因 动物会患上癌症，并能预测它们与人类癌症的相关性。成功完成这项工作 作为研究专家的突破性新信息学研究将为分子基础带来新的曙光 在许多癌症中，将为俄亥俄州立大学活跃的癌症研究做出贡献，并将继续在最近 人类癌症研究中新的基因组学技术和分析方法的进展。