Project 3: Statistical/Computational Methods for Pharmacogenomics and Individuali

项目3：药物基因组学和个体的统计/计算方法

• 批准号: 8794728
• 负责人: DANYU LIN
• 金额: $ 46.13万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8794728
• 关键词: Accounting; Address; Adverse event; Algorithms; Alleles; Applied Research; Biological; Biological Markers; Cancer Patient; Cessation of life; Characteristics; Clinical; Clinical Trials; Collaborations; Communities; Complex; Computing Methodologies; Constitution; DNA; DNA Methylation; Data; Disease; Event; Funding; General Population; Genes; Genetic; Genetic Determinism; Genetic Transcription; Genomics; Heterogeneity; Hypertension; Lead; Learning; Longevity; Machine Learning; Malignant Neoplasms; Measurement; Methods; Methylation; MicroRNAs; Neuropathy; Neutropenia; North Carolina; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacogenomics; Pharmacotherapy; Ploidies; Point Mutation; Procedures; Process; Property; RNA; Research; Research Design; Research Personnel; Sampling; Scheme; Solutions; Somatic Mutation; Statistical Methods; Techniques; Therapy trial; Time; Toxic effect; Treatment Protocols; Uncertainty; Universities; Variant; Work; base; computerized tools; data mining; design; exome sequencing; experience; genetic variant; genome-wide; improved; individualized medicine; interest; neoplastic cell; novel; protein expression; rare variant; research and development; response; simulation; sound; theories; tool; treatment response; trial design; tumor; user friendly software

Project 3: Statistical/Computational Methods for Pharmacogenomics and Indi- vidualized Therapy PROJECT SUMMARY The broad, long-term objectives of this research are the development of novel and high-impact statistical and computational tools for discovering genetic variants associated with interindividual differences in the efficacy and toxicity of cancer medications and for optimizing drug therapy on the basis of each patient's genetic consti- tution. The specific aims of this renewal application include: (1) investigating statistical methods to assess the effects of DNA variations on the occurrence of adverse clinical events (e.g., neuropathy, neutropenia and hyper- tension) in cancer clinical trials under complex censoring and sampling schemes; (2) exploring statistical tools to integrate multiple types of genomics data (e.g., copy number alteration, point mutation, DNA methylation, RNA and microRNA expressions, and protein expressions) in understanding the influence of genomic profile on treatment response; (3) pursuing statistical methods to discern tumor cell subclones and to relate intra-tumor heterogeneity to clinical outcomes; and (4) developing machine learning techniques to discover and validate biomarkers that can distinguish groups of patients with different treatment response. All of these aims have been motivated by the investigators' applied research experiences and address the most timely and important issues in pharmacogenomics and individualized therapy. The proposed solutions are built on sound statistical and data-mining principles. The theoretical properties of the new methods will be established rigorously via modern empirical process theory and other advanced mathematical arguments. Efficient and stable numerical algorithms will be devised to implement the new methods. Extensive simulation studies will be conducted to evaluate the operating characteristics of the new inferential and numerical procedures in realistic settings. Ap- plications will be provided to a large number of cancer studies, most of which are carried out at Duke University (Duke) and the University of North Carolina at Chapel Hill (UNC-CH). In collaboration with Core B, practical and user-friendly software will be developed and disseminated freely to the general public. This research will change the ways pharmacogenomic studies and individualized therapy trials are designed and analyzed, which will lead to optimal treatments for patients in cancer and other diseases.

项目3：药物基因组学和指数的统计/计算方法 多性疗法 项目摘要 这项研究的广泛，长期目标是新颖和高影响力统计的发展以及 用于发现与个体差异相关的遗传变异的计算工具 和癌症药物的毒性以及根据每个患者的遗传构成的优化药物治疗 t。此更新应用的具体目的包括：（1）研究统计方法以评估 DNA变异对不良临床事件发生的影响（例如，神经病，中性粒细胞减少和高 - 在复杂的检查和抽样方案下进行癌症临床试验的张力）； （2）探索统​​计工具 整合多种类型的基因组学数据（例如，拷贝数改变，点突变，DNA甲基化， RNA和microRNA表达以及蛋白质表达式）在理解基因组谱对 治疗反应； （3）追求统计方法来识别肿瘤细胞亚克隆并与肿瘤内有关 临床结果的异质性； （4）开发机器学习技术以发现和验证 可以区分具有不同治疗反应的患者组的生物标志物。所有这些目标 受到调查人员的应用研究经验的动机，并解决了最及时，最重要的 药物基因组学和个性化疗法的问题。提出的解决方案建立在健全的统计上 和数据挖掘原则。新方法的理论特性将通过 现代经验过程理论和其他先进的数学论点。高效稳定的数值 将设计算法以实现新方法。大量的模拟研究将进行 评估在现实设置中新推论和数值程序的工作特性。 ap 将向大量的癌症研究提供plications，其中大多数是在杜克大学进行的 （杜克大学）和北卡罗来纳大学教堂山（UNC-CH）。与核心B合作，实用 将开发和用户友好的软件将自由地传播给公众。这项研究会 改变设计和分析的药物基因组学研究和个性化治疗试验的方式， 将导致对癌症和其他疾病患者的最佳治疗方法。