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：药物基因组学和个体差异的统计/计算方法 个体化治疗 项目概要 这项研究的广泛、长期目标是开发新颖且具有高影响力的统计和方法 用于发现与个体间功效差异相关的遗传变异的计算工具 癌症药物的毒性和毒性，并根据每个患者的遗传结构优化药物治疗 学费。本次续展申请的具体目标包括：（1）研究统计方法来评估 DNA 变异对不良临床事件发生的影响（例如神经病、中性粒细胞减少症和高血压） 复杂的审查和抽样方案下的癌症临床试验中的紧张）； (2)探索统计工具 整合多种类型的基因组数据（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），以了解基因组谱对 治疗反应； (3) 采用统计方法来辨别肿瘤细胞亚克隆并关联肿瘤内 临床结果的异质性； (4) 开发机器学习技术来发现和验证 可以区分具有不同治疗反应的患者群体的生物标志物。所有这些目标都有 受到研究者应用研究经验的激励，并解决最及时和重要的问题 药物基因组学和个体化治疗的问题。所提出的解决方案建立在可靠的统计基础上 和数据挖掘原理。新方法的理论特性将通过严格建立 现代经验过程理论和其他高级数学论证。高效稳定的数值 将设计算法来实施新方法。将进行广泛的模拟研究 评估现实环境中新的推理和数值程序的操作特性。阿普- 复制将提供给大量癌症研究，其中大部分是在杜克大学进行的 （杜克大学）和北卡罗来纳大学教堂山分校 (UNC-CH)。与Core B合作，实用 将开发用户友好的软件并向公众免费传播。这项研究将 改变药物基因组学研究和个体化治疗试验的设计和分析方式， 将为癌症和其他疾病患者带来最佳治疗。