Project 3: Statistical/Computational Methods for Pharmacogenomics and Individuali

项目3：药物基因组学和指数的统计/计算方法 视觉化治疗 项目总结 这项研究的广泛和长期目标是开发新的和高影响力的统计和 用于发现与个体间疗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成分优化药物治疗。 辅导课。这次续期申请的具体目的包括：(1)调查统计方法，以评估 DNA变异对临床不良事件(如神经病、中性粒细胞减少症和高血糖)发生的影响 在复杂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)，以了解基因组图谱对 治疗反应；(3)采用统计学方法识别肿瘤细胞亚克隆并与肿瘤内相关 临床结果的异质性；以及(4)开发机器学习技术以发现和验证 可以区分不同治疗反应的患者组的生物标志物。所有这些目标都有 受到调查人员应用研究经验的激励，并解决最及时和最重要的问题 药物基因组学和个体化治疗中的问题。建议的解决方案建立在声音统计的基础上 和数据挖掘原理。新方法的理论性质将通过 现代经验过程理论和其他高级数学论点。高效稳定的数值计算 将设计算法来实施新方法。将进行广泛的模拟研究，以 评估新的推论和数值程序在现实环境中的操作特点。AP- 将为大量的癌症研究提供实验，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，Practical 并将开发用户友好的软件，并免费向公众传播。这项研究将 改变药物基因组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的患者带来最佳治疗。

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.