Integrative genomic models for analysis of pharmacogenomic studies

用于分析药物基因组研究的综合基因组模型

• 批准号: 7698677
• 负责人: Brooke L Fridley
• 金额: $ 13.3万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7698677
• 关键词: Accounting; Antineoplastic Agents; Applications Grants; Bayesian Method; Biological Models; Cancer Patient; Cell Line; Cells; Clinical; Complex; Computers; Computing Methodologies; Copy Number Polymorphism; DNA Resequencing; Data; Data Analyses; Development; Discipline; Disease; Dose; Drug usage; Employee Strikes; Etiology; Genes; Genetic; Genetic Variation; Genome; Genomics; Goals; Human; Individual; Joints; Lead; Markov Chains; Measures; Medicine; Methodology; Methods; Modeling; Nature; Non-linear Models; Nutrient; Ovarian; Pathway interactions; Pharmaceutical Preparations; Pharmacogenetics; Pharmacogenomics; Phenotype; Research; Research Personnel; Role; Science; Simulate; Single Nucleotide Polymorphism; Source; Statistical Methods; Testing; Variant; Xenobiotics; analytical method; anticancer research; base; chemotherapeutic agent; clinical application; cytotoxicity; gemcitabine; genome-wide; insight; interest; lymphoblastoid cell line; mRNA Expression; malignant breast neoplasm; novel; public health relevance; response; simulation; tool; translational study

DESCRIPTION (provided by applicant): Recently, there has been an increased interest in "individualized medicine" and thus pharmacogenetics and pharmacogenomics in cancer research have moved to the forefront as well. Pharmacogenetics is the study of the role of inheritance in individual variation in response to drugs, nutrients and other xenobiotics. In this post-genomic era, pharmacogenetics has evolved into pharmacogenomics, a discipline that has been heralded as one of the first major clinical applications of the striking advances that have occurred and continue to occur in human genomic science. Statistical methods that combine various sources of genomic data would likely provide novel insights, yet are lacking in pharmacogenomic studies. Joint analysis of multiple types of genomic data is advantageous, especially when the etiology of the disease or phenotype is complex. For example, my group has been collaborating with a pharmacogenomic research group that is conducting studies of the effect of anti-cancer drugs on lymphoblastoid cell lines (Coriell Human Variation Panel) to begin to define the effect of common genetic variation on drug response phenotypes. These studies have included cytotoxicity measures at multiple drug concentrations, basal mRNA expression array data, post-drug treatment mRNA expression data, metabolite data, resequencing data for genes in known drug pathways, and genome-wide genetic information in the form of single nucleotide polymorphisms (SNPs), and copy number variation (CNVs). Analysis of this data-rich "model system" is a challenge. A statistical approach that could account for disparate "layers" of genomic data is based on Bayesian methodology. Over the last few decades, applications of Bayesian methods by Markov Chain Monte Carlo (MCMC) have increased with the advancement of computers and computational methods, particularly with application to genetic data. In this current grant application, we intend to combine various sources of phenotypic and genomic data collected on the "Human Variation Panel" cell lines (e.g. cytotoxicity data, mRNA expression data, genotypic data) into pharmacogenomic models using Bayesian methods that will assist researchers in generating hypotheses that will lead to better understanding of the complex nature of the relationship between the genome and drug response, leading eventually to the development of "individualized therapy" for cancer patients. To accomplish this, we will be combining path modeling ideas into a Bayesian hierarchical nonlinear model to assess the relationship between cytotoxicity drug endpoints and genomic information. PUBLIC HEALTH RELEVANCE: This application proposes to develop novel statistical methods for the joint analysis of genomic data collected on the Coriell Human Variation Panel cell lines involving pharmacogenomic studies of anti-cancer drugs. In addition, results from these pharmacogenomic studies involving the cell lines will be tested in pharmacogenomic translational studies, using genomic information collected from cancer patients treated with the anti-cancer drug. These translational studies will test whether genetic variations identified from the pharmacogenomic studies of the cell lines might be associated with clinical response. Therefore, these statistical methods will have broader applications in pharmacogenomic studies beyond cell-based model systems to translational studies involving cancer patients treated with chemotherapeutic agents. These models will also aid investigator in generate hypotheses that will lead to better understanding of the complex nature of the relationship between genomic variation and drug response, leading eventually to the development of "individualized therapy" for cancer patients. These models will also be applicable to the study of complex diseases where multiple types of genomic data are collected.

描述（由申请人提供）：最近，人们对“个体化医学”的兴趣越来越大，因此癌症研究中的药物遗传学和药物基因组学也走到了最前沿。药物遗传学是研究遗传在个体对药物、营养素和其他外源性物质的反应中的作用。在这个后基因组时代，药物遗传学已经演变成药物基因组学，这是一门被誉为人类基因组科学已经发生并将继续发生的惊人进展的第一个主要临床应用的学科。联合收割机结合各种来源的基因组数据的统计方法可能会提供新的见解，但缺乏药物基因组学研究。多种类型的基因组数据的联合分析是有利的，特别是当疾病或表型的病因学是复杂的。例如，我的小组一直在与一个药物基因组学研究小组合作，该研究小组正在进行抗癌药物对淋巴母细胞系（Coriell Human Variation Panel）的影响研究，以开始确定常见遗传变异对药物反应表型的影响。这些研究包括多种药物浓度下的细胞毒性测量、基础mRNA表达阵列数据、药物治疗后mRNA表达数据、代谢物数据、已知药物途径中基因的重测序数据以及单核苷酸多态性（SNP）和拷贝数变异（CNV）形式的全基因组遗传信息。分析这个数据丰富的“模型系统”是一个挑战。一种可以解释基因组数据的不同“层”的统计方法是基于贝叶斯方法。在过去的几十年里，贝叶斯方法的马尔可夫链蒙特卡罗（MCMC）的应用程序随着计算机和计算方法的进步而增加，特别是与遗传数据的应用。在当前的拨款申请中，我们打算将“人类变异小组”细胞系收集的表型和基因组数据的各种来源联合收割机结合起来（例如细胞毒性数据、mRNA表达数据、基因型数据）转化为使用贝叶斯方法的药物基因组学模型，这将有助于研究人员产生假设，从而更好地理解基因组与药物反应之间关系的复杂性质，最终导致癌症患者的“个体化治疗”的发展。为了实现这一点，我们将结合路径建模的想法到贝叶斯分层非线性模型，以评估细胞毒性药物终点和基因组信息之间的关系。公共卫生相关性：本申请提出开发新的统计方法，用于对Coriell Human Variation Panel细胞系收集的基因组数据进行联合分析，涉及抗癌药物的药物基因组学研究。此外，将使用从接受抗癌药物治疗的癌症患者中收集的基因组信息，在药物基因组学转化研究中检测这些涉及细胞系的药物基因组学研究的结果。这些转化研究将检测从细胞系药物基因组学研究中鉴定的遗传变异是否可能与临床应答相关。因此，这些统计方法将在药物基因组学研究中具有更广泛的应用，超越基于细胞的模型系统，以涉及用化疗剂治疗的癌症患者的转化研究。这些模型还将帮助研究人员产生假设，从而更好地理解基因组变异和药物反应之间关系的复杂性，最终导致癌症患者“个体化治疗”的发展。这些模型也将适用于收集多种类型基因组数据的复杂疾病的研究。