Integrative genomic models for analysis of pharmacogenomic studies

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。药物遗传学是一门研究遗传在个体对药物、营养物质和其他外源药物的反应中的作用的学科。在这个后基因组时代，药物遗传学已经演变为药物基因组学，这是一门学科，被誉为人类基因组科学中已经发生并将继续发生的惊人进展的首批主要临床应用之一。结合各种基因组数据来源的统计方法可能会提供新的见解，但在药物基因组学研究中缺乏。联合分析多种类型的基因组数据是有利的，特别是当疾病的病因或表型是复杂的。例如，我的小组一直在与一个药物基因组研究小组合作，该小组正在研究抗癌药物对淋巴母细胞样细胞系的影响（科里ell人类变异小组），开始定义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、已知药物通路中基因的重测序数据、单核苷酸多态性（snp）和拷贝数变异（CNVs）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不同“层”的统计方法是基于贝叶斯方法的。在过去的几十年里，随着计算机和计算方法的进步，特别是在遗传数据方面的应用，贝叶斯方法在马尔可夫链蒙特卡罗（MCMC）中的应用越来越多。在当前的拨款申请中，我们打算使用贝叶斯方法将“人类变异小组”细胞系上收集的各种表型和基因组数据来源（例如细胞毒性数据、mRNA表达数据、基因型数据）结合到药物基因组学模型中，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本质。最终导致针对癌症患者的“个体化治疗”的发展。为了实现这一目标，我们将把路径建模思想结合到贝叶斯层次非线性模型中，以评估细胞毒性药物端点与基因组信息之间的关系。公共卫生相关性：本申请旨在开发新的统计方法，用于联合分析从科里埃尔人类变异小组细胞系收集的基因组数据，涉及抗癌药物的药物基因组学研究。此外，这些涉及细胞系的药物基因组学研究结果将在药物基因组学转化研究中进行测试，使用从接受抗癌药物治疗的癌症患者收集的基因组信息。这些转化研究将测试从细胞系的药物基因组学研究中发现的遗传变异是否可能与临床反应有关。因此，这些统计方法将在药物基因组学研究中有更广泛的应用，不仅仅是基于细胞的模型系统，还包括涉及化疗药物治疗的癌症患者的转化研究。这些模型还将帮助研究者产生假设，从而更好地理解基因组变异和药物反应之间关系的复杂本质，最终导致癌症患者“个性化治疗”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。

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.