Statistical Genetics of Dose Response Traits

使用淋巴母细胞系模型剖析癌症药物反应的遗传病因一直是一个长期的研究目标。 今年，我继续与我以前的博士生Farida Akhtari一起工作，并在北卡罗来纳州州立大学和北查佩尔山合作。 我们在方法和应用成果两方面都取得了重大进展。 联合治疗在现代化疗治疗中相当常见，因为药物通常协同作用，并且使用LCL模型来扩展药物组合的工作是一个重要的进展。淋巴母细胞系是一种非常成功的模型，用于评估癌症药物反应的遗传病因，但使用这种模型的应用通常集中在单一药物。联合治疗在现代化疗治疗中相当常见，因为药物通常协同作用，并且使用LCL模型来扩展药物组合的工作是一个重要的进展。我们证明了协同作用的发生，并可以在一系列临床上重要的药物组合的LCL中进行量化。在这里，我们使用来自扩展谱系的细胞系来证明协同药物反应有大量的可遗传成分。该证明支持扩展LCL模型的使用以执行联合治疗的关联映射的前提。 这项研究也提出了一些统计/计算的挑战。 与我最近毕业的博士生Jun Ma博士一起，我们开发了一种解决非线性剂量反应关系挑战的方法。非线性剂量-反应关系广泛存在于受不同水平的生物、化学或辐射应激影响的细胞、生物化学和生理过程中。 非线性剂量-反应关系广泛存在于受不同水平的生物、化学或辐射应激影响的细胞、生物化学和生理过程中。因此，我们建议使用EA的剂量-反应模型的范围内的潜在的反应模型的功能形式。这种新方法不仅可以拟合最常用的非线性剂量-反应模型（如指数模型和3-，4-和5-参数logistic模型），而且还可以选择最佳模型，如果没有模型假设，这是特别有用的情况下，高通量曲线拟合。开发了一个R软件包来实现该方法。 此外，与Farida博士一起，我们使用多变量方差分析方法在来自美国查佩尔山合作者的数据集中识别与体外剂量反应相关的体内变量。在一项包括乳腺癌患者体内和体外数据的新研究中，发现种族和吸烟状况与LCL的体外剂量反应显著相关。创建LCL的供体个体的吸烟状态通常是未知的，因此在LCL的剂量反应分析中不进行控制。需要进一步研究以了解体内暴露于吸烟影响LCL体外剂量反应的机制。 我们最近还在该模型中完成了44种抗癌药物的高通量筛选。 癌症患者对几种广泛使用的抗癌药物的反应和毒性表现出广泛的个体间差异。遗传关联作图可用于通过鉴定与差异反应相关的基因来了解癌症药物反应的遗传病因。为了确定影响广泛用于治疗各种不同类型癌症的44种FDA批准的抗癌药物的反应的新基因，我们用这些药物筛选了来自种族和种族多样化的1000个基因组项目的680个淋巴母细胞系。我们的全基因组关联图谱确定了与广泛的抗癌药物的反应相关的几种新的遗传变异。我们对关联作图结果中的一个基因NAD H醌脱氢酶进行了进一步的分析和功能验证，以确定其影响药物反应的作用机制。 目前正在几个方面继续开展工作。 我们还在继续将GWAS映射到一类新的药物单克隆抗体治疗。

Dissecting the genetic etiology of cancer drug response using a lymphoblastoid cell line model has been a longstanding research goal. This year, I have continued my work with my former PhD student Farida Akhtari, and collaborations at North Carolina State University, and UNC Chapel Hill. We have made substantial progress in both methodological aspects, and with applied results. Combination therapy is quite common in modern chemotherapy treatment since drugs often work synergistically, and it is an important progression in the use of the LCL model to expand work for drug combinations. Lymphoblastoid cell lines are a highly successful model for evaluating the genetic etiology of cancer drug response, but applications using this model have typically focused on single drugs. Combination therapy is quite common in modern chemotherapy treatment since drugs often work synergistically, and it is an important progression in the use of the LCL model to expand work for drug combinations. We demonstrated that synergy occurs and can be quantified in LCLs across a range of clinically important drug combinations. Here we used cell lines from extended pedigrees to demonstrate that there is a substantial heritable component to synergistic drug response. This demonstration supports the premise of expanding the use of the LCL model to perform association mapping for combination therapies. This study also presents a number of statistical/computational challenges. With my recently graduated PhD student Dr. Jun Ma we developed an approach that addresses challenges in nonlinear dose-response relationships. Nonlinear dose-response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Nonlinear dose-response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Therefore, we propose the use of an EA for dose-response modeling for a range of potential response model functional forms. This new method can not only fit the most commonly used nonlinear dose-response models (eg, exponential models and 3-, 4-, and 5-parameter logistic models) but also select the best model if no model assumption is made, which is especially useful in the case of high-throughput curve fitting. An R package to implement the method was developed. Also, with Dr. Farida, we used the multivariate analysis of variance method to identify in vivo variables associated with in vitro dose response in a dataset from collaborators at UNC Chapel Hill. In a new study that includes both in vivo and in vitro data from breast cancer patients, race and smoking status were found to be significantly associated with in vitro dose response in LCLs. The smoking status of the donor individuals, from whom the LCLs are created, is usually unknown and hence not controlled for in dose response analyses in LCLs. Further research is required to understand the mechanism by which exposure to smoking in vivo affects in vitro dose response in LCLs. We also recently completed a high throughput screen of 44 anti-cancer drugs in this model. Cancer patients exhibit a broad range of inter-individual variability in response and toxicity to several widely used anticancer drugs. Genetic association mapping can be used to understand the genetic etiology of cancer drug response by identifying genes related to differential response. To identify novel genes that influence the response of 44 FDA-approved anticancer drugs widely used to treat various different types of cancer, we screened 680 lymphoblastoid cell lines from the racially and ethnically diverse 1000 Genomes Project with these drugs. Our genome-wide association mapping identified several novel genetic variants associated with the response of a broad range of anticancer drugs. We conducted further analyses and functional validation for one of the genes from our association mapping results, NAD H quinone dehydrogenase, to identify the mechanism of action by which it influences drug response. Ongoing work is continuing on several fronts. We are also continuing the GWAS mapping to a new class of drugs monoclonal antibody treatment.