Genetic Etiology of Cancer Drug Response

描述（由申请人提供）：在大多数常见癌症的治疗中继续取得重要进展，但治疗益处仍然难以预测，严重或致命的不良事件频繁发生。人类基因组计划推动了遗传信息可以为个体患者提供有效且具有成本效益的治疗选择的概念，但预测对大多数化疗方案的反应的经验证的遗传特征仍有待确定。许多基因可能影响药物反应，但目前的候选基因方法受到有关所涉及基因的先验知识的要求的限制，并且大多数临床试验的中等规模通常限制了体外全基因组关联研究（GWAS）用于癌症药物基因组学发现的能力。针对这些局限性，我们使用离体模型系统对大多数FDA批准的抗癌化合物的细胞毒性作用进行了全面的药物基因组学评估，以确定药物诱导的细胞杀伤的遗传性，从而优先考虑药物基因组学图谱。这些结果是重要的第一步，虽然性状的高遗传性并不能保证成功的关联作图结果，但它代表了重要的第一步，其结果将用于优先考虑具有高遗传性的药物进行全基因组关联作图。在当前提案中，将在欧洲裔美国人人群中进行细胞毒性药物的GWAS图谱，然后在东亚人群中进行复制GWAS图谱。除了发现和验证预测药物反应的遗传变异外，所收集的大量数据将用于剖析药物反应性状的潜在病因，包括评估遗传、环境和相互作用成分对变异的相对贡献。这些结果将为在宝贵的临床人群资源中优先考虑遗传变异以进行随访提供重要的见解，并可能揭示对药物反应的总体病因学的新见解。 公共卫生相关性：我们将在我们以前的工作的基础上，在两个大的、独立的人群队列中对药物反应表型进行离体GWAS研究，并使用尖端的统计方法来剖析这些性状的遗传病因。我们的总体目标是确定高兴趣的基因，并描述这些药物反应结果的特征病因，以便在未来的研究中进一步研究。该应用程序利用先前完成的全基因组基因分型进行有效的关联映射，并将在两个独立的队列中评估遗传关联。

DESCRIPTION (provided by applicant): Important progress continues to be made in the treatment of most common cancers, but therapeutic benefit remains difficult to predict and severe or fatal adverse events occur frequently. The Human Genome Project has fueled the notion that genetic information can produce effective and cost-efficient selection of therapies for individual patients, but validated genetic signatures that predict response to most chemotherapy regimens remain to be identified. Numerous genes potentially influence drug response, but current candidate-gene approaches are limited by the requirement of a priori knowledge about the genes involved and the moderate size of most clinical trials often limits the power of in vitro genome wide association studies (GWAS) for cancer pharmacogenomics discovery. In response to these limitations, we have undertaken a thorough, pharmacogenomic assessment of cytotoxic effect of the majority of FDA approved anti-cancer compounds using an ex vivo model system to determine the heritability of drug-induced cell killing to prioritize drugs for pharmacogenomic mapping. These results are an important first step, and while high heritability of a trait does not guarantee successful association mapping results, it represents an important first step and the results will be used to prioritize drugs with high heritabilities for genome-wide association mapping. In the current proposal, GWAS mapping of cytotoxic agents will be performed in a European American population, and then replication GWAS mapping will be performed in an East Asian population. In addition to discovering and validating genetic variants that predict drug response, the wealth of data collected will be used to dissect the underlying etiology of drug response traits, including assessing the relative contribution of genetic, environmental, and interaction components of variation. These results will provide crucial insight to prioritize genetic variants for follow-up in precious clinical population resources, and potentially reveal new insight into the overall etiology of drug responses. PUBLIC HEALTH RELEVANCE: We will build on our previous work to conduct ex vivo GWAS studies in two large, independent population cohorts on drug response phenotypes, and use cutting-edge statistical approaches to dissect the genetic etiology of these traits. Our overall goal is to identify high interest genes and characterize the trait etiology of these drug response outcomes so that they may be further investigated in future studies. This application leverages previously completed genome-wide genotyping for efficient association mapping, and will evaluate genetic associations in two independent cohorts.