Zhang Wei, Zheng Yinan, Hou Lifang
Department of Pediatrics, University of Illinois, Chicago, Illinois, USA ; Institute of Human Genetics, University of Illinois, Chicago, Illinois, USA ; University of Illinois Cancer Center, Chicago, Illinois, USA.
Curr Genet Med Rep. 2013 Sep 1;1(3):143-149. doi: 10.1007/s40142-013-0019-1.
Personalized medicine has the promise to tailor medical care based on the patient's genetic make-up and clinical variables such as gender, race and exposure to environmental stimuli. Recent progress in pharmacogenetic and pharmacogenomic studies has suggested that drug response to therapeutic treatments is likely a complex trait influenced by a variety of genetic and non-genetic factors. Identifying molecular targets (e.g., genetic variants) delineating the genetic basis of drug response could help understand the complex nature of drug response. The last decade has witnessed significant advances in genome-wide profiling technologies for genetic/epigenetic variations and gene expression. As an unbiased, cell-based model for pharmacogenomic discovery, a tremendous resource of whole-genome molecular targets has been accumulated for the HapMap lymphoblastoid cell lines (LCLs) during the past decade. The current progress, particularly in cancer pharmacogenomics, using the LCL model was reviewed to illustrate the potential impact of systems biology approaches on pharmacogenomic discovery.
个性化医疗有望根据患者的基因构成以及性别、种族和环境刺激暴露等临床变量来定制医疗护理。药物遗传学和药物基因组学研究的最新进展表明，对治疗性治疗的药物反应可能是一种受多种遗传和非遗传因素影响的复杂性状。识别描绘药物反应遗传基础的分子靶点（例如基因变异）有助于理解药物反应的复杂本质。过去十年见证了用于遗传/表观遗传变异和基因表达的全基因组分析技术取得的重大进展。作为药物基因组学发现的一种无偏差的基于细胞的模型，在过去十年中，HapMap淋巴母细胞系（LCLs）积累了大量全基因组分子靶点资源。本文回顾了利用LCL模型取得的当前进展，特别是在癌症药物基因组学方面的进展，以说明系统生物学方法对药物基因组学发现的潜在影响。
