摘要
药品的新标准正在形成,旨在通过研究遗传变异关联,以提高个人对药物的反应。由于一系列低投入、高产出的先进技术,药物基因组学这一领域正在迅速发展。在这里,我们概述了使用淋巴母细胞系(LCL)作为人类药物基因组学研究的模式系统的优势和局限性。 这里有广泛的公开可用的资源,如从相关和不相关的人群的全组基因数据中可获得淋巴母细胞系,这消除了药物反应研究的数据基因分型的成本。此外,与人的临床试验或在体内模型系统相比,药物基因组学研究在体外高通量筛选技术下可以很容易地扩展,以适应大样本。 在淋巴母细胞系模型中关联映射是一个重要的组成部分以扩充全组基因数据。在这儿讨论了几种方法,包括多元浓度响应模型、多重检测试验及已成功的“三角模型”来识别候选变体。一旦确定候选基因变异,它们的生物学作用可通过通路分析阐明,可利用siRNA敲除实验来证实其功能。 通过相关和不相关的人口产生的基因组学数据,它的财富是正在创造许多令人兴奋的机会,这将引起对遗传基因和遗传药物反应新的深刻见解。
关键词: 细胞毒素,基因关联映射,基因组广泛的关联分析,淋巴母细胞系,药物基因组学
Current Molecular Medicine
Title:Lymphoblastoid Cell Lines Models of Drug Response: Successes and Lessons from this Pharmacogenomic Model
Volume: 14 Issue: 7
Author(s): J. Jack, D. Rotroff and A. Motsinger-Reif
Affiliation:
关键词: 细胞毒素,基因关联映射,基因组广泛的关联分析,淋巴母细胞系,药物基因组学
摘要: A new standard for medicine is emerging that aims to improve individual drug responses through studying associations with genetic variations. This field, pharmacogenomics, is undergoing a rapid expansion due to a variety of technological advancements that are enabling higher throughput with reductions in cost. Here we review the advantages, limitations, and opportunities for using lymphoblastoid cell lines (LCL) as a model system for human pharmacogenomic studies.
There are a wide range of publicly available resources with genome-wide data available for LCLs from both related and unrelated populations, removing the cost of genotyping the data for drug response studies. Furthermore, in contrast to human clinical trials or in vivo model systems, with high-throughput in vitro screening technologies, pharmacogenomics studies can easily be scaled to accommodate large sample sizes.
An important component to leveraging genome-wide data in LCL models is association mapping. Several methods are discussed herein, and include multivariate concentration response modeling, issues with multiple testing, and successful examples of the ‘triangle model’ to identify candidate variants. Once candidate gene variants have been determined, their biological roles can be elucidated using pathway analyses and functionally confirmed using siRNA knockdown experiments.
The wealth of genomics data being produced using related and unrelated populations is creating many exciting opportunities leading to new insights into the genetic contribution and heritability of drug response.
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Cite this article as:
Jack J., Rotroff D. and Motsinger-Reif A., Lymphoblastoid Cell Lines Models of Drug Response: Successes and Lessons from this Pharmacogenomic Model, Current Molecular Medicine 2014; 14 (7) . https://dx.doi.org/10.2174/1566524014666140811113946
DOI https://dx.doi.org/10.2174/1566524014666140811113946 |
Print ISSN 1566-5240 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5666 |
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