In vitro human cell line models to predict clinical response to anticancer drugs.
In vitro human cell line models to predict clinical response to anticancer drugs.
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DOI：
10.2217/pgs.14.170
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发表时间：
2015
期刊：
影响因子：
2.1
通讯作者：
Wang L
中科院分区：
文献类型：
--
作者：
Niu N;Wang L
体外人细胞系模型已广泛用于癌症药物基因组学研究，以预测临床反应，帮助生成药物基因组学假设以进行进一步测试，并帮助识别与药物反应变化相关的新机制。在细胞系模型系统中，永生化细胞系如EB病毒（EBV）转化的淋巴母细胞样细胞系（LCL）最常用于测试生殖系遗传变异对药物功效和毒性的影响。另一种模型，特别是在癌症研究中，使用癌细胞系，如NCI-60面板。这些模型主要用于确定体细胞改变对抗癌治疗反应的影响。尽管这些细胞系模型系统对于初始筛选非常有用，但是使用细胞系模型系统对多个组学数据和药物应答表型进行综合分析的结果仍然需要通过功能验证和机制研究以及使用临床样品的验证研究来确认。未来的模型可能包括使用患者特异性诱导多能干细胞和3D培养，这可以进一步优化体外细胞系模型，以提高其预测有效性。
In vitro human cell line models have been widely used for cancer pharmacogenomic studies to predict clinical response, to help generate pharmacogenomic hypothesis for further testing, and to help identify novel mechanisms associated with variation in drug response. Among cell line model systems, immortalized cell lines such as Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines (LCLs) have been used most often to test the effect of germline genetic variation on drug efficacy and toxicity. Another model, especially in cancer research, uses cancer cell lines such as the NCI-60 panel. These models have been used mainly to determine the effect of somatic alterations on response to anticancer therapy. Even though these cell line model systems are very useful for initial screening, results from integrated analyses of multiple omics data and drug response phenotypes using cell line model systems still need to be confirmed by functional validation and mechanistic studies, as well as validation studies using clinical samples. Future models might include the use of patient-specific inducible pluripotent stem cells and the incorporation of 3D culture which could further optimize in vitro cell line models to improve their predictive validity.
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来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
--
DOI：
10.1073/pnas.0808757105
发表时间：
2008-12-30
影响因子：
11.1
作者：
Di Nicolantonio, Federica;Arena, Sabrina;Bardelli, Alberto
通讯作者：
Bardelli, Alberto
影响因子：
4.5
作者：
Choy E;Yelensky R;Bonakdar S;Plenge RM;Saxena R;De Jager PL;Shaw SY;Wolfish CS;Slavik JM;Cotsapas C;Rivas M;Dermitzakis ET;Cahir-McFarland E;Kieff E;Hafler D;Daly MJ;Altshuler D
通讯作者：
Altshuler D
影响因子：
5.7
作者：
Blower, Paul E.;Verducci, Joseph S.;Sadee, Wolfgang
通讯作者：
Sadee, Wolfgang
影响因子：
2.6
作者：
Brown CC;Havener TM;Medina MW;Auman JT;Mangravite LM;Krauss RM;McLeod HL;Motsinger-Reif AA
通讯作者：
Motsinger-Reif AA