A pharmacogenomic method for individualized prediction of drug sensitivity.
A pharmacogenomic method for individualized prediction of drug sensitivity.
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DOI：
10.1038/msb.2011.47
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发表时间：
2011-07-19
影响因子：
9.9
通讯作者：
中科院分区：
文献类型：
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作者：
以丙戊酸为例，作者证明了来自全基因组表达数据的药物反应特征可以识别可能对药物有反应的个体，并提出这种方法可以为新疗法的临床试验选择最佳人群。为每个癌症患者确定最佳药物需要有效的个性化策略。我们提出了MATCH（将基因组和药理学分析合并用于治疗选择），这是一种使用公共基因组资源和新鲜肿瘤样本的药物测试将药物与患者联系起来的方法。丙戊酸（VPA）作为原理证明被强调。为了预测具有高药物敏感性概率的特定肿瘤类型，我们使用可获得的基因表达数据创建药物反应特征，并在>40种癌症类型的数据集中评估敏感性。接下来，我们评估匹配的肿瘤和正常组织中的药物敏感性，并排除不比正常组织更敏感的癌症类型。根据这些分析，预测乳腺肿瘤对VPA敏感。对乳腺癌数据集的荟萃分析显示，侵袭性亚型最有可能对VPA敏感，但所有亚型都有敏感的肿瘤。MATCH预测与癌细胞系和新鲜肿瘤样品的三维培养物中的生长抑制显著相关。MATCH可准确预测VPA治疗患者肿瘤异种移植物后肿瘤生长速率的降低。MATCH使用基因组分析和患者肿瘤的体外测试，在临床试验开始前选择最佳药物方案。
Using valproic acid as an example, the authors demonstrate that drug response signatures derived from genome-wide expression data can identify individuals likely to respond to a drug, and propose that this method could select optimal populations for clinical trials of new therapies. Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof-of-principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta-analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three-dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.
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影响因子：
46.9
作者：
Du, Jinyan;Bernasconi, Paula;Clauser, Karl R.;Mani, D. R.;Finn, Stephen P.;Beroukhim, Rameen;Burns, Melissa;Julian, Bina;Peng, Xiao P.;Hieronymus, Haley;Maglathlin, Rebecca L.;Lewis, Timothy A.;Liau, Linda M.;Nghiemphu, Phioanh;Mellinghoff, Ingo K.;Louis, David N.;Loda, Massimo;Carr, Steven A.;Kung, Andrew L.;Golub, Todd R.
通讯作者：
Golub, Todd R.
影响因子：
64.8
作者：
Bild, AH;Yao, G;Nevins, JR
通讯作者：
Nevins, JR
影响因子：
4.4
作者：
Hu, Zhiyuan;Fan, Cheng;Perou, Charles M.
通讯作者：
Perou, Charles M.
影响因子：
11.2
作者：
Huang, Fei;Reeves, Karen;Clark, Edwin
通讯作者：
Clark, Edwin
DOI：
10.1056/nejmoa1002011
发表时间：
2010-08-26
期刊：
The New England journal of medicine
影响因子：
--
作者：
Flaherty KT;Puzanov I;Kim KB;Ribas A;McArthur GA;Sosman JA;O'Dwyer PJ;Lee RJ;Grippo JF;Nolop K;Chapman PB
通讯作者：
Chapman PB