Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection.
Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection.
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通过递归特征选择从基线基因表达预测细胞系中的抗癌药物敏感性
DOI：
10.1186/s12885-015-1492-6
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发表时间：
2015-06-30
期刊：
影响因子：
3.8
通讯作者：
Zheng X
中科院分区：
文献类型：
--
作者：
Dong Z;Zhang N;Li C;Wang H;Fang Y;Wang J;Zheng X
个性化医疗的一个持久挑战是为个体患者选择正确的药物。在大型临床试验中对患者进行药物测试是评估其疗效和毒性的一种方法，但测试目前正在开发的数百种药物是不切实际的。因此，临床前预测模型备受期待，因为它能够并行预测对数百种细胞系的药物反应。最近，两项大规模的药物基因组研究在1000多个细胞系上筛选了多种抗癌药物，以期阐明抗癌药物的反应机制。为此，我们使用癌细胞系百科全书（CCLE）中的基因表达特征和药物敏感性数据来构建基于支持向量机（SVM）和递归特征选择工具的预测器。我们模型的稳健性通过交叉验证和独立数据集癌症基因组计划（CGP）进行了验证。我们的模型对癌细胞系百科全书中的大多数药物都取得了良好的交叉验证性能（10 种药物的准确率≥80%，19 种药物的准确率≥75%）。对 CCLE 和 CGP 之间的 11 种常见药物进行独立测试，其中 3 种（AZD6244、厄洛替尼和 PD-0325901）分别仅使用 12、6 和 7 个基因的表达水平，取得了令人满意的性能。这些结果表明，可以根据基因组特征有效预测药物反应。我们的模型可用于预测某些药物的药物反应，并可能在个性化医疗中发挥补充作用。本文的在线版本 (doi:10.1186/s12885-015-1492-6) 包含补充材料，可供授权用户使用。
An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel. Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP). Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80 % accuracy for 10 drugs, ≥ 75 % accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively. These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine. The online version of this article (doi:10.1186/s12885-015-1492-6) contains supplementary material, which is available to authorized users.
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影响因子：
5.2
作者：
Lian, Zhen-Qiang;Wang, Qi;Wu, Ling
通讯作者：
Wu, Ling
影响因子：
5.1
作者：
Genuer, Robin;Poggi, Jean-Michel;Tuleau-Malot, Christine
通讯作者：
Tuleau-Malot, Christine
DOI：
10.1136/amiajnl-2012-001442
发表时间：
2013-07-01
影响因子：
6.4
作者：
Papillon-Cavanagh, Simon;De Jay, Nicolas;Haibe-Kains, Benjamin
通讯作者：
Haibe-Kains, Benjamin
影响因子：
64.8
作者：
Garnett, Mathew J.;Edelman, Elena J.;Heidorn, Sonja J.;Greenman, Chris D.;Dastur, Anahita;Lau, King Wai;Greninger, Patricia;Thompson, I. Richard;Luo, Xi;Soares, Jorge;Liu, Qingsong;Iorio, Francesco;Surdez, Didier;Chen, Li;Milano, Randy J.;Bignell, Graham R.;Tam, Ah T.;Davies, Helen;Stevenson, Jesse A.;Barthorpe, Syd;Lutz, Stephen R.;Kogera, Fiona;Lawrence, Karl;McLaren-Douglas, Anne;Mitropoulos, Xeni;Mironenko, Tatiana;Thi, Helen;Richardson, Laura;Zhou, Wenjun;Jewitt, Frances;Zhang, Tinghu;O'Brien, Patrick;Boisvert, Jessica L.;Price, Stacey;Hur, Wooyoung;Yang, Wanjuan;Deng, Xianming;Butler, Adam;Choi, Hwan Geun;Chang, JaeWon;Baselga, Jose;Stamenkovic, Ivan;Engelman, Jeffrey A.;Sharma, Sreenath V.;Delattre, Olivier;Saez-Rodriguez, Julio;Gray, Nathanael S.;Settleman, Jeffrey;Futreal, P. Andrew;Haber, Daniel A.;Stratton, Michael R.;Ramaswamy, Sridhar;McDermott, Ultan;Benes, Cyril H.
通讯作者：
Benes, Cyril H.
影响因子：
12.3
作者：
Geeleher P;Cox NJ;Huang RS
通讯作者：
Huang RS