Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties.
Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties.
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DOI：
10.1371/journal.pone.0061318
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Saez-Rodriguez J
中科院分区：
文献类型：
--
作者：
Menden MP;Iorio F;Garnett M;McDermott U;Benes CH;Ballester PJ;Saez-Rodriguez J
预测特定癌症对治疗的反应是现代肿瘤学的一个主要目标，最终应该导致个性化治疗。针对一组基因组异质性癌细胞系的潜在活性化合物的高通量筛选揭示了基因组改变和药物反应之间的多种关系。已经提出了各种计算方法来预测基于基因组特征的敏感性，而其他方法则使用药物的化学性质来确定其效果。为了整合这些互补的方法，我们开发了机器学习模型来预测癌细胞系对药物治疗的反应，通过IC50值量化，基于细胞系的基因组特征和所考虑药物的化学性质。模型在8倍交叉验证和独立盲法检验中预测了IC50值，决定系数R2分别为0.72和0.64。此外，模型能够以相当的准确度（R2为0.61）预测来自训练阶段未使用的组织的细胞系的IC 50。我们的计算机模型可用于优化药物细胞筛选的实验设计，方法是估计大部分缺失的IC50值，而不是通过实验测量它们。我们的研究结果的意义超越了虚拟药物筛选设计：可能有数千种药物可以在计算机上进行探测，以系统地测试它们作为基于其结构的抗肿瘤药物的潜在功效，从而提供一个计算框架来识别新的药物重新定位机会，并最终通过将患者的基因组特征与药物敏感性联系起来，用于个性化医疗。
Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.
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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：
11.2
作者：
Reinhold WC;Sunshine M;Liu H;Varma S;Kohn KW;Morris J;Doroshow J;Pommier Y
通讯作者：
Pommier Y
影响因子：
7.4
作者：
Iorio, Francesco;Rittman, Timothy;Ge, Hong;Menden, Michael;Saez-Rodriguez, Julio
通讯作者：
Saez-Rodriguez, Julio
DOI：
10.1073/pnas.1018854108
发表时间：
2012-02-21
影响因子：
11.1
作者：
Heiser, Laura M.;Sadanandam, Anguraj;Spellman, Paul T.
通讯作者：
Spellman, Paul T.
影响因子：
46.9
作者：
Kutalik, Zoltan;Beckmann, Jacques S.;Bergmann, Sven
通讯作者：
Bergmann, Sven