Prioritizing therapeutics for lung cancer: an integrative meta-analysis of cancer gene signatures and chemogenomic data.
Prioritizing therapeutics for lung cancer: an integrative meta-analysis of cancer gene signatures and chemogenomic data.
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DOI：
10.1371/journal.pcbi.1004068
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发表时间：
2015-03
影响因子：
4.3
通讯作者：
Jurisica I
中科院分区：
文献类型：
--
作者：
Fortney K;Griesman J;Kotlyar M;Pastrello C;Angeli M;Sound-Tsao M;Jurisica I
在疾病基因特征的帮助下重新利用FDA批准的药物可以加速新疗法的开发。开发可靠的药物预测的一个主要挑战是异质性。由于生物学和技术的差异性，同一疾病或药物治疗的不同基因特征在研究中往往表现出较差的重叠性，这可能会影响计算药物预测的质量和可重复性。基于签名的药物再利用的现有算法仅使用个体签名作为输入。但对于许多疾病，在公共领域有几十个签名。利用所有可用的疾病转录知识的方法应该产生改进的药物预测。在这里，我们采用了一个既定的荟萃分析框架，以解决使用疾病签名合奏的药物再利用的问题。我们的计算管道将一组疾病特征作为输入，并输出一系列预测可持续逆转病理基因变化的药物。我们应用我们的方法对肺癌转录组进行了最大和最系统的再利用研究，使用了21个签名。我们发现，扩大转录知识显着增加了顶级药物命中的再现性，从44%到78%。我们在计算机上广泛表征了药物命中，证明它们在NCI-60集合的9种肺癌细胞系中显着减缓生长，并将CALM 1和PLA 2G 4A确定为肺癌的有希望的药物靶点。我们的荟萃分析管道是通用的，适用于任何疾病背景;它可以通过利用公共领域中的大量疾病特征来改善基于特征的药物再利用的结果。为已知药物找到新用途的计算机算法可以加速包括癌症在内的许多疾病的新疗法的开发。一个有希望的策略是确定在转录水平上逆转疾病基因表达特征的药物。这种策略的一个主要困难是变异性：同一疾病或药物治疗的不同基因表达特征可能在研究中表现出很差的重叠。由于现有的算法一次分析一个特征，这意味着它们识别的候选药物可能会逆转某种疾病的某些特征，但不会逆转其他特征。对于许多疾病，来自不同实验室的数十个签名现在可以在在线数据库中获得。将所有特征的知识结合起来应该会导致更好的药物预测。在这里，我们设计了一个荟萃分析管道，该管道接收了大量的疾病特征，然后识别出持续逆转有害基因变化的药物。我们应用我们的方法来寻找肺癌的新候选药物，使用21个签名。我们表明，我们的荟萃分析管道增加了顶级药物命中的再现性，然后在计算机上广泛表征新的肺癌候选药物。
Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain. Computer algorithms that find new uses for known drugs can accelerate the development of new therapies for many diseases, including cancer. One promising strategy is to identify drugs that, at the transcriptional level, reverse the gene expression signature of a disease. A major difficulty with this strategy is variability: different gene expression signatures of the same disease or drug treatment can show poor overlap across studies. Since existing algorithms analyze one signature at a time, this means that the drug candidates they identify may reverse some signatures of a disease but not others. For many diseases, dozens of signatures from different labs are now available in online databases. Combining knowledge across all signatures should lead to better drug predictions. Here, we design a meta-analysis pipeline that takes in a large set of disease signatures and then identifies drugs that consistently reverse deleterious gene changes. We apply our method to find new drug candidates for lung cancer, using 21 signatures. We show that our meta-analysis pipeline increases the reproducibility of top drug hits, and then extensively characterize new lung cancer drug candidates in silico.
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影响因子：
29
作者：
Kunkel SD;Suneja M;Ebert SM;Bongers KS;Fox DK;Malmberg SE;Alipour F;Shields RK;Adams CM
通讯作者：
Adams CM
影响因子：
3.7
作者：
McArt DG;Zhang SD
通讯作者：
Zhang SD
影响因子：
5.3
作者：
Fortney K;Jurisica I
通讯作者：
Jurisica I
影响因子：
5.8
作者：
Hayat, Matthew J.;Howlader, Nadia;Edwards, Brenda K.
通讯作者：
Edwards, Brenda K.
影响因子：
11.2
作者：
Ebi, Hiromichi;Tomida, Shuta;Takahashi, Takashi
通讯作者：
Takahashi, Takashi