A simple gene set-based method accurately predicts the synergy of drug pairs.
A simple gene set-based method accurately predicts the synergy of drug pairs.
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DOI：
10.1186/s12918-016-0310-3
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发表时间：
2016-08-26
影响因子：
--
通讯作者：
Chuang EY
中科院分区：
文献类型：
--
作者：
Hsu YC;Chiu YC;Chen Y;Hsiao TH;Chuang EY
靶向治疗的进展大大提高了临床癌症治疗的有效性，降低了治疗对正常细胞的细胞毒性。然而，由于耐药的发生，患者仍然会出现癌症复发。由于单一药物可能不足以抑制癌症成瘾基因或途径的持续激活，因此非常需要探索潜在的联合药物治疗。DREAM挑战证实了计算方法预测协同药物组合的潜力，同时预测精度还可以进一步提高。根据以往的报道，我们假设两种药物在生物学功能或基因上的相似性可以决定它们的协同作用。为了验证这一假设的可行性，我们提出了三种评分系统：共基因评分、共GS评分和共基因/GS评分，分别测量一对药物之间显著表达变化基因、富集基因集和富集基因集内显著变化基因的相似性。这些评分系统的性能由DREAM联盟设计的概率c指数（PC-index）进行评估。我们还将提出的方法应用于Connectivity Map数据集，以探索更多潜在的协同药物组合。采用DREAM联盟导出的金标准，我们证实了三种评分系统的预测能力（p值均< 0.05）。共基因/GS评分对药物协同作用的预测效果最好（PC-index = 0.663， p值< 0.0001），优于DREAM挑战期间提出的所有方法。此外，二分类测试表明，共基因/GS评分具有很高的准确性和特异性。由于我们的方法是建立在基于基因集的分析基础上的，除了协同作用预测之外，它还提供了对药物组合的功能相关性和药物实现协同作用的潜在机制的见解。本文提出了一种新颖、简便的药物协同作用预测和研究方法，并验证了其准确预测协同药物组合的有效性，全面探索其潜在机制。该方法可广泛应用于其他药物治疗的表达谱分析，有望加速癌症精准治疗的实现。本文的在线版本（doi:10.1186/s12918-016-0310-3）包含补充材料，可供授权用户使用。
The advance in targeted therapy has greatly increased the effectiveness of clinical cancer therapy and reduced the cytotoxicity of treatments to normal cells. However, patients still suffer from cancer relapse due to the occurrence of drug resistance. It is of great need to explore potential combinatorial drug therapy since individual drug alone may not be sufficient to inhibit continuous activation of cancer-addicted genes or pathways. The DREAM challenge has confirmed the potentiality of computational methods for predicting synergistic drug combinations, while the prediction accuracy can be further improved. Based on previous reports, we hypothesized the similarity in biological functions or genes perturbed by two drugs can determine their synergistic effects. To test the feasibility of the hypothesis, we proposed three scoring systems: co-gene score, co-GS score, and co-gene/GS score, measuring the similarities in genes with significant expressional changes, enriched gene sets, and significantly changed genes within an enriched gene sets between a pair of drugs, respectively. Performances of these scoring systems were evaluated by the probabilistic c-index (PC-index) devised by the DREAM consortium. We also applied the proposed method to the Connectivity Map dataset to explore more potential synergistic drug combinations. Using a gold standard derived by the DREAM consortium, we confirmed the prediction power of the three scoring systems (all P-values < 0.05). The co-gene/GS score achieved the best prediction of drug synergy (PC-index = 0.663, P-value < 0.0001), outperforming all methods proposed during DREAM challenge. Furthermore, a binary classification test showed that co-gene/GS scoring was highly accurate and specific. Since our method is constructed on a gene set-based analysis, in addition to synergy prediction, it provides insights into the functional relevance of drug combinations and the underlying mechanisms by which drugs achieve synergy. Here we proposed a novel and simple method to predict and investigate drug synergy, and validated its efficacy to accurately predict synergistic drug combinations and to comprehensively explore their underlying mechanisms. The method is widely applicable to expression profiles of other drug treatments and is expected to accelerate the realization of precision cancer treatment. The online version of this article (doi:10.1186/s12918-016-0310-3) contains supplementary material, which is available to authorized users.
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DOI：
10.1093/bioinformatics/btu278
发表时间：
2014-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Huang L;Li F;Sheng J;Xia X;Ma J;Zhan M;Wong ST
通讯作者：
Wong ST
影响因子：
20.3
作者：
Gupta, Mamta;Ansell, Stephen M.;Witzig, Thomas E.
通讯作者：
Witzig, Thomas E.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
8.6
作者：
Ryall KA;Tan AC
通讯作者：
Tan AC
影响因子：
7
作者：
Draghici, Sorin;Khatri, Purvesh;Romero, Roberto
通讯作者：
Romero, Roberto