DrugComboRanker: drug combination discovery based on target network analysis.
DrugComboRanker: drug combination discovery based on target network analysis.
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DOI：
10.1093/bioinformatics/btu278
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发表时间：
2014-06-15
期刊：
影响因子：
--
通讯作者：
Wong ST
中科院分区：
文献类型：
--
作者：
Huang L;Li F;Sheng J;Xia X;Ma J;Zhan M;Wong ST
研究动机：目前没有治愈性的抗癌药物，药物治疗后往往获得耐药性。原因之一是癌症是一种复杂的疾病，受多种信号通路和通路之间的交叉作用的调节。预计药物组合可以减少耐药性并改善患者的结局。在临床实践中，理想和可行的药物组合是现有食品和药物管理局批准的药物或生物活性化合物的组合，这些药物或生物活性化合物已经用于患者或已经进入临床试验并通过安全性测试。这些药物组合可以直接用于患者，而不必担心毒性作用。然而，到目前为止，还没有有效的计算方法来从大量的可能性中搜索有效的药物组合。结果如下：在这项研究中，我们提出了一种新的系统计算工具DrugComboRanker，以优先考虑协同药物组合并揭示其作用机制。我们首先建立一个药物功能网络的基础上，他们的基因组图谱，并划分网络成许多药物网络社区，使用贝叶斯非负矩阵分解方法。由于重叠社区内的药物具有共同的作用机制，我们接下来通过对药物社区应用推荐系统来发现药物的潜在靶点。同时，我们根据患者的基因组图谱和相互作用组数据构建疾病特异性信号网络。然后，我们通过搜索靶点富含疾病信号网络互补信号模块的药物来确定药物组合。在肺腺癌和内分泌受体阳性乳腺癌中评估了新方法，并与其他药物组合方法进行了比较。这些案例研究发现了一组在我们的预测列表中排名靠前的有效药物组合，并将药物靶点映射到疾病信号网络上，以突出药物组合的作用机制。可用性和实施：该方案可应要求提供。联系方式：stwong@tmhs.org
Motivation: Currently there are no curative anticancer drugs, and drug resistance is often acquired after drug treatment. One of the reasons is that cancers are complex diseases, regulated by multiple signaling pathways and cross talks among the pathways. It is expected that drug combinations can reduce drug resistance and improve patients’ outcomes. In clinical practice, the ideal and feasible drug combinations are combinations of existing Food and Drug Administration-approved drugs or bioactive compounds that are already used on patients or have entered clinical trials and passed safety tests. These drug combinations could directly be used on patients with less concern of toxic effects. However, there is so far no effective computational approach to search effective drug combinations from the enormous number of possibilities. Results: In this study, we propose a novel systematic computational tool DrugComboRanker to prioritize synergistic drug combinations and uncover their mechanisms of action. We first build a drug functional network based on their genomic profiles, and partition the network into numerous drug network communities by using a Bayesian non-negative matrix factorization approach. As drugs within overlapping community share common mechanisms of action, we next uncover potential targets of drugs by applying a recommendation system on drug communities. We meanwhile build disease-specific signaling networks based on patients’ genomic profiles and interactome data. We then identify drug combinations by searching drugs whose targets are enriched in the complementary signaling modules of the disease signaling network. The novel method was evaluated on lung adenocarcinoma and endocrine receptor positive breast cancer, and compared with other drug combination approaches. These case studies discovered a set of effective drug combinations top ranked in our prediction list, and mapped the drug targets on the disease signaling network to highlight the mechanisms of action of the drug combinations. Availability and implementation: The program is available on request. Contact: stwong@tmhs.org
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影响因子：
5.8
作者：
Saric, J;Jensen, LJ;Bork, P
通讯作者：
Bork, P
DOI：
10.2202/1544-6115.1534
发表时间：
2010-01-01
影响因子：
0.9
作者：
Lin, Shili
通讯作者：
Lin, Shili
影响因子：
9.9
作者：
通讯作者：
--
DOI：
10.1109/tpami.2012.240
发表时间：
2013-07-01
影响因子：
23.6
作者：
Tan, Vincent Y. F.;Fevotte, Cedric
通讯作者：
Fevotte, Cedric
DOI：
10.1111/j.2517-6161.1977.tb01624.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DIACONIS, P;GRAHAM, RL
通讯作者：
GRAHAM, RL