Predicting response to preoperative chemotherapy agents by identifying drug action on modeled microRNA regulation networks.
Predicting response to preoperative chemotherapy agents by identifying drug action on modeled microRNA regulation networks.
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DOI：
10.1371/journal.pone.0098140
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Xiong J
中科院分区：
文献类型：
--
作者：
Zhu L;Liu J;Liang F;Rayner S;Xiong J
在新辅助治疗中，确定对特定化疗药物最敏感的患者可以帮助最大限度地提高治疗效果，最大限度地减少不必要的副作用。基于临床数据关联分析得出的基因表达特征来预测疗效的Metagene方法可以识别由肿瘤异质性引起的偶然性关联，从而导致独立验证中的可重复性问题。在本研究中，为了整合药物作用机制的信息，我们探索了microRNA调控网络作为识别预测标记物的新特征空间的潜力。我们引入了一种我们称之为CoMi（上下文特异性miRNA调节）模式的测量方法，以代表转录组中miRNA调节网络的描述性特征。我们通过预测乳腺癌患者对新辅助紫杉醇治疗的反应，研究了特异性生物过程中CoMi模式的改变是否是药物作用的有用代表，并表明该药物抵消了肿瘤发生引起的CoMi网络失调。然后，我们创建了一个定量测试平台，以研究CoMi模式区分FDA批准的乳腺癌药物与其他FDA批准的与乳腺癌无关的药物的能力。我们还比较了CoMi和metagene方法在临床队列中预测新辅助紫杉醇治疗反应的能力。我们发现CoMi方法优于metagene方法，曲线下面积（AUC）分别为0.78和0.66。此外，一些预测的CoMi特征突出了基于网络的耐药机制。因此，我们的研究表明，使用网络生物学明确建模药物作用为预测性标记物发现提供了一种有前途的方法。
Identifying patients most responsive to specific chemotherapy agents in neoadjuvant settings can help to maximize the benefits of treatment and minimize unnecessary side effects. Metagene approaches that predict response based on gene expression signatures derived from an associative analysis of clinical data can identify chance associations caused by the heterogeneity of a tumor, leading to reproducibility issues in independent validations. In this study, to incorporate information from drug mechanisms of action, we explore the potential of microRNA regulation networks as a new feature space for identifying predictive markers. We introduce a measure we term the CoMi (Context-specific-miRNA-regulation) pattern to represent a descriptive feature of the miRNA regulation network in the transcriptome. We examine whether the modifications to the CoMi pattern on specific biological processes are a useful representation of drug action by predicting the response to neoadjuvant Paclitaxel treatment in breast cancer and show that the drug counteracts the CoMi network dysregulation induced by tumorigenesis. We then generate a quantitative testbed to investigate the ability of the CoMi pattern to distinguish FDA approved breast cancer drugs from other FDA approved drugs not related to breast cancer. We also compare the ability of the CoMi and metagene methods to predict response to neoadjuvant Paclitaxel treatment in clinical cohorts. We find the CoMi method outperforms the metagene method, achieving area under curve (AUC) values of 0.78 and 0.66 respectively. Furthermore, several of the predicted CoMi features highlight the network-based mechanism of drug resistance. Thus, our study suggests that explicitly modeling the drug action using network biology provides a promising approach for predictive marker discovery.
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