Brain Tumor Center, Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.
Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH 45229, USA.
Genes (Basel). 2019 Aug 9;10(8):602. doi: 10.3390/genes10080602.
With the advances in different biological networks including gene regulation, gene co-expression, protein-protein interaction networks, and advanced approaches for network reconstruction, analysis, and interpretation, it is possible to discover reliable and accurate molecular network-based biomarkers for monitoring cancer treatment. Such efforts will also pave the way toward the realization of biomarker-driven personalized medicine against cancer. Previously, we have reconstructed disease-specific driver signaling networks using multi-omics profiles and cancer signaling pathway data. In this study, we developed a network-based sparse Bayesian machine (NBSBM) approach, using previously derived disease-specific driver signaling networks to predict cancer cell responses to drugs. NBSBM made use of the information encoded in a disease-specific (differentially expressed) network to improve its prediction performance in problems with a reduced amount of training data and a very high-dimensional feature space. Sparsity in NBSBM is favored by a spike and slab prior distribution, which is combined with a Markov random field prior that encodes the network of feature dependencies. Gene features that are connected in the network are assumed to be both relevant and irrelevant to drug responses. We compared the proposed method with network-based support vector machine (NBSVM) approaches and found that the NBSBM approach could achieve much better accuracy than the other two NBSVM methods. The gene modules selected from the disease-specific driver networks for predicting drug sensitivity might be directly involved in drug sensitivity or resistance. This work provides a disease-specific network-based drug sensitivity prediction approach and can uncover the potential mechanisms of the action of drugs by selecting the most predictive sub-networks from the disease-specific network.
随着不同生物网络（包括基因调控、基因共表达、蛋白质-蛋白质相互作用网络以及用于网络重构、分析和解释的先进方法）的进步，有可能发现可靠且准确的基于分子网络的生物标志物，以监测癌症治疗。这些努力也将为实现基于生物标志物的癌症个性化医疗铺平道路。之前，我们已经使用多组学图谱和癌症信号通路数据重建了疾病特异性驱动信号网络。在这项研究中，我们开发了一种基于网络的稀疏贝叶斯机（NBSBM）方法，使用之前推导的疾病特异性驱动信号网络来预测癌细胞对药物的反应。NBSBM 利用疾病特异性（差异表达）网络中编码的信息来提高其在训练数据量减少且特征空间非常高维的问题中的预测性能。NBSBM 的稀疏性受到尖峰和板条先验分布的青睐，该分布与编码特征依赖性网络的马尔可夫随机场先验相结合。在网络中连接的基因特征被假定为与药物反应既相关又不相关。我们将所提出的方法与基于网络的支持向量机（NBSVM）方法进行了比较，发现 NBSBM 方法可以比其他两种 NBSVM 方法实现更高的准确性。从疾病特异性驱动网络中选择用于预测药物敏感性的基因模块可能直接参与药物敏感性或耐药性。这项工作提供了一种基于疾病特异性网络的药物敏感性预测方法，通过从疾病特异性网络中选择最具预测性的子网络，可以揭示药物作用的潜在机制。
