Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
Machine Learning Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
BMC Cancer. 2019 Apr 23;19(1):370. doi: 10.1186/s12885-019-5532-5.
Most methods that integrate network and mutation data to study cancer focus on the effects of genes/proteins, quantifying the effect of mutations or differential expression of a gene and its neighbors, or identifying groups of genes that are significantly up- or down-regulated. However, several mutations are known to disrupt specific protein-protein interactions, and network dynamics are often ignored by such methods. Here we introduce a method that allows for predicting the disruption of specific interactions in cancer patients using somatic mutation data and protein interaction networks.
We extend standard network smoothing techniques to assign scores to the edges in a protein interaction network in addition to nodes. We use somatic mutations as input to our modified network smoothing method, producing scores that quantify the proximity of each edge to somatic mutations in individual samples.
Using breast cancer mutation data, we show that predicted edges are significantly associated with patient survival and known ligand binding site mutations. In-silico analysis of protein binding further supports the ability of the method to infer novel disrupted interactions and provides a mechanistic explanation for the impact of mutations on key pathways.
Our results show the utility of our method both in identifying disruptions of protein interactions from known ligand binding site mutations, and in selecting novel clinically significant interactions. Supporting website with software and data: https://www.cs.cmu.edu/~mruffalo/mut-edge-disrupt/ .
大多数整合网络和突变数据来研究癌症的方法都侧重于基因/蛋白质的影响，量化基因突变或基因及其邻居的差异表达的效果，或识别显著上调或下调的基因群。然而，已知有几种突变会破坏特定的蛋白质-蛋白质相互作用，而这些方法往往忽略了网络动态。在这里，我们介绍了一种使用体细胞突变数据和蛋白质相互作用网络来预测癌症患者特定相互作用中断的方法。
我们将标准网络平滑技术扩展到不仅对节点而且对蛋白质相互作用网络中的边分配分数。我们将体细胞突变作为输入应用于我们修改后的网络平滑方法，生成的分数量化了每个边与个体样本中体细胞突变的接近程度。
使用乳腺癌突变数据，我们表明预测的边缘与患者的生存和已知的配体结合位点突变显著相关。蛋白质结合的计算机分析进一步支持了该方法推断新的破坏相互作用的能力，并为突变对关键途径的影响提供了机制解释。
我们的结果表明，我们的方法在识别已知配体结合位点突变引起的蛋白质相互作用中断以及选择新的具有临床意义的相互作用方面都具有实用性。软件和数据支持网站：https://www.cs.cmu.edu/~mruffalo/mut-edge-disrupt/ 。
