Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
Bioinformatics. 2018 Jul 1;34(13):i484-i493. doi: 10.1093/bioinformatics/bty247.
Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion of pathway signals and complicates the identification of cancer subtypes.
To address this problem we introduce a propagation algorithm, Network-Based Supervised Stratification (NBS2), which learns the mutated subnetworks underlying tumor subtypes using a supervised approach. Given an annotated molecular network and reference tumor mutation profiles for which subtypes have been predefined, NBS2 is trained by adjusting the weights on interaction features such that network propagation best recovers the provided subtypes. After training, weights are fixed such that mutation profiles of new tumors can be accurately classified. We evaluate NBS2 on breast and glioblastoma tumors, demonstrating that it outperforms the best network-based approaches in classifying tumors to known subtypes for these diseases. By interpreting the interaction weights, we highlight characteristic molecular pathways driving selected subtypes.
The NBS2 package is freely available at: https://github.com/wzhang1984/NBSS.
Supplementary data are available at Bioinformatics online.
网络传播已被广泛用于聚合和放大肿瘤突变的影响，利用分子相互作用网络的知识。然而，通过与癌症无关的相互作用传播突变会导致途径信号的侵蚀，并使癌症亚型的识别复杂化。
为了解决这个问题，我们引入了一种传播算法，即基于网络的监督分层（NBS2），它使用监督方法学习肿瘤亚型的突变子网络。给定一个注释的分子网络和参考肿瘤突变谱，其中已经预定义了亚型，NBS2 通过调整交互特征的权重进行训练，以便网络传播最好地恢复提供的亚型。训练后，权重被固定，以便可以准确地对新肿瘤的突变谱进行分类。我们在乳腺癌和胶质母细胞瘤肿瘤上评估了 NBS2，表明它在对这些疾病的已知亚型进行肿瘤分类方面优于最佳的基于网络的方法。通过解释交互权重，我们突出了驱动选定亚型的特征分子途径。
NBS2 包可在以下网址免费获得：https://github.com/wzhang1984/NBSS。
补充数据可在生物信息学在线获得。
