Improving the identification of cancer driver modules using deep features learned from multi-omics data

Identifying the cancer driver modules or pathways is crucial to understanding the fundamental mechanisms of cancer occurrence and progression. The rapid abundance of cancer omics data provides unprecedented opportunities to study the driver modules in cancer, and many computational methods have been developed in recent years. However, most existing methods have limitations in considering different types of cancer omics data and cannot effectively learn informative omics features for integrated identification of driver modules. In this paper, we introduce a new integrated framework to accurately identify the cancer driver modules by integrating the protein-protein interaction network, transcriptional regulatory network, gene expression and mutation data in cancer. We first develop a series of methods to learn the deep features of functional connectivity between genes in each omics data and then construct an integrated gene functional coherence network. Furthermore, we present a two-step module mining method to efficiently identify the cancer driver modules from the integrated gene functional coherence network. Systematic experiments in three cancer types demonstrate that the proposed framework can obtain more significant driver modules than most existing methods, and some identified driver modules are associated with clinical survival phenotypes.