A novel network control model for identifying personalized driver genes in cancer

Abstract

Although existing computational models have identified many common driver genes, it remains challenging to identify the personalized driver genes by using samples of an individual patient. Recently, the methods of exploiting the structure-based control principles of complex networks provide new clues for identifying minimum number of driver nodes to drive the state transition of large-scale complex networks from an initial state to the desired state. However, the structure-based network control methods cannot be directly applied to identify the personalized driver genes due to the unknown network dynamics of the personalized system. Here we proposed the personalized network control model (PNC) to identify the personalized driver genes by employing the structure-based network control principle on genetic data of individual patients. In PNC model, we firstly presented a paired single sample network construction method to construct the personalized state transition network for capturing the phenotype transitions between healthy and disease states. Then, we designed a novel structure-based network control method from the Feedback Vertex Sets-based control perspective to identify the personalized driver genes. The wide experimental results on 13 cancer datasets from The Cancer Genome Atlas firstly showed that PNC model outperforms current state-of-the-art methods, in terms of F-measures for identifying cancer driver genes enriched in the gold-standard cancer driver gene lists. Furthermore, these results showed that personalized driver genes can be explored by their network characteristics even when they are hidden factors in transcription and mutation profiles. Our PNC gives novel insights and useful tools into understanding the tumor heterogeneity in cancer. The PNC package and data resources used in this work can be freely downloaded from https://github.com/NWPU-903PR/PNC.

pDriver: A novel method for unravelling personalised coding and miRNA cancer drivers

“…In this section, we compare the performance of pDriver with eight existing methods with different approaches for discovering cancer driver genes, including three methods for identifying personalised cancer drivers (DawnRank (Hou and Ma, 2014), PNC (Guo et al, 2019), and SCS (Guo et al, 2018)) and five methods for identifying cancer drivers at the population level (ActiveDriver (Reimand and Bader, 2013), DriverML (Han et al, 2019), DriverNet (Bashashati et al, 2012), MutSigCV (Lawrence et al, 2013), and OncodriveFM (Gonzalez-Perez and Lopez-Bigas, 2012)). Besides, ActiveDriver, DriverML, MutSigCV, and OncodriveFM are mutation-based methods while DawnRank, DriverNet, PNC, and SCS are network-based methods.…”

“…Recently, some methods have been developed to identify personalised cancer drivers such as DawnRank (Hou and Ma, 2014), SCS (Guo et al, 2018), and PNC (Guo et al, 2019). DawnRank considers mutated genes with higher connectivity in the gene regulatory network are more impactful and identifies such genes by applying PageRank Brin and Page, 1998) to the gene network.…”

“…We apply pDriver to five TCGA cancer datasets and validate the results with the Cancer Gene Census (CGC), in comparison with the state-ofthe-art cancer driver prediction methods, including 3 personalised driver prediction methods (DawnRank (Hou and Ma, 2014), PNC (Guo et al, 2019), and SCS (Guo et al, 2018)) and 5 population level driver prediction methods (ActiveDriver (Reimand and Bader, 2013), DriverML (Han et al, 2019), DriverNet (Bashashati et al, 2012), MutSigCV (Lawrence et al, 2013), and OncodriveFM (Gonzalez-Perez and Lopez-Bigas, 2012)). Since there is no ground truth available for individual patients' cancer drivers, following the same approach in existing literature on personalised driver prediction, we aggregate the results of personalised coding drivers discovered by pDriver (and the other 3 personalised driver prediction methods) for the comparison.…”

Prioritizing Cancer Genes Based on an Improved Random Walk Method

“…To retrieve more specific peculiarity of different types of cancer, we constructed different networks for different types of cancer by integrating the known PPI network and differential coexpression network (Guo et al, 2019).…”

“…The assumption of our method is that genes in the interaction network with a higher degree have a higher transition probability from their upstream neighbors. First, we constructed different networks for different types of cancer by selecting those edges that exist in both the known PPI network and differential coexpression network (Guo et al, 2019), in which the known information of the PPI network used is a directed network from DanwRank (Hou and Ma, 2014). The tumor and normal expression data were used to construct the differential coexpression network for each type of cancer.…”