Cooperative driver pathways discovery by multiplex network embedding.

Cooperative driver pathways discovery helps researchers to study the pathogenesis of cancer. However, most discovery methods mainly focus on genomics data, and neglect the known pathway information and other related multi-omics data; thus they cannot faithfully decipher the carcinogenic process. We propose CDPMiner (Cooperative Driver Pathways Miner) to discover cooperative driver pathways by multiplex network embedding, which can jointly model relational and attribute information of multi-type molecules. CDPMiner first uses the pathway topology to quantify the weight of genes in different pathways, and optimizes the relations between genes and pathways. Then it constructs an attributed multiplex network consisting of micro RNAs, long noncoding RNAs, genes and pathways, embeds the network through deep joint matrix factorization to mine more essential information for pathway-level analysis and reconstructs the pathway interaction network. Finally, CDPMiner leverages the reconstructed network and mutation data to define the driver weight between pathways to discover cooperative driver pathways. Experimental results on Breast invasive carcinoma and Stomach adenocarcinoma datasets show that CDPMiner can effectively fuse multi-omics data to discover more driver pathways, which indeed cooperatively trigger cancers and are valuable for carcinogenesis analysis. Ablation study justifies CDPMiner for a more comprehensive analysis of cancer by fusing multi-omics data.

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm.

BACKGROUND: Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data. RESULTS: In this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein-Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with "inclusion" relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the "inclusion" relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases. CONCLUSIONS: The gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones.

Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways.

Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene-pathway and gene-miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $\geq $0.9] and reconstruct the pathway interaction network (AUROC of>0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway.

Identifying Cancer Driver Pathways Based on the Mouth Brooding Fish Algorithm.

Identifying the driver genes of cancer progression is of great significance in improving our understanding of the causes of cancer and promoting the development of personalized treatment. In this paper, we identify the driver genes at the pathway level via an existing intelligent optimization algorithm, named the Mouth Brooding Fish (MBF) algorithm. Many methods based on the maximum weight submatrix model to identify driver pathways attach equal importance to coverage and exclusivity and assign them equal weight, but those methods ignore the impact of mutational heterogeneity. Here, we use principal component analysis (PCA) to incorporate covariate data to reduce the complexity of the algorithm and construct a maximum weight submatrix model considering different weights of coverage and exclusivity. Using this strategy, the unfavorable effect of mutational heterogeneity is overcome to some extent. Data involving lung adenocarcinoma and glioblastoma multiforme were tested with this method and the results compared with the MDPFinder, Dendrix, and Mutex methods. When the driver pathway size was 10, the recognition accuracy of the MBF method reached 80% in both datasets, and the weight values of the submatrix were 1.7 and 1.89, respectively, which are better than those of the compared methods. At the same time, in the signal pathway enrichment analysis, the important role of the driver genes identified by our MBF method in the cancer signaling pathway is revealed, and the validity of these driver genes is demonstrated from the perspective of their biological effects.

A Sensorimotor Pathway via Higher-Order Thalamus.

We now know that sensory processing in cortex occurs not only via direct communication between primary to secondary areas, but also via their parallel cortico-thalamo-cortical (i.e., trans-thalamic) pathways. Both corticocortical and trans-thalamic pathways mainly signal through glutamatergic class 1 (driver) synapses, which have robust and efficient synaptic dynamics suited for the transfer of information such as receptive field properties, suggesting the importance of class 1 synapses in feedforward, hierarchical processing. However, such a parallel arrangement has only been identified in sensory cortical areas: visual, somatosensory, and auditory. To test the generality of trans-thalamic pathways, we sought to establish its presence beyond purely sensory cortices to determine whether there is a trans-thalamic pathway parallel to the established primary somatosensory (S1) to primary motor (M1) pathway. We used trans-synaptic viral tracing, optogenetics in slice preparations, and bouton size analysis in the mouse (both sexes) to document that a circuit exists from layer 5 of S1 through the posterior medial nucleus of the thalamus to M1 with glutamatergic class 1 properties. This represents a hitherto unknown, robust sensorimotor linkage and suggests that the arrangement of parallel direct and trans-thalamic corticocortical circuits may be present as a general feature of cortical functioning.SIGNIFICANCE STATEMENT During sensory processing, feedforward pathways carry information such as receptive field properties via glutamatergic class 1 synapses, which have robust and efficient synaptic dynamics. As expected, class 1 synapses subserve the feedforward projection from primary to secondary sensory cortex, but also a route through specific higher-order thalamic nuclei, creating a parallel feedforward trans-thalamic pathway. We now extend the concept of cortical areas being connected via parallel, direct, and trans-thalamic circuits from purely sensory cortices to a sensorimotor cortical circuit (i.e., primary sensory cortex to primary motor cortex). This suggests a generalized arrangement for corticocortical communication.

Integrated genomic characterization of cancer genes in glioma.

BACKGROUND: Cancers are caused by the acquisition of somatic mutations. Numerous efforts have been made to characterize the key driver genes and pathways in glioma, however, the etiology of glioma is still not completely known. This study was implemented to characterize driver genes in glioma independently of somatic mutation frequencies. METHODS: Driver genes and pathways were predicted by OncodriveCLUST, OncodriveFM, Icages, Drgap and Dendrix in glioma using 31,958 somatic mutations from TCGA, followed by an integrative characterization of driver genes. RESULTS: Overall, 685 driver genes and 215 driver pathways were determined by the five tools. FSTL5, HCN1, TMEM132D, TRHDE and KRT222 showed the strongest expression correlation with other genes in the co-expression network of glioma tissues. ST6GAL2, PIK3CA, PIK3R1, TP53 and EGFR are at the core of the protein-protein interaction network. 133 driver genes were up-regulated and associated to poor prognosis, 43 driver genes were down-regulated and related to favorable clinical outcome in glioma patients. The driver genes such as MSH6 and RUNX1T1 might serve as candidate prognostic biomarkers and therapeutic targets in glioma. CONCLUSIONS: The set of new cancer genes and pathways sheds insights into the tumorigenesis of glioma and paves the way for developing driver gene-targeted therapy and prognostic biomarkers in glioma.

Identification of driver pathways in cancer based on combinatorial patterns of somatic gene mutations.

With the availability of high-throughput technologies, a huge number of biological data (e.g., somatic mutation, DNA methylation and gene expression) in multiple cancers have been generated. A major challenge is to identify functional and vital driver mutation import for the initiation and progression of cancer. In this paper, we introduce a novel method, named Co-occurring mutated metagene Genetic Algorithm (CoGA), to solve the maximum weight submatrix problem, with the aim of distinguishing mutated driver pathways in cancer. The algorithm relies on the combinatorial properties of mutations in the same pathways: high coverage and mutual exclusivity, and the possible properties of mutations in different pathways: co-occurring pattern. We carried out the experiment with glioblastoma multiform (GBM) data. The experimental results show that compared with the original model, our algorithm has more potential to identify driver pathways in cancer with biological significance.

DriverGenePathway: Identifying driver genes and driver pathways in cancer based on MutSigCV and statistical methods.

Although computational methods for driver gene identification have progressed rapidly, it is far from the goal of obtaining widely recognized driver genes for all cancer types. The driver gene lists predicted by these methods often lack consistency and stability across different studies or datasets. In addition to analytical performance, some tools may require further improvement regarding operability and system compatibility. Here, we developed a user-friendly R package (DriverGenePathway) integrating MutSigCV and statistical methods to identify cancer driver genes and pathways. The theoretical basis of the MutSigCV program is elaborated and integrated into DriverGenePathway, such as mutation categories discovery based on information entropy. Five methods of hypothesis testing, including the beta-binomial test, Fisher combined p-value test, likelihood ratio test, convolution test, and projection test, are used to identify the minimal core driver genes. Moreover, de novo methods, which can effectively overcome mutational heterogeneity, are introduced to identify driver pathways. Herein, we describe the computational structure and statistical fundamentals of the DriverGenePathway pipeline and demonstrate its performance using eight types of cancer from TCGA. DriverGenePathway correctly confirms many expected driver genes with high overlap with the Cancer Gene Census list and driver pathways associated with cancer development. The DriverGenePathway R package is freely available on GitHub: https://github.com/bioinformatics-xu/DriverGenePathway.

Comprehensive characterization of driver genes in diffuse large B cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) is the most common hematological malignancy and is one of the most frequent non-Hodgkin lymphomas. Large-scale genomic studies have defined genetic drivers of DLBCL and their association with functional and clinical outcomes. However, the lymphomagenesis of DLBCL is yet to be fully understood. In the present study, four computational tools OncodriveFM, OncodriveCLUST, integrated Cancer Genome Score and Driver Genes and Pathways were used to detect driver genes and driver pathways involved in DLBCL. The aforementioned tools were also used to perform an integrative investigation of driver genes, including co-expression network, protein-protein interaction, copy number variation and survival analyses. The present study identified 208 driver genes and 31 driver pathways in DLBCL. IGLL5, MLL2, BTG2, B2M, PIM1, CARD11 were the top five frequently mutated genes in DLBCL. NOTCH3, LAMC1, COL4A1, PDGFRB and KDR were the 5 hub genes in the blue module that were associated with patient age. TP53, MYC, EGFR, PTEN, IL6, STAT3, MAPK8, TNF and CDH1 were at the core of the protein-protein interaction network. PRDM1, CDKN2A, CDKN2B, TNFAIP3, RSPO3 were the top five frequently deleted driver genes in DLBCL, while ACTB, BTG2, PLET1, CARD11, DIXDC1 were the top five frequently amplified driver genes in DLBCL. High EIF3B, MLH1, PPP1CA and RECQL4 expression was associated with decreased overall survival rate of patients with DLBCL. High XPO1 and LYN expression were associated with increased overall survival rate of patients with DLBCL. The present study improves the understanding of the biological processes and pathways involved in lymphomagenesis. The driver genes, EIF3B, MLH1, PPP1CA, RECQL4, XPO1 and LYN, pave the way for developing prognostic biomarkers and new therapeutic strategies for DLBCL.

Identifying mutated driver pathways in cancer by integrating multi-omics data.

Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.

Comprehensive characterization of cancer genes in hepatocellular carcinoma genomes.

The present study was performed to detect moderate or low-frequency mutated cancer driver genes in hepatocellular carcinoma (HCC), using OncodriveFM and Dendrix. Following this, integrated analyses were conducted on these novel cancer driver genes. A total of 112,980 somatic mutations were retrieved from TCGA and classified into 11 categories based on their function. Driver genes and pathways were predicted by OncodriveFM and Dendrix, followed by differential expression, DNA-methylation, copy number variations and survival analyses. Overall, non-synonymous mutations accounted for >60% (72,149/112, 980) of total variants, 108 and 3 driver genes were determined by OncodriveFM and Dendrix, respectively. Tumor protein p53, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4, smad family member 3, RB transcriptional corepressor 1, catenin ß 1, smad family member 4, mitogen-activated protein kinase 1 and TSC complex subunit 2 are at the core of the driver gene interaction network. Two genes, transportin 1 (TNPO1) and chaperonin containing TCP1 subunit 3 (CCT3), were hypomethylated and overexpressed, and high expression of TNPO1 and CCT3 indicated a poor prognosis in patients with HCC. ß-carotene oxygenase 2 was hypermethylated, under-expressed and associated with favorable prognosis in HCC. The present study has identified a set of novel cancer genes and pathways, offering potential therapeutic targets and prognostic biomarkers for the treatment of HCC.

A comprehensive analysis of cancer-driving mutations and genes in kidney cancer.

An accumulation of driver mutations is important for cancer formation and progression, and leads to the disruption of genes and signaling pathways. The identification of driver mutations and genes has been the subject of numerous previous studies. The present study was performed to identify cancer-driving mutations and genes in renal cell carcinoma (RCC), prioritizing noncoding variants with a high functional impact, in order to analyze the most informative features. Sorting Intolerant From Tolerant (SIFT), Polymorphism Phenotyping version 2 (Polyphen2) and MutationAssessor were applied to predict deleterious mutations in the coding genome. OncodriveFM and OncodriveCLUST were used to detect potential driver genes and signaling pathways. The functional impact of noncoding variants was evaluated using Combined Annotation Dependent Depletion, FunSeq2 and Genome-Wide Annotation of Variants. Noncoding features were analyzed with respect to their enrichment of high-scoring variants. A total of 1,327 coding mutations in clear cell RCC, 258 in chromophobe RCC and 1,186 in papillary RCC were predicted to be deleterious by all three of MutationAssessor, Polyphen2 and SIFT. In total, 77 genes were positively selected by OncodriveFM and 1 by OncodriveCLUST, 45 of which were recurrently mutated genes. In addition, 10 signaling pathways were recurrently mutated and had a high functional impact bias (FM bias), and 31 novel signaling pathways with high FM bias were identified. Furthermore, noncoding regulatory features and conserved regions contained numerous high-scoring variants, and expression, replication time, GC content and recombination rate were positively correlated with the densities of high-scoring variants. In conclusion, the present study identified a list of cancer-driving genes and signaling pathways, features like regulatory elements, conserved regions, replication time, expression, GC content and recombination rate are major factors that affect the distribution of high-scoring non-coding mutations in kidney cancer.