Identification Of Driver Pathways Research Articles

A nonlinear model and an algorithm for identifying cancer driver pathways

The identification of driver pathways has attracted considerable attention in recent years due to the high significance of driver pathways in cancer related studies. In most identification methods, the linear measurement is adopted for evaluating exclusivity, and some preset parameters are introduced to balance the contributions of different measurements. However, both the linear measurement and the preset parameters may exert negative effects on the applications of these methods. In this study, a nonlinear function is devised to measure exclusivity, and a parameter free nonlinear maximum weight submatrix (NMWS) identification model is proposed by considering coverage, exclusivity and connectivity. A competitive co-evolution algorithm (CCA) is also put forward for solving the presented NMWS model. Experimental results on both real biological data and simulated one were used compare the identification performance of the presented method with that of seven state of the art ones. The pathway detected by the presented method not only contains more genes enriched in a known signaling pathway, but also has a stronger connectivity in Protein–Protein Interaction (PPI) network. Simultaneously, the high efficiency of the presented method makes it practical in realistic applications. All of which have been confirmed through a large number of experiments.

A model and algorithm for identifying driver pathways based on weighted non-binary mutation matrix

It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments.

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

With the availability of high-throughput technologies, ahuge number of biological data (e.g., somatic mutation, DNA methylation and gene expression) in multiple cancers have been generated. Amajor challenge is to identify functional and vital driver mutation import for the initiation and progression of cancer. In this paper, we introduce anovel 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.

Abstract 3971: DrGaP: A powerful tool for identifying driver genes and pathways in cancer sequencing studies

Abstract Recent next-generation sequencing experiments have brought success in identifying cancer genes that were highly frequently mutated in tumors. As genomic sequencing experiments continue to identify large numbers of novel cancer mutations, one big challenge will be distinguishing driver mutations from passenger mutations. Driver mutations are the ones that have a role in oncogenesis or in the cancer phenotype, whereas passenger mutations are co-traveler that accumulate through DNA replication but are irrelevant to tumor development. From statistical point of view, driver genes are defined as those for which the non-silent mutation rate is significantly greater than a background (or passenger) mutation rate estimated from silent mutations. Statistical methods are now actively being developed to attempt to assess functional significance of a mutated gene in cancer sequencing studies. However, embedding biological knowledge on mutational process in tumors into statistical models is not trivial. These biological considerations include length of protein coding regions, gene structure, transcript isoforms, difference in mutation types, variation in background mutation rates, redundancy of the genetic code, multiple mutations in one gene, and functional consequence of different tumor types. They have not yet been fully addressed by current methods and tools for identifying driver genes. Driver mutations could be either common or rare among tumors. Identification of rare driver mutations may pose a potential challenge. In addition, mutation in one member of a collection of functionally related genes may result in the same net effect, and/or mutations in certain genes may be observed less frequently if they play functional roles in later stages of tumor development. At the pathway level, the frequency of rare driver mutations is accumulated in that pathway which warrants sufficient detection power. Thus, there is an increasing interest in the identification of driver pathways in tumor formation and progression. To meet these challenges, we developed a tool-DrGaP for identify driver genes and pathways in cancer sequencing studies. We introduced the Poisson processes to model the random nature of somatic mutation under no selection pressure. The new model can handle the identification of both driver genes and pathways or any gene sets in the same statistical framework. The new tool provides several significant improvements and increased power over current existing methods through simulations. To illustrate its utility, we applied the DrGaP to analysis of a recent lung cancer sequencing study. The DrGaP identified a few novel driver genes that were missed by previous methods, demonstrating its high accuracy and sensitivity. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3971. doi:1538-7445.AM2012-3971