Driver pathway identification software tools | Protein interaction data analysis
It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. Therefore, discovering driver pathways is becoming an important step to understand the molecular mechanisms underlying cancer and design efficient treatments for cancer patients. Previous studies have focused mainly on observation of the alterations in cancer genomes at the individual gene or single pathway level. However, a great deal of evidence has indicated that multiple pathways often function cooperatively in carcinogenesis and other key biological processes.
A method for stratification (clustering) of patients in a cancer cohort based on genome scale somatic mutations measurements and a gene interaction network. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology.
Allows identification of cancer genes using a network-based approaches. NetSig is a statistic, designed to be independent of gene-based statistical tests, that combines cancer mutation data and molecular network information. The software addresses the effects of knowledge contamination. It can work with several types of functional genomics network data. NetSig was tested using a large-scale, in vivo, quantitative experimental framework.
An algorithm for discovery of mutated driver pathways in cancer using only mutation data. Dendrix finds sets of genes, domains, or nucleotides whose mutations exhibit both high coverage and high exclusivity in the analyzed samples.
Predicts functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). DriverNet combines different kinds of data thanks to an influence graph, that is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which have mutated in a larger number of patients. These mutations will push the gene expression values of the connected genes to some extreme values.
Identifies combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations.
A method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern.
A cancer gene prioritization method based on a pathway-centric analysis of mutation data. MUFFINN integrates mutational information for individual genes and their neighbors in co-functional networks. MUFFINN is highly predictive for known cancer genes, particularly for genes with low mutation occurrence among cancer patients, with the identification of drivers amongst these genes having substantially higher sensitivity than conventional methods based on gene-centric analysis of mutation data. MUFFINN works effectively with both pan-cancer and individual cancer type samples.
A data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively).
A powerful and flexible statistical framework for identifying driver genes and driver signaling pathways in cancer genome-sequencing studies. DrGaP is immediately applicable to cancer genome-sequencing studies and will lead to a more complete identification of altered driver genes and driver signaling pathways in cancer.
Identifies driver genes. This method displays the following characteristics: (1) it assigns scores to non-silent mutations according to their expected impacts on the protein function, (2) it permits each sample to have a different background mutation rate, (3) it accounts for the variable number of possible non-silent mutations that can occur at each base pair according to the genetic code, and (4) it takes into account uncertainties in the background mutation rate. The software was evaluated using lung tumor genome sequences.
Large-scale cancer genomics projects are providing a large volume of data about genomic, epigenomic and gene expression aberrations in multiple cancer types. One of the remaining challenges is to identify driver mutations, driver genes and driver pathways promoting cancer proliferation and filter out the unfunctional and passenger ones. MDPFinder is designed to de novo identify mutated driver pathways from mutation data in cancer.
Captures biologically and clinically relevant information. iPAS was applied to samples of lung and colon adenocarcinoma. It can recognize altered pathways in an individual by employing the nRef. This tool is able to find pathway aberrances that are associated with a patient’s clinical outcome. It provides an approach that uses the inter-gene correlation structure of the accumulated normal samples.
Identifies a large number of subnetworks dysregulated across many cancer types. MEMCover is an algorithm that uses a module cover optimization strategy to combine functional interactions, mutual exclusivity and genomic aberration frequency, identified many Pan-Cancer dysregulated subnetworks including previously known subnetworks as well as several new subnetworks whose across-cancer role has not been well appreciated previously.
Estimates p-values of mutual exclusivity while taking into account mutation frequencies of patients. WeSME closely approximates the results of the permutation-based method and does so without using the costly permutations of a mutation matrix. Moreover, WeSME can compute p-values for a subset of genes independently from the rest of genome unlike the permutation method that requires whole genome permutations. By dynamically adjusting sampling depth, WeSME can provide high precision p-values without a significant increase in computational cost.
A web tool for identifying altered pathways in cancer data. PathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.
Leverages mutual exclusivity of mutations, patient coverage and driver network concentration principles. To test C³, we performed a detailed analysis on TCGA breast cancer and glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying biologically relevant driver genes. The proposed agnostic clustering method represents a unique tool for efficient and reliable identification of mutation patterns and driver pathways in large-scale cancer genomics studies, and it may also be used for other clustering problems on biological graphs.
A package for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. DIGGIT searches for genetic alterations associated with dysregulation of MR protein activity, reducing the number of hypothesis to test, while providing regulatory clues to help elucidate associated biological mechanisms. It evaluates candidate alterations within a set of functional disease drivers and their upstream regulators .This is accomplished by a five-step process, requiring gene expression, matched genetic-variant profiles, specifically copy number variation data (CNV), and context-specific transcriptional and postranslational regulatory models. DIGGIT boosts the statistical power by focusing on the regulators mechanistically linked to the phenotype and on their upstream post-translational modulators.
An algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. Multi-Dendrix relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity.
A mutation network method to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.
Identifies multiple mutated modules displaying specific mutation patterns between and within modules. BeWith is a general framework that reveals complex relations between mutual exclusivity, functional interactions, and co-occurrence. This application can be used to uncover relationships between genes, gene groups, and pathways that were not accessible by previous methods. Its formulation is very general and is appropriate to interrogate other aspects of the mutational landscape by exploring different combinations of within-between definitions and constraints with simple modifications.