ArticlePLoS computational biology2015
Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- Variant-Specific Landscape of Mutual Exclusivity Among BRAF, EGFR, and KRAS Oncogenes Reveals Overlap With Functionally Antagonistic Mutant Pairs.International journal of cancer · 2026Article
- IBI-DT: a novel approach combining individualized Bayesian inference and decision tree for identifying cancer drivers and their interactions.Briefings in bioinformatics · 2025Article
- The Role of Fibroblast Growth Factor 19 in Hepatocellular Carcinoma.The American journal of pathology · 2021Review
- Analysis, identification and visualization of subgroups in genomics.Briefings in bioinformatics · 2021Review
- Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling.BMC cancer · 2020Article
- Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference.PLoS computational biology · 2019Article
- Adaptively Weighted and Robust Mathematical Programming for the Discovery of Driver Gene Sets in Cancers.Scientific reports · 2019Article
- Pathway-based dissection of the genomic heterogeneity of cancer hallmarks' acquisition with SLAPenrich.Scientific reports · 2018Article
- A signal-based method for finding driver modules of breast cancer metastasis to the lung.Scientific reports · 2017Article
- Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations.Cancer research · 2016Article
- Understanding Genotype-Phenotype Effects in Cancer via Network Approaches.PLoS computational biology · 2016Review
- An exact algorithm for finding cancer driver somatic genome alterations: the weighted mutually exclusive maximum set cover problem.Algorithms for molecular biology : AMB · 2016Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers. It is well known that somatic genome alterations (SGAs) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity, in which these SGAs usually do not co-occur in a tumor. With some success, this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway. However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways. Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data. Next, we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity. Finally, we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach. We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database, and perform systematic evaluations. Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways.
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