ArticleGenome research2009
Identification of rare cancer driver mutations by network reconstruction.
Article in Genome research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
60 citing papers in PubMed, 124 citations in OpenAlex.
- PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data.PLoS computational biology · 2026Article
- Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome.NAR cancer · 2024Article
- A pathway-based mutation signature to predict the clinical outcomes and response to CTLA-4 inhibitors in melanoma.Computational and structural biotechnology journal · 2023Article
- A New View of Activating Mutations in Cancer.Cancer research · 2022Article
- Article
- TGF-β links glycolysis and immunosuppression in glioblastoma.Histology and histopathology · 2021Review
- Review
- Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis.Global challenges (Hoboken, NJ) · 2021Article
- Analysis, identification and visualization of subgroups in genomics.Briefings in bioinformatics · 2021Review
- A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk.Genome biology · 2020Article
- Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2019Article
- Use case driven evaluation of open databases for pediatric cancer research.BioData mining · 2019Article
- Extracting Fitness Relationships and Oncogenic Patterns among Driver Genes in Cancer.Molecules (Basel, Switzerland) · 2017Article
- Dynamical differential networks and modules inferring disrupted genes associated with the progression of Alzheimer's disease.Experimental and therapeutic medicine · 2017Article
- Review
- Integrating omics data and protein interaction networks to prioritize driver genes in cancer.Oncotarget · 2017Article
- Characterization of potential driver mutations involved in human breast cancer by computational approaches.Oncotarget · 2017Review
- Image-guided genomics of phenotypically heterogeneous populations reveals vascular signalling during symbiotic collective cancer invasion.Nature communications · 2017Article
- Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples.PLoS computational biology · 2017Article
- Computational approaches for the identification of cancer genes and pathways.Wiley interdisciplinary reviews. Systems biology and medicine · 2017Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Recent large-scale tumor resequencing studies have identified a number of mutations that might be involved in tumorigenesis. Analysis of the frequency of specific mutations across different tumors has been able to identify some, but not all of the mutated genes that contribute to tumor initiation and progression. One reason for this is that other functionally important genes are likely to be mutated more rarely and only in specific contexts. Thus, for example, 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, such as metastasis. We modified and applied a network reconstruction and coexpression module identification-based approach to identify functionally related gene modules targeted by somatic mutations in cancer. This method was applied to available breast cancer, colorectal cancer, and glioblastoma sequence data, and identified Wnt/TGF-beta cross-talk, Wnt/VEGF signaling, and MAPK/focal adhesion kinase pathways as targets of rare driver mutations in breast, colorectal cancer, and glioblastoma, respectively. These mutations do not appear to alter genes that play a central role in these pathways, but rather contribute to a more refined shaping or "tuning" of the functioning of these pathways in such a way as to result in the inhibition of their tumor-suppressive signaling arms, and thereby conserve or enhance tumor-promoting processes.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.