ArticleNature communications2019
Detailed modeling of positive selection improves detection of cancer driver genes.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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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
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic Roles of N6-Methyladenosine METTL3 in Different Cancers: A System Review and Meta-Analysis.Cancer control : journal of the Moffitt Cancer CenterPooled it
- Inhibiting METTL3 synergizes with Notch blockade to treat ESCC by targeting cancer stemness via m6A-JAG2.Journal of gastroenterology · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- Loss of METTL3 m6A methyltransferase results in short-term progression and poor treatment outcome of bladder cancer patients.International journal of cancer · 2026Article
- Identification of cancer mini-drivers by deciphering selective landscape in the cancer genome.Briefings in bioinformatics · 2026Article
- Unraveling the dual role of METTL3-mediated mCancer biology & therapy · 2025Review
- Dual roles of NScience China. Life sciences · 2025Article
- RNA mCell investigation · 2025Article
- METTL3: a multifunctional regulator in diseases.Molecular and cellular biochemistry · 2025Review
- Article
- Multimodal tumor suppression by METTL3 gene knockdown in melanoma and colon cancer cells.Histochemistry and cell biology · 2024Article
- The Role of N6-methyladenosine Modification in Gametogenesis and Embryogenesis: Impact on Fertility.Genomics, proteomics & bioinformatics · 2024Review
- Integrative identification of non-coding regulatory regions driving metastatic prostate cancer.Cell reports · 2024Article
- Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden.Scientific reports · 2024Article
- Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.Scientific reports · 2024Article
- METTL3 recruiting M2-type immunosuppressed macrophages by targeting m6A-SNAIL-CXCL2 axis to promote colorectal cancer pulmonary metastasis.Journal of experimental & clinical cancer research : CR · 2024Article
- Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.Biology · 2024Article
- Assessing the validity of driver gene identification tools for targeted genome sequencing data.Bioinformatics advances · 2024Article
- Reinvestigating the clinical relevance of the miScience · 2023Article
- Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.Journal of molecular evolution · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Identifying driver genes from somatic mutations is a central problem in cancer biology. Existing methods, however, either lack explicit statistical models, or use models based on simplistic assumptions. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a model-based approach to driver gene identification. This method explicitly models positive selection at the single-base level, as well as highly heterogeneous background mutational processes. In particular, the selection model captures elevated mutation rates in functionally important sites using multiple external annotations, and spatial clustering of mutations. Simulations under realistic evolutionary models demonstrate the increased power of driverMAPS over current approaches. Applying driverMAPS to TCGA data of 20 tumor types, we identified 159 new potential driver genes, including the mRNA methyltransferase METTL3-METTL14. We experimentally validated METTL3 as a tumor suppressor gene in bladder cancer, providing support to the important role mRNA modification plays in tumorigenesis.
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