ArticleBioinformatics (Oxford, England)2014
OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 61 citations in OpenAlex.
- Biomarker Analysis from Patients with Metastatic PDAC Treated with TGFβ Antibody NIS793 plus Abraxane + Gemcitabine versus Abraxane + Gemcitabine Alone in a Phase II, Open-Label, Randomized Study.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Trial
- Nanomaterial-mediated functional remodeling of antigen-presenting cells: A novel strategy to break tumor immune escape.Acta pharmaceutica Sinica. B · 2026Review
- Dissecting the expression pattern during embryonic development and unveiling sustained ZGA genes with oncogenic relevance.BMC genomics · 2026Article
- TP63 mediates the generation of tumour-specific chromatin loops that underlie MYC activation in radiation-induced tumorigenesis.Nature communications · 2025Article
- Nanomedicines Targeting Metabolic Pathways in the Tumor Microenvironment: Future Perspectives and the Role of AI.Metabolites · 2025Review
- Pharmacogenomic discovery of genetically targeted cancer therapies optimized against clinical outcomes.NPJ precision oncology · 2024Article
- Functional exploration of copy number alterations in a Drosophila model of triple-negative breast cancer.Disease models & mechanisms · 2024Article
- Systematic integration of protein-affecting mutations, gene fusions, and copy number alterations into a comprehensive somatic mutational profile.Cell reports methods · 2023Article
- Regulatory patterns analysis of transcription factor binding site clustered regions and identification of key genes in endometrial cancer.Computational and structural biotechnology journal · 2022Article
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- Mutation bias within oncogene families is related to proliferation-specific codon usage.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- New insights on human essential genes based on integrated analysis and the construction of the HEGIAP web-based platform.Briefings in bioinformatics · 2020Article
- Identification of new candidate genes and signalling pathways associated with the development of neuroendocrine pancreatic tumours based on next generation sequencing data.Molecular biology reports · 2020Article
- Exomes of Ductal Luminal Breast Cancer Patients from Southwest Colombia: Gene Mutational Profile and Related Expression Alterations.Biomolecules · 2020Article
- Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine.Advanced materials (Deerfield Beach, Fla.) · 2020Review
- Interpreting pathways to discover cancer driver genes with Moonlight.Nature communications · 2020Article
- Comprehensive Genomic Review of TCGA Head and Neck Squamous Cell Carcinomas (HNSCC).Journal of clinical medicine · 2019Article
- Article
- The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers.Nature reviews. Cancer · 2018Review
- Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations.Genome medicine · 2018Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationSeveral computational methods have been developed to identify cancer drivers genes-genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making. Here, we assess the capacity of multiple gene features related to the pattern of genomic alterations across tumors to distinguish between activating and LoF cancer genes, and we present an automated approach to aid the classification of novel cancer drivers according to their role.
resultOncodriveROLE is a machine learning-based approach that classifies driver genes according to their role, using several properties related to the pattern of alterations across tumors. The method shows an accuracy of 0.93 and Matthew's correlation coefficient of 0.84 classifying genes in the Cancer Gene Census. The OncodriveROLE classifier, its results when applied to two lists of predicted cancer drivers and TCGA-derived mutation and copy number features used by the classifier are available at http://bg.upf.edu/oncodrive-role. AVAILABILITY AND IMPLEMENTATION: The R implementation of the OncodriveROLE classifier is available at http://bg.upf.edu/oncodrive-role. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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