ArticleGenome medicine2021
Pan-cancer detection of driver genes at the single-patient resolution.
Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Mining cancer genomes for copy number alterations identifies glycosylation enzymes as oncogenic drivers.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Integrating Multi-Source Directed Gene Networks and Multi-Omics Data to Identify Cancer Driver Genes Based on Graph Neural Networks.International journal of molecular sciences · 2025Article
- Challenges and opportunities for oncology drug repurposing informed by synthetic lethality.NPJ systems biology and applications · 2025Article
- MLGCN-Driver: a cancer driver gene identification method based on multi-layer graph convolutional neural network.BMC bioinformatics · 2025Article
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification.International journal of molecular sciences · 2024Article
- ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification.PLoS computational biology · 2024Article
- Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.Biology · 2024Article
- Multiomics analysis reveals metabolic subtypes and identifies diacylglycerol kinase α (DGKA) as a potential therapeutic target for intrahepatic cholangiocarcinoma.Cancer communications (London, England) · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Identifying Explainable Machine Learning Models and a Novel SFRP2International journal of molecular sciences · 2023Article
- SMG: self-supervised masked graph learning for cancer gene identification.Briefings in bioinformatics · 2023Article
- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- Driver gene detection through Bayesian network integration of mutation and expression profiles.Bioinformatics (Oxford, England) · 2022Article
- Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource.Genome biology · 2022Review
- Exome sequencing of hepatocellular carcinoma in lemurs identifies potential cancer drivers: A pilot study.Evolution, medicine, and public health · 2022Article
- Molecular-based precision oncology clinical decision making augmented by artificial intelligence.Emerging topics in life sciences · 2021Review
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Authors and funding
4 authors.
Funding
Abstract
backgroundIdentifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.
resultsWe present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.
conclusionssysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).
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