ArticlePLoS computational biology2017
NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis.
Article in PLoS computational biology, 2017. 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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17 citing papers in PubMed.
- Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives.Cells · 2025Review
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- Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes.Briefings in bioinformatics · 2023Article
- Integrating multiomics and prior knowledge: a study of the Graphnet penalty impact.Bioinformatics (Oxford, England) · 2023Article
- Pan-cancer integrative analysis of whole-genome De novo somatic point mutations reveals 17 cancer types.BMC bioinformatics · 2022Article
- Network Approaches for Precision Oncology.Advances in experimental medicine and biology · 2022Article
- Estimating the dimensionality of the manifold underlying multi-electrode neural recordings.PLoS computational biology · 2021Article
- Identification of Clinically Relevant Subgroups of Chronic Lymphocytic Leukemia Through Discovery of Abnormal Molecular Pathways.Frontiers in genetics · 2021Article
- A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes.Human mutation · 2020Article
- Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.Scientific reports · 2020Article
- Interrogating Mutant Allele Expression via Customized Reference Genomes to Define Influential Cancer Mutations.Scientific reports · 2019Article
- Development of somatic mutation signatures for risk stratification and prognosis in lung and colorectal adenocarcinomas.BMC medical genomics · 2019Article
- Differentially mutated subnetworks discovery.Algorithms for molecular biology : AMB · 2019Article
- The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.Journal of molecular biology · 2018Review
- Cancer subtype identification using somatic mutation data.British journal of cancer · 2018Article
- The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes.Molecules (Basel, Switzerland) · 2018Article
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3 authors.
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Abstract
Genome-wide somatic mutation profiles of tumours can now be assessed efficiently and promise to move precision medicine forward. Statistical analysis of mutation profiles is however challenging due to the low frequency of most mutations, the varying mutation rates across tumours, and the presence of a majority of passenger events that hide the contribution of driver events. Here we propose a method, NetNorM, to represent whole-exome somatic mutation data in a form that enhances cancer-relevant information using a gene network as background knowledge. We evaluate its relevance for two tasks: survival prediction and unsupervised patient stratification. Using data from 8 cancer types from The Cancer Genome Atlas (TCGA), we show that it improves over the raw binary mutation data and network diffusion for these two tasks. In doing so, we also provide a thorough assessment of somatic mutations prognostic power which has been overlooked by previous studies because of the sparse and binary nature of mutations.
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