ArticleBioinformatics (Oxford, England)2020
Modelling cancer progression using Mutual Hazard Networks.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Quantifying uncertainty of predictions from cancer progression models.Bioinformatics (Oxford, England) · 2026Article
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- A hypercubic Mk model framework for capturing reversibility in disease, cancer, and evolutionary accumulation modelling.Bioinformatics (Oxford, England) · 2024Article
- Cancerous time estimation for interpreting the evolution of lung adenocarcinoma.Briefings in bioinformatics · 2024Article
- HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures.PLoS computational biology · 2024Article
- Modeling metastatic progression from cross-sectional cancer genomics data.Bioinformatics (Oxford, England) · 2024Article
- A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer.bioRxiv : the preprint server for biology · 2024Article
- Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees.Nature communications · 2023Article
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- HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs.Bioinformatics (Oxford, England) · 2023Article
- EvAM-Tools: tools for evolutionary accumulation and cancer progression models.Bioinformatics (Oxford, England) · 2022Article
- ToMExO: A probabilistic tree-structured model for cancer progression.PLoS computational biology · 2022Article
- Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?PLoS computational biology · 2021Article
- Inferring tumor progression in large datasets.PLoS computational biology · 2020Article
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4 authors.
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Abstract
motivationCancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurrence patterns in cross-sectional data to infer epistatic interactions between events and thereby uncover their most likely order of occurrence. State-of-the-art progression models, however, are limited by mathematical tractability and only allow events to interact in directed acyclic graphs, to promote but not inhibit subsequent events, or to be mutually exclusive in distinct groups that cannot overlap.
resultsHere we propose Mutual Hazard Networks (MHN), a new Machine Learning algorithm to infer cyclic progression models from cross-sectional data. MHN model events by their spontaneous rate of fixation and by multiplicative effects they exert on the rates of successive events. MHN compared favourably to acyclic models in cross-validated model fit on four datasets tested. In application to the glioblastoma dataset from The Cancer Genome Atlas, MHN proposed a novel interaction in line with consecutive biopsies: IDH1 mutations are early events that promote subsequent fixation of TP53 mutations. AVAILABILITY AND IMPLEMENTATION: Implementation and data are available at https://github.com/RudiSchill/MHN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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