ArticleBioinformatics (Oxford, England)2020
Identification of conserved evolutionary trajectories in tumors.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Inferring and summarizing tumor phylogenies from bulk DNA data.Algorithms for molecular biology : AMB · 2026Article
- Bayesian inference of fitness landscapes via tree-structured branching processes.Bioinformatics (Oxford, England) · 2025Article
- CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.Genome biology · 2025Article
- Oncotree2vec - a method for embedding and clustering of tumor mutation trees.Bioinformatics (Oxford, England) · 2024Article
- Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.Journal of molecular evolution · 2023Review
- Modeling and predicting cancer clonal evolution with reinforcement learning.Genome research · 2023Article
- Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees.Nature communications · 2023Article
- Targeting the Deterministic Evolutionary Trajectories of Clear Cell Renal Cell Carcinoma.Cancers · 2020Review
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7 authors.
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Abstract
motivationAs multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics).
resultsIn this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories. AVAILABILITY AND IMPLEMENTATION: CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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