ArticleBioinformatics (Oxford, England)2020
Distance measures for tumor evolutionary trees.
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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16 citing papers in PubMed, 53 citations in OpenAlex.
- Identifying Robust Subclonal Structures through Tumor Progression Tree Alignment.bioRxiv : the preprint server for biology · 2026Article
- Inferring and summarizing tumor phylogenies from bulk DNA data.Algorithms for molecular biology : AMB · 2026Article
- General-purpose topology-aware embedding of tumor phylogenetic trees with graph neural networks.Bioinformatics advances · 2026Article
- Marker selection strategies for circulating tumor DNA guided by phylogenetic inference.Bioinformatics (Oxford, England) · 2025Article
- New generalized metric based on branch length distance to compare B cell lineage trees.Algorithms for molecular biology : AMB · 2024Article
- Weighted centroid trees: a general approach to summarize phylogenies in single-labeled tumor mutation tree inference.Bioinformatics (Oxford, England) · 2024Article
- Oncotree2vec - a method for embedding and clustering of tumor mutation trees.Bioinformatics (Oxford, England) · 2024Article
- TheJournal of computational biology : a journal of computational molecular cell biology · 2024Article
- A weighted distance-based approach for deriving consensus tumor evolutionary trees.Bioinformatics (Oxford, England) · 2023Article
- ToMExO: A probabilistic tree-structured model for cancer progression.PLoS computational biology · 2022Article
- Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations.Bioinformatics (Oxford, England) · 2022Article
- A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods.Frontiers in immunology · 2022Article
- The Generalized Robinson-Foulds Distance for Phylogenetic Trees.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- The Bourque distances for mutation trees of cancers.Algorithms for molecular biology : AMB · 2021Article
- Triplet-based similarity score for fully multilabeled trees with poly-occurring labels.Bioinformatics (Oxford, England) · 2021Article
- GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data.Frontiers in genetics · 2021Article
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Authors and funding
4 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
motivationThere has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology.
resultsHere, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. AVAILABILITY AND IMPLEMENTATION: Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.