ArticleJournal of biomedicine & biotechnology2012
Inference of tumor phylogenies from genomic assays on heterogeneous samples.
Article in Journal of biomedicine & biotechnology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Estimating growth patterns and driver effects in tumor evolution from individual samples.Nature communications · 2020Article
- The evolution of tumour phylogenetics: principles and practice.Nature reviews. Genetics · 2017Review
- An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.BioMed research international · 2017Article
- Article
- Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics.PLoS computational biology · 2014Article
- Explaining evolution via constrained persistent perfect phylogeny.BMC genomics · 2014Article
- TrAp: a tree approach for fingerprinting subclonal tumor composition.Nucleic acids research · 2013Article
- Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations.Bioinformatics (Oxford, England) · 2013Article
- Applying ecological and evolutionary theory to cancer: a long and winding road.Evolutionary applications · 2013Article
- Novel multisample scheme for inferring phylogenetic markers from whole genome tumor profiles.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Tumorigenesis can in principle result from many combinations of mutations, but only a few roughly equivalent sequences of mutations, or "progression pathways," seem to account for most human tumors. Phylogenetics provides a promising way to identify common progression pathways and markers of those pathways. This approach, however, can be confounded by the high heterogeneity within and between tumors, which makes it difficult to identify conserved progression stages or organize them into robust progression pathways. To tackle this problem, we previously developed methods for inferring progression stages from heterogeneous tumor profiles through computational unmixing. In this paper, we develop a novel pipeline for building trees of tumor evolution from the unmixed tumor data. The pipeline implements a statistical approach for identifying robust progression markers from unmixed tumor data and calling those markers in inferred cell states. The result is a set of phylogenetic characters and their assignments in progression states to which we apply maximum parsimony phylogenetic inference to infer tumor progression pathways. We demonstrate the full pipeline on simulated and real comparative genomic hybridization (CGH) data, validating its effectiveness and making novel predictions of major progression pathways and ancestral cell states in breast cancers.
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Registered trials
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.