ArticleBioinformatics (Oxford, England)2013
Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A zero-agnostic model for copy number evolution in cancer.PLoS computational biology · 2023Article
- A zero-agnostic model for copy number evolution in cancer.bioRxiv : the preprint server for biology · 2023Article
- Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.Bioinformatics (Oxford, England) · 2021Article
- Joint Clustering of Single-Cell Sequencing and Fluorescence In Situ Hybridization Data for Reconstructing Clonal Heterogeneity in Cancers.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Assessing the contribution of tumor mutational phenotypes to cancer progression risk.PLoS computational biology · 2021Article
- Single-cell genetic analysis of clonal dynamics in colorectal adenomas indicates CDX2 gain as a predictor of recurrence.International journal of cancer · 2019Article
- The evolution of single cell-derived colorectal cancer cell lines is dominated by the continued selection of tumor-specific genomic imbalances, despite random chromosomal instability.Carcinogenesis · 2018Article
- Deconvolution and phylogeny inference of structural variations in tumor genomic samples.Bioinformatics (Oxford, England) · 2018Article
- The evolution of tumour phylogenetics: principles and practice.Nature reviews. Genetics · 2017Review
- The Cellular Origin and Evolution of Breast Cancer.Cold Spring Harbor perspectives in medicine · 2017Review
- An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.BioMed research international · 2017Article
- Big data need big theory too.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2016Review
- OncoNEM: inferring tumor evolution from single-cell sequencing data.Genome biology · 2016Article
- Computational Cancer Biology: An Evolutionary Perspective.PLoS computational biology · 2016Review
- Analysis of gene copy number changes in tumor phylogenetics.Algorithms for molecular biology : AMB · 2016Article
- Phylogenetic analysis of multiple FISH markers in oral tongue squamous cell carcinoma suggests that a diverse distribution of copy number changes is associated with poor prognosis.International journal of cancer · 2016Article
- FISHtrees 3.0: Tumor Phylogenetics Using a Ploidy Probe.PloS one · 2016Article
- A simplicial complex-based approach to unmixing tumor progression data.BMC bioinformatics · 2015Article
- Inferring models of multiscale copy number evolution for single-tumor phylogenetics.Bioinformatics (Oxford, England) · 2015Article
- BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies.Genome biology · 2015Article
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6 authors.
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Abstract
motivationDevelopment and progression of solid tumors can be attributed to a process of mutations, which typically includes changes in the number of copies of genes or genomic regions. Although comparisons of cells within single tumors show extensive heterogeneity, recurring features of their evolutionary process may be discerned by comparing multiple regions or cells of a tumor. A useful source of data for studying likely progression of individual tumors is fluorescence in situ hybridization (FISH), which allows one to count copy numbers of several genes in hundreds of single cells. Novel algorithms for interpreting such data phylogenetically are needed, however, to reconstruct likely evolutionary trajectories from states of single cells and facilitate analysis of tumor evolution.
resultsIn this article, we develop phylogenetic methods to infer likely models of tumor progression using FISH copy number data and apply them to a study of FISH data from two cancer types. Statistical analyses of topological characteristics of the tree-based model provide insights into likely tumor progression pathways consistent with the prior literature. Furthermore, tree statistics from the resulting phylogenies can be used as features for prediction methods. This results in improved accuracy, relative to unstructured gene copy number data, at predicting tumor state and future metastasis. AVAILABILITY: Source code for software that does FISH tree building (FISHtrees) and the data on cervical and breast cancer examined here are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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