ArticlePLoS computational biology2014
Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Theoretical estimates on the expected number of mutations needed to reconstruct clonal lineage trees.Bioinformatics advances · 2026Article
- Single-cell copy number calling and event history reconstruction.Bioinformatics (Oxford, England) · 2025Article
- Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data.Cancer pathogenesis and therapy · 2025Review
- scAbsolute: measuring single-cell ploidy and replication status.Genome biology · 2024Article
- CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples.Genome 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
- Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL.Nature biotechnology · 2021Article
- Copy number evolution with weighted aberrations in cancer.Bioinformatics (Oxford, England) · 2020Article
- Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks.GigaScience · 2019Article
- Deconvolution and phylogeny inference of structural variations in tumor genomic samples.Bioinformatics (Oxford, England) · 2018Article
- Phylogenetic Copy-Number Factorization of Multiple Tumor Samples.Journal of computational biology : a journal of computational molecular cell biology · 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
- Complexity and algorithms for copy-number evolution problems.Algorithms for molecular biology : AMB · 2017Article
- An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.BioMed research international · 2017Article
- Computational Methods for Characterizing Cancer Mutational Heterogeneity.Frontiers in genetics · 2017Review
- OncoNEM: inferring tumor evolution from single-cell sequencing data.Genome biology · 2016Article
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Authors and funding
6 authors.
Funding
Abstract
We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome. We then apply this theory to develop a practical heuristic algorithm, implemented in publicly available software, for inferring tumor phylogenies on data from potentially hundreds of single cells by this evolutionary model. We demonstrate and validate the methods on simulated data and published FISH data from cervical cancers and breast cancers. Our computational experiments show that the new model and algorithm lead to more parsimonious trees than prior methods for single-tumor phylogenetics and to improved performance on various classification tasks, such as distinguishing primary tumors from metastases obtained from the same patient population.
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