ArticleRoyal Society open science2017
Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer.
Article in Royal Society open science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- Onco-Breastomics: An Eco-Evo-Devo Holistic Approach.International journal of molecular sciences · 2024Review
- Interpreting the pervasive observation of U-shaped Site Frequency Spectra.PLoS genetics · 2023Article
- Article
- Population genetics of clonally transmissible cancers.Nature ecology & evolution · 2022Review
- Single-cell DNA and RNA sequencing reveals the dynamics of intra-tumor heterogeneity in a colorectal cancer model.BMC biology · 2021Article
- Multiple Merger Genealogies in Outbreaks of Mycobacterium tuberculosis.Molecular biology and evolution · 2021Article
- tugHall: a simulator of cancer-cell evolution based on the hallmarks of cancer and tumor-related genes.Bioinformatics (Oxford, England) · 2020Article
- Cannings models, population size changes and multiple-merger coalescents.Journal of mathematical biology · 2020Article
- Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer.Royal Society open science · 2017Article
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
Single-cell sequencing is a promising technology that can address cancer cell evolution by identifying genetic alterations in individual cells. In a recent study, genome-wide DNA copy numbers of single cells were accurately quantified by single-cell sequencing in breast cancers. Phylogenetic-tree analysis revealed genetically distinct populations, each consisting of homogeneous cells. Bioinformatics methods based on population genetics should be further developed to quantitatively analyse the single-cell sequencing data. We developed a bioinformatics framework that was combined with molecular-evolution theories to analyse copy-number losses. This analysis revealed that most deletions in the breast cancers at the single-cell level were generated by simple stochastic processes. A non-standard type of coalescent theory, the multiple-merger coalescent model, aided by approximate Bayesian computation fit well with the data, allowing us to estimate the population-genetic parameters in addition to false-positive and false-negative rates. The estimated parameters suggest that the cancer cells underwent sweepstake evolution, where only one or very few parental cells produced a descendent cell population. We conclude that breast cancer cells successively substitute in a tumour mass, and the high reproduction of only a portion of cancer cells may confer high adaptability to this cancer.
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