Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer

Kato, M., Vasco, D. A., Sugino, R., Narushima, D., Krasnitz, A. (September 2017) Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer. R Soc Open Sci, 4 (9). p. 171060. ISSN 2054-5703 (Print)2054-5703

URL: https://www.ncbi.nlm.nih.gov/pubmed/28989791
DOI: 10.1098/rsos.171060

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.

Item Type: Paper
Uncontrolled Keywords: bioinformatics cancer genomics coalescent theory copy-number alteration molecular evolution single-cell sequencing
Subjects: bioinformatics
diseases & disorders > cancer > cancer types > breast cancer
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > copy number variants
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > population genetics
CSHL Authors:
Communities: CSHL labs > Krasnitz lab
CSHL Cancer Center Program > Cancer Genetics
Depositing User: Matt Covey
Date: September 2017
Date Deposited: 11 Oct 2017 19:38
Last Modified: 12 Jun 2018 16:08
PMCID: PMC5627131
Related URLs:
URI: http://repository.cshl.edu/id/eprint/35566

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