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Measuring single cell divisions in human tissues from multi-region sequencing data

Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a si...

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Autores principales: Werner, Benjamin, Case, Jack, Williams, Marc J., Chkhaidze, Ketevan, Temko, Daniel, Fernández-Mateos, Javier, Cresswell, George D., Nichol, Daniel, Cross, William, Spiteri, Inmaculada, Huang, Weini, Tomlinson, Ian P. M., Barnes, Chris P., Graham, Trevor A., Sottoriva, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042311/
https://www.ncbi.nlm.nih.gov/pubmed/32098957
http://dx.doi.org/10.1038/s41467-020-14844-6
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author Werner, Benjamin
Case, Jack
Williams, Marc J.
Chkhaidze, Ketevan
Temko, Daniel
Fernández-Mateos, Javier
Cresswell, George D.
Nichol, Daniel
Cross, William
Spiteri, Inmaculada
Huang, Weini
Tomlinson, Ian P. M.
Barnes, Chris P.
Graham, Trevor A.
Sottoriva, Andrea
author_facet Werner, Benjamin
Case, Jack
Williams, Marc J.
Chkhaidze, Ketevan
Temko, Daniel
Fernández-Mateos, Javier
Cresswell, George D.
Nichol, Daniel
Cross, William
Spiteri, Inmaculada
Huang, Weini
Tomlinson, Ian P. M.
Barnes, Chris P.
Graham, Trevor A.
Sottoriva, Andrea
author_sort Werner, Benjamin
collection PubMed
description Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/death rates.
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spelling pubmed-70423112020-03-04 Measuring single cell divisions in human tissues from multi-region sequencing data Werner, Benjamin Case, Jack Williams, Marc J. Chkhaidze, Ketevan Temko, Daniel Fernández-Mateos, Javier Cresswell, George D. Nichol, Daniel Cross, William Spiteri, Inmaculada Huang, Weini Tomlinson, Ian P. M. Barnes, Chris P. Graham, Trevor A. Sottoriva, Andrea Nat Commun Article Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/death rates. Nature Publishing Group UK 2020-02-25 /pmc/articles/PMC7042311/ /pubmed/32098957 http://dx.doi.org/10.1038/s41467-020-14844-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Werner, Benjamin
Case, Jack
Williams, Marc J.
Chkhaidze, Ketevan
Temko, Daniel
Fernández-Mateos, Javier
Cresswell, George D.
Nichol, Daniel
Cross, William
Spiteri, Inmaculada
Huang, Weini
Tomlinson, Ian P. M.
Barnes, Chris P.
Graham, Trevor A.
Sottoriva, Andrea
Measuring single cell divisions in human tissues from multi-region sequencing data
title Measuring single cell divisions in human tissues from multi-region sequencing data
title_full Measuring single cell divisions in human tissues from multi-region sequencing data
title_fullStr Measuring single cell divisions in human tissues from multi-region sequencing data
title_full_unstemmed Measuring single cell divisions in human tissues from multi-region sequencing data
title_short Measuring single cell divisions in human tissues from multi-region sequencing data
title_sort measuring single cell divisions in human tissues from multi-region sequencing data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042311/
https://www.ncbi.nlm.nih.gov/pubmed/32098957
http://dx.doi.org/10.1038/s41467-020-14844-6
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