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dc.contributor.authorWerner, B
dc.contributor.authorCase, J
dc.contributor.authorWilliams, MJ
dc.contributor.authorChkhaidze, K
dc.contributor.authorTemko, D
dc.contributor.authorFernández-Mateos, J
dc.contributor.authorCresswell, GD
dc.contributor.authorNichol, D
dc.contributor.authorCross, W
dc.contributor.authorSpiteri, I
dc.contributor.authorHuang, W
dc.contributor.authorTomlinson, IPM
dc.contributor.authorBarnes, CP
dc.contributor.authorGraham, TA
dc.contributor.authorSottoriva, A
dc.date.accessioned2020-03-02T09:57:15Z
dc.date.issued2020-02-25
dc.identifier.citationNature communications, 2020, 11 (1), pp. 1035 - ?
dc.identifier.issn2041-1723
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3519
dc.identifier.eissn2041-1723
dc.identifier.doi10.1038/s41467-020-14844-6
dc.description.abstractBoth 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.
dc.formatElectronic
dc.format.extent1035 - ?
dc.languageeng
dc.language.isoeng
dc.publisherNATURE PUBLISHING GROUP
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectBrain
dc.subjectNeurons
dc.subjectHumans
dc.subjectNeoplasms
dc.subjectBayes Theorem
dc.subjectReproducibility of Results
dc.subjectCell Division
dc.subjectHematopoiesis
dc.subjectCell Survival
dc.subjectGenetic Heterogeneity
dc.subjectModels, Genetic
dc.subjectSingle-Cell Analysis
dc.subjectMutation Rate
dc.subjectMutation Accumulation
dc.subjectWhole Genome Sequencing
dc.titleMeasuring single cell divisions in human tissues from multi-region sequencing data.
dc.typeJournal Article
dcterms.dateAccepted2020-01-29
rioxxterms.versionofrecord10.1038/s41467-020-14844-6
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2020-02-25
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfNature communications
pubs.issue1
pubs.notesNo embargo
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Evolutionary Genomics & Modelling
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Evolutionary Genomics & Modelling
pubs.publication-statusPublished
pubs.volume11
pubs.embargo.termsNo embargo
icr.researchteamEvolutionary Genomics & Modelling
dc.contributor.icrauthorChkhaidze, Ketevan
dc.contributor.icrauthorCresswell, George
dc.contributor.icrauthorSpiteri Sagastume, Maria
dc.contributor.icrauthorGraham, Trevor
dc.contributor.icrauthorSottoriva, Andrea


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