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dc.contributor.authorChkhaidze, K
dc.contributor.authorHeide, T
dc.contributor.authorWerner, B
dc.contributor.authorWilliams, MJ
dc.contributor.authorHuang, W
dc.contributor.authorCaravagna, G
dc.contributor.authorGraham, TA
dc.contributor.authorSottoriva, A
dc.date.accessioned2019-07-23T15:18:48Z
dc.date.issued2019-07-29
dc.identifier.citationPLoS computational biology, 2019, 15 (7), pp. e1007243 - ?
dc.identifier.issn1553-734X
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3304
dc.identifier.eissn1553-7358
dc.identifier.doi10.1371/journal.pcbi.1007243
dc.description.abstractQuantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constraints, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from both bulk and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We also propose a statistical inference framework that incorporates spatial effects within a growing tumour and so represents a further step forwards in the inference of evolutionary dynamics from genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors remains challenging. However, mechanistic model-based approaches have the potential to capture the sources of noise and better interpret the data.
dc.formatElectronic-eCollection
dc.format.extente1007243 - ?
dc.languageeng
dc.language.isoeng
dc.publisherPUBLIC LIBRARY SCIENCE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectHumans
dc.subjectNeoplasms
dc.subjectStochastic Processes
dc.subjectComputational Biology
dc.subjectCell Proliferation
dc.subjectGenetic Drift
dc.subjectMutation
dc.subjectModels, Biological
dc.subjectModels, Genetic
dc.subjectComputer Simulation
dc.subjectSingle-Cell Analysis
dc.subjectHigh-Throughput Nucleotide Sequencing
dc.subjectClonal Evolution
dc.titleSpatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data.
dc.typeJournal Article
dcterms.dateAccepted2019-07-05
rioxxterms.versionofrecord10.1371/journal.pcbi.1007243
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2019-07-29
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfPLoS computational biology
pubs.issue7
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/Cancer Therapeutics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics/Paediatric Solid Tumour Biology and Therapeutics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Paediatric Solid Tumour Biology and Therapeutics
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/Primary Group/ICR Divisions/Molecular Pathology/Paediatric Solid Tumour Biology and Therapeutics
pubs.organisational-group/ICR/Students
pubs.organisational-group/ICR/Students/PhD and MPhil
pubs.organisational-group/ICR/Students/PhD and MPhil/16/17 Starting Cohort
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/Cancer Therapeutics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics/Paediatric Solid Tumour Biology and Therapeutics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Paediatric Solid Tumour Biology and Therapeutics
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/Primary Group/ICR Divisions/Molecular Pathology/Paediatric Solid Tumour Biology and Therapeutics
pubs.organisational-group/ICR/Students
pubs.organisational-group/ICR/Students/PhD and MPhil
pubs.organisational-group/ICR/Students/PhD and MPhil/16/17 Starting Cohort
pubs.publication-statusPublished
pubs.volume15
pubs.embargo.termsNo embargo
icr.researchteamEvolutionary Genomics & Modelling
icr.researchteamPaediatric Solid Tumour Biology and Therapeutics
dc.contributor.icrauthorChkhaidze, Ketevan
dc.contributor.icrauthorHeide, Timon
dc.contributor.icrauthorCaravagna, Giulio
dc.contributor.icrauthorGraham, Trevor
dc.contributor.icrauthorSottoriva, Andrea


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