dc.contributor.author | Chkhaidze, K | |
dc.contributor.author | Heide, T | |
dc.contributor.author | Werner, B | |
dc.contributor.author | Williams, MJ | |
dc.contributor.author | Huang, W | |
dc.contributor.author | Caravagna, G | |
dc.contributor.author | Graham, TA | |
dc.contributor.author | Sottoriva, A | |
dc.date.accessioned | 2019-07-23T15:18:48Z | |
dc.date.issued | 2019-07-29 | |
dc.identifier.citation | PLoS computational biology, 2019, 15 (7), pp. e1007243 - ? | |
dc.identifier.issn | 1553-734X | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/3304 | |
dc.identifier.eissn | 1553-7358 | |
dc.identifier.doi | 10.1371/journal.pcbi.1007243 | |
dc.description.abstract | Quantification 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.format | Electronic-eCollection | |
dc.format.extent | e1007243 - ? | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | PUBLIC LIBRARY SCIENCE | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.subject | Humans | |
dc.subject | Neoplasms | |
dc.subject | Stochastic Processes | |
dc.subject | Computational Biology | |
dc.subject | Cell Proliferation | |
dc.subject | Genetic Drift | |
dc.subject | Mutation | |
dc.subject | Models, Biological | |
dc.subject | Models, Genetic | |
dc.subject | Computer Simulation | |
dc.subject | Single-Cell Analysis | |
dc.subject | High-Throughput Nucleotide Sequencing | |
dc.subject | Clonal Evolution | |
dc.title | Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2019-07-05 | |
rioxxterms.versionofrecord | 10.1371/journal.pcbi.1007243 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2019-07-29 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | PLoS computational biology | |
pubs.issue | 7 | |
pubs.notes | No 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-status | Published | |
pubs.volume | 15 | |
pubs.embargo.terms | No embargo | |
icr.researchteam | Evolutionary Genomics & Modelling | |
icr.researchteam | Paediatric Solid Tumour Biology and Therapeutics | |
dc.contributor.icrauthor | Chkhaidze, Ketevan | |
dc.contributor.icrauthor | Heide, Timon | |
dc.contributor.icrauthor | Caravagna, Giulio | |
dc.contributor.icrauthor | Graham, Trevor | |
dc.contributor.icrauthor | Sottoriva, Andrea | |