dc.contributor.author | Caravagna, G | |
dc.contributor.author | Heide, T | |
dc.contributor.author | Williams, MJ | |
dc.contributor.author | Zapata, L | |
dc.contributor.author | Nichol, D | |
dc.contributor.author | Chkhaidze, K | |
dc.contributor.author | Cross, W | |
dc.contributor.author | Cresswell, GD | |
dc.contributor.author | Werner, B | |
dc.contributor.author | Acar, A | |
dc.contributor.author | Chesler, L | |
dc.contributor.author | Barnes, CP | |
dc.contributor.author | Sanguinetti, G | |
dc.contributor.author | Graham, TA | |
dc.contributor.author | Sottoriva, A | |
dc.date.accessioned | 2020-08-14T15:17:51Z | |
dc.date.issued | 2020-09-01 | |
dc.identifier.citation | Nature genetics, 2020, 52 (9), pp. 898 - 907 | |
dc.identifier.issn | 1061-4036 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/3966 | |
dc.identifier.eissn | 1546-1718 | |
dc.identifier.doi | 10.1038/s41588-020-0675-5 | |
dc.description.abstract | Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their evolutionary history. However, current approaches are entirely data driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated with multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction, called MOBSTER, which combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show that this method is more robust and accurate than current techniques in single-sample, multiregion and longitudinal data. This approach minimizes the confounding factors of nonevolutionary methods, thus leading to more accurate recovery of the evolutionary history of human cancers. | |
dc.format | Print-Electronic | |
dc.format.extent | 898 - 907 | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | NATURE PUBLISHING GROUP | |
dc.rights.uri | https://www.rioxx.net/licenses/under-embargo-all-rights-reserved | |
dc.subject | Humans | |
dc.subject | Neoplasms | |
dc.subject | Genetics, Population | |
dc.subject | Genomics | |
dc.subject | Clonal Evolution | |
dc.subject | Machine Learning | |
dc.subject | Whole Genome Sequencing | |
dc.title | Subclonal reconstruction of tumors by using machine learning and population genetics. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2020-07-01 | |
rioxxterms.versionofrecord | 10.1038/s41588-020-0675-5 | |
rioxxterms.licenseref.uri | https://www.rioxx.net/licenses/under-embargo-all-rights-reserved | |
rioxxterms.licenseref.startdate | 2020-09-02 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Nature genetics | |
pubs.issue | 9 | |
pubs.notes | Not known | |
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 | 52 | |
pubs.embargo.terms | Not known | |
icr.researchteam | Evolutionary Genomics & Modelling | |
icr.researchteam | Paediatric Solid Tumour Biology and Therapeutics | |
dc.contributor.icrauthor | Heide, Timon | |
dc.contributor.icrauthor | Zapata Ortiz, Luis | |
dc.contributor.icrauthor | Chkhaidze, Ketevan | |
dc.contributor.icrauthor | Cresswell, George | |
dc.contributor.icrauthor | Chesler, Louis | |
dc.contributor.icrauthor | Graham, Trevor | |
dc.contributor.icrauthor | Sottoriva, Andrea | |