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dc.contributor.authorCaravagna, Gen_US
dc.contributor.authorGiarratano, Yen_US
dc.contributor.authorRamazzotti, Den_US
dc.contributor.authorTomlinson, Ien_US
dc.contributor.authorGraham, TAen_US
dc.contributor.authorSanguinetti, Gen_US
dc.contributor.authorSottoriva, Aen_US
dc.coverage.spatialUnited Statesen_US
dc.date.accessioned2018-08-23T10:40:42Z
dc.date.issued2018-09en_US
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/30171232en_US
dc.identifier10.1038/s41592-018-0108-xen_US
dc.identifier.citationNat Methods, 2018, 15 (9), pp. 707 - 714en_US
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/2374
dc.identifier.eissn1548-7105en_US
dc.identifier.doi10.1038/s41592-018-0108-xen_US
dc.description.abstractRecurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression.en_US
dc.format.extent707 - 714en_US
dc.languageengen_US
dc.language.isoengen_US
dc.rights.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden_US
dc.subjectCell Line, Tumoren_US
dc.subjectCohort Studiesen_US
dc.subjectEvolution, Molecularen_US
dc.subjectHigh-Throughput Nucleotide Sequencingen_US
dc.subjectHumansen_US
dc.subjectMachine Learningen_US
dc.subjectNeoplasmsen_US
dc.subjectReproducibility of Resultsen_US
dc.subjectStochastic Processesen_US
dc.titleDetecting repeated cancer evolution from multi-region tumor sequencing data.en_US
dc.typeJournal Article
dcterms.dateAccepted2018-07-23en_US
rioxxterms.versionofrecord10.1038/s41592-018-0108-xen_US
rioxxterms.licenseref.urien_US
rioxxterms.licenseref.startdate2018-09en_US
rioxxterms.typeJournal Article/Reviewen_US
dc.relation.isPartOfNat Methodsen_US
pubs.issue9en_US
pubs.notes6 monthsen_US
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.publication-statusPublisheden_US
pubs.volume15en_US
pubs.embargo.terms6 monthsen_US
icr.researchteamEvolutionary Genomics & Modellingen_US
icr.researchteamPaediatric Solid Tumour Biology and Therapeuticsen_US
dc.contributor.icrauthorSottoriva, Andreaen_US
dc.contributor.icrauthorCaravagna, Giulioen_US


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