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dc.contributor.authorOpasic, Len_US
dc.contributor.authorZhou, Den_US
dc.contributor.authorWerner, Ben_US
dc.contributor.authorDingli, Den_US
dc.contributor.authorTraulsen, Aen_US
dc.date.accessioned2020-03-02T15:21:46Z
dc.date.available2019-04-11en_US
dc.date.issued2019-04-29en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/62989
dc.description.abstractBACKGROUND: Modern cancer treatment strategies aim to target tumour specific genetic (or epigenetic) alterations. Treatment response improves if these alterations are clonal, i.e. present in all cancer cells within tumours. However, the identification of truly clonal alterations is impaired by the tremendous intra-tumour genetic heterogeneity and unavoidable sampling biases. METHODS: Here, we investigate the underlying causes of these spatial sampling biases and how the distribution and sizes of biopsies in sampling protocols can be optimised to minimize such biases. RESULTS: We find that in the ideal case, less than a handful of samples can be enough to infer truly clonal mutations. The frequency of the largest sub-clone at diagnosis is the main factor determining the accuracy of truncal mutation estimation in structured tumours. If the first sub-clone is dominating the tumour, higher spatial dispersion of samples and larger sample size can increase the accuracy of the estimation. In such an improved sampling scheme, fewer samples will enable the detection of truly clonal alterations with the same probability. CONCLUSIONS: Taking spatial tumour structure into account will decrease the probability to misclassify a sub-clonal mutation as clonal and promises better informed treatment decisions.en_US
dc.format.extent403 - ?en_US
dc.languageengen_US
dc.relation.ispartofBMC Canceren_US
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.subjectClonal mutationsen_US
dc.subjectIntratumour heterogeneityen_US
dc.subjectSomatic evolutionen_US
dc.subjectSpatial modelen_US
dc.subjectTargeted therapyen_US
dc.subjectTruncal mutationsen_US
dc.subjectAlgorithmsen_US
dc.subjectCell Counten_US
dc.subjectClone Cellsen_US
dc.subjectGenetic Heterogeneityen_US
dc.subjectHumansen_US
dc.subjectModels, Theoreticalen_US
dc.subjectMutationen_US
dc.subjectNeoplasmsen_US
dc.titleHow many samples are needed to infer truly clonal mutations from heterogenous tumours?en_US
dc.typeArticle
dc.rights.holder© The Author(s). 2019
dc.identifier.doi10.1186/s12885-019-5597-1en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/31035962en_US
pubs.issue1en_US
pubs.notesNot knownen_US
pubs.publication-statusPublished onlineen_US
pubs.volume19en_US
dcterms.dateAccepted2019-04-11en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
rioxxterms.funder.project483cf8e1-88a1-4b8b-aecb-8402672d45f8en_US


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