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dc.contributor.authorFlensburg, Christoffer
dc.contributor.authorSargeant, Tobias
dc.contributor.authorOshlack, Alicia
dc.contributor.authorMajewski, Ian J
dc.date.accessioned2020-02-26T23:10:43Z
dc.date.available2020-02-26T23:10:43Z
dc.date.issued2020-02
dc.date.submitted2019-07-03
dc.identifier.issn1553-734X
dc.identifier.otherpcompbiol-d-19-01106
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/302781
dc.description.abstractAnalysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples.
dc.languageen
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectResearch Article
dc.subjectBiology and life sciences
dc.subjectResearch and analysis methods
dc.subjectMedicine and health sciences
dc.subjectEngineering and technology
dc.titleSuperFreq: Integrated mutation detection and clonal tracking in cancer.
dc.typeArticle
dc.date.updated2020-02-26T23:10:42Z
prism.issueIdentifier2
prism.publicationNamePLoS Comput Biol
prism.volume16
dc.identifier.doi10.17863/CAM.49856
dcterms.dateAccepted2019-12-11
rioxxterms.versionofrecord10.1371/journal.pcbi.1007603
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Markowetz, Florian
dc.contributor.orcidFlensburg, Christoffer [0000-0002-3280-8966]
dc.contributor.orcidOshlack, Alicia [0000-0001-9788-5690]
dc.contributor.orcidMajewski, Ian J [0000-0002-6087-635X]
dc.identifier.eissn1553-7358
cam.issuedOnline2020-02-13


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)