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dc.contributor.authorVerleyen, Wim
dc.contributor.authorLangdon, Simon P
dc.contributor.authorFaratian, Dana
dc.contributor.authorHarrison, David James
dc.contributor.authorSmith, V Anne
dc.identifier.citationVerleyen , W , Langdon , S P , Faratian , D , Harrison , D J & Smith , V A 2015 , ' Novel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosis ' , Scientific Reports , vol. 5 , 15563 .
dc.identifier.otherPURE: 218682431
dc.identifier.otherPURE UUID: 31242ce3-8b06-4a78-8d39-ac711e7ab8c0
dc.identifier.otherScopus: 84946070258
dc.identifier.otherORCID: /0000-0002-0487-2469/work/32209211
dc.identifier.otherWOS: 000363452500002
dc.identifier.otherORCID: /0000-0001-9041-9988/work/64034263
dc.descriptionWV is a SULSA Systems Biology Prize PhD Student; VAS is supported by the BBSRC Research Council [grant number BB/F001398/1] and Medical Research Scotland [grant number FRG353]. DJH is supported by CASyM Concerted Action [grant number EU HEALTH-F4-2012-305033] and the Chief Scientist Office of Scotland.en
dc.description.abstractCurrent clinical practice in cancer stratifies patients based on tumour histology to determine prognosis. Molecular profiling has been hailed as the path towards personalised care, but molecular data are still typically analysed independently of known clinical information. Conventional clinical and histopathological data, if used, are added only to improve a molecular prediction, placing a high burden upon molecular data to be informative in isolation. Here, we develop a novel Monte Carlo analysis to evaluate the usefulness of data assemblages. We applied our analysis to varying assemblages of clinical data and molecular data in an ovarian cancer dataset, evaluating their ability to discriminate one-year progression-free survival (PFS) and three-year overall survival (OS). We found that Cox proportional hazard regression models based on both data types together provided greater discriminative ability than either alone. In particular, we show that proteomics data assemblages that alone were uninformative (p = 0.245 for PFS, p = 0.526 for OS) became informative when combined with clinical information (p = 0.022 for PFS, p = 0.048 for OS). Thus, concurrent analysis of clinical and molecular data enables exploitation of prognosis-relevant information that may not be accessible from independent analysis of these data types.
dc.relation.ispartofScientific Reportsen
dc.rightsCopyright © 2019 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit
dc.subjectQH301 Biologyen
dc.titleNovel Monte Carlo approach quantifies data assemblage utility and reveals power of integrating molecular and clinical information for cancer prognosisen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews.Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.description.statusPeer revieweden

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