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dc.contributor.authorGadaleta, Emanuela
dc.identifier.citationGadaleta, E. 2015. A Multidisciplinary Computational Approach to Model Cancer–omics Data: Organising, Integrating and Mining Multiple Sources of Data. Queen Mary University of Londonen_US
dc.description.abstractIt is imperative that the cancer research community has the means with which to effectively locate, access, manage, analyse and interpret the plethora of data values being generated by novel technologies. This thesis addresses this unmet requirement by using pancreatic cancer and breast cancer as prototype malignancies to develop a generic integrative transcriptomic model. The analytical workflow was initially applied to publicly available pancreatic cancer data from multiple experimental types. The transcriptomic landscape of comparative groups was examined both in isolation and relative to each other. The main observations included (i) a clear separation of profiles based on experimental type, (ii) identification of three subgroups within normal tissue samples resected adjacent to pancreatic cancer, each showing disruptions to biofunctions previously associated with pancreatic cancer (iii) and that cell lines and xenograft models are not representative of changes occurring during pancreatic tumourigenesis. Previous studies examined transcriptomic profiles across 306 biological and experimental samples, including breast cancer. The plethora of clinical and survival data readily available for breast cancer, compared to the paucity of publicly available pancreatic cancer data, allowed for expansion of the pipeline’s infrastructure to include functionalities for cross-platform and survival analysis. Application of this enhanced pipeline to multiple cohorts of triple negative and basal-like breast cancers identified differential risk groups within these breast cancer subtypes. All of the main experimental findings of this thesis are being integrated with the Pancreatic Expression Database and the Breast Cancer Campaign Tissue Bank bioinformatics portal, which enhances the sharing capacity of this information and ensures its exposure to a wider audienceen_US
dc.description.sponsorshipEngineering and Physical Sciences Research Council and the Barts Cancer Instituteen_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectcancer researchen_US
dc.subjectbreast canceren_US
dc.subjectpancreatic canceren_US
dc.titleA Multidisciplinary Computational Approach to Model Cancer–omics Data: Organising, Integrating and Mining Multiple Sources of Dataen_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author

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  • Theses [2768]
    Theses Awarded by Queen Mary University of London

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