Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.699969
Title: Network biology and machine learning approaches to metastasis and treatment response
Author: Lubbock, Alexander Lyulph Robert
ISNI:       0000 0004 5991 1319
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2014
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Abstract:
Cancer causes 13% of human deaths worldwide, 90% of which involve metastasis. The reactivation of embryonic processes in epithelial cancers—and the epithelial-mesenchymal transition (EMT) in particular—results in increased cell motility and invasiveness, and is a known mechanism for initiating metastasis. The reverse process, the mesenchymal-epithelial transition (MET), is implicated in the process of cells colonising pre-metastatic niches. Understanding the relationships between EMT, MET and metastasis is therefore highly relevant to cancer research and treatment. Key challenges include deciphering the large, uncharted space of gene function, mapping the complex signalling networks involved and understanding how the EMT and MET programmes function in vivo within specific environments and disease contexts. Inference and analysis of small-scale networks from human tumour tissue samples, scored for protein expression, provides insight into pleiotropy, complex interactions and context-specific behaviour. Small sets of proteins (10–50, representative of key biological processes) are scored using quantitative antibody-based technologies (e.g. immunofluorescence) to give static expression values. A novel inference algorithm specifically for these data, Gabi, is presented, which produces signed, directed networks. On synthetic data, inferred networks often recapitulate the information flow between proteins in ground truth connectivity. Directionality predictions are highly accurate (90% correct) if the input network structure is itself accurate. The Gabi algorithm was applied to study multiple carcinomas (renal, breast, ovarian), providing novel insights into the relationships between EMT players and fundamental processes dysregulated in cancers (e.g. apoptosis, proliferation). Survival analysis on these cohorts shows further evidence for association of EMT with poor outcome. A patent-pending method is presented for stratifying response to sunitinib in metastatic renal cancer patients. The method is based on a proportional hazards model with predictive features selected automatically using regularisation (Bayesian information criterion). The final algorithm includes N-cadherin expression, a determinant of mesenchymal properties, and shows significant predictive power (p = 7.6x10-7, log-rank test). A separate method stratifies response to tamoxifen in estrogen-receptor positive, node-negative breast cancer patients using a cross-validated support vector machine (SVM). The algorithm was predictive on blind-test data (p = 4.92 x 10-6, log-rank test). Methods developed have been made available within a web application (TMA Navigator) and an R package (rTMA). TMA Navigator produces visual data summaries, networks and survival analysis for uploaded tissue microarray (TMA) scores. rTMA expands on TMA Navigator capabilities for advanced workflows within a programming environment.
Supervisor: Baldock, Richard ; Overton, Ian ; Harrison, David Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.699969  DOI: Not available
Keywords: networks ; cancer ; systems medicine ; predictive medicine
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