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Title: Monitoring viral infections and immune repertoires in transplanted children : a statistical approach
Author: Margetts, Ben Keith
ISNI:       0000 0004 8500 0818
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Transplantation in children is a relatively high-risk procedure, where pharmacological treatment and disease often render the recipient immunocompromised. This state allows typically non-serious infectious organisms such as cytomegalovirus (CMV) to grow uninhibited, leading to morbidity and mortality in the host. Underpinning the dynamics of these infections lies the immune response, driven in part by T cells and their innate ability to recognise pathogens via the T cell receptor (TCR). Applying deep sequencing techniques to the TCR repertoire allows researchers to explore this cellular response in unprecedented resolution. This thesis explores the application of statistical modelling and data analysis techniques to two key areas of interest: the reconstituting immune response and the consequences of immunodeficiency within transplanted children. Specifically, the first part of this thesis focuses on the TCR repertoire, opening with an automated TCR next generation sequencing (NGS) data processing, subsampling, and analysis pipeline. This pipeline is then applied to NGS data from two distinct patient cohorts, facilitating a detailed statistical analysis on the clinical applicability of TCR deep sequencing, alongside an analysis of repertoire reconstitution. Lastly, open access complementarity determining region 3 (CDR3) epitope specificity data are integrated with the processed sequencing data to model antigen-specific CDR3 sequence profiles using profile hidden Markov models (PHMMs) and unsupervised CDR3 sequence clustering. This section of the thesis explores the utility of CDR3 sequencing for clinical TCR repertoire evaluation and demonstrates the wide-ranging potential applications of the sequencing data. The second part of the thesis focuses on CMV infections, opening with a quantitative clinical audit on the treatment of CMV infections post-haematopoietic stem cell transplant (HSCT). The audit includes an analysis of the efficacy of anti-CMV drugs in children and a viral load-based time-to-event (survival) model. This work is then expanded with a Bayesian nonlinear mixed effects model for predicting CMV loads in infected transplant recipients, using a workflow that prioritises identifiability and biological plausibility of model parameters.
Supervisor: Standing, J. ; Breuer, J. ; Klein, N. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available