Ismail, Mazlina; (2019) Computational analysis of T cell receptor repertoires in health and disease. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

The T cell receptor (TCR) repertoire can be regarded as an example of a high-dimensional and personalized biomarker, and with the advancements made in high-throughput sequencing technology, the repertoire can be studied in greater detail. To this end, the Chain group recently developed a reliable and economical amplification protocol that can be used to characterize the TCR repertoire using next-generation sequencing techniques, and a robust computational pipeline for quantitative downstream analysis. Introduction of barcodes, which label every cDNA molecule before amplification, allow for correction of \gls{PCR} bias, and PCR and sequencing error. The protocol can be used to sequence TCR repertoires of diverse types of samples, including ex vivo collections of whole \gls{FACS} fractionated blood or tissue, or after in vitro culture and expansion. In this thesis, I have looked at the different metrics that can be used to describe the TCR repertoire in healthy and disease settings. I firstly demonstrate that our protocol is fairly robust across two sequencing platforms, and that I am able to quantitatively measure the efficiency. I then used different metrics to measure the repertoire in two disease settings; primary immunodeficiency and early-stage non-small-cell lung cancer. Where applicable, I have compared them with the repertoire of relatively healthy individuals. As the size of the cohorts are continually expanding, the analyses that I carried out in this thesis has provided a good starting point for future work. This will provide more solid evidence to derive meaningful, biologically relevant conclusions. Quantitative analysis of the TCR repertoire in these two cohorts may provide a better understanding of the immune landscape involving T cell response, which may lead to insights into the pathogenesis of these diseases. Additional information may be obtained with which can then be used to develop tools to improve patient stratification, prediction of disease outcome, and patient response to treatment.

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