Quantitative dynamics of T-cell receptor repertoires and their use for cancer patient stratification

T-cells in the adaptive immune system can recognise a vast space of potentially foreign antigens in a very specific way through their T-cell Receptors (TCR). As the genetic sequence encoding for the TCR is the result of somatic recombination, the TCR sequence varies from T-cell to T-cell, making it a functional barcode. The collection of TCRs present in an individual is known as a TCR repertoire, and it holds information about past and ongoing immune responses in that individual. Previous work has shown the use of TCR repertoire information to find evidence of infection history of certain viral infections. There is also evidence that TCRs that recognise cancer neoantigens undergo clonal expansion in peripheral blood and can be found at detectable clone frequencies. In this work, we explore the uses of quantitative analyses of longitudinal TCR repertoire samples in health and cancer to understand the clonal dynamics of TCR repertoires over time, and identify TCR clones with potential use for patient stratification based on their mutational status for the hotspot mutation IDH1 R132H in glioma. First, in Chapter 2 we deeply characterised the statistical properties of data generated from TCR repertoire sequencing, in order to understand the source and amount of expected technical noise in experimental data. Accordingly, we developed a statistical framework for the robust detection of individual TCR clones that behave in non-neutral way, which could be indicative of immune activation and response. We then applied this framework in Chapter 3 to a dataset of longitudinal TCR repertoire samples taken over the period of one year from 3 clinically healthy individuals. In order to find groups of TCR clones showing coordinated response dynamics, we defined an appropriate dynamic distance metric. We found 9, 7 and 15 groups of TCR clones with strongly correlated temporal behaviour in each individual, with evidence of shared specificity in a subset of them. The suspicion of relatively high levels of cross-contamination in this longitudinal dataset and others led us to the development of a systematic contamination detection and cleaning framework for TCR repertoire data. In Chapter 4 we describe the steps of the framework and showcase its use in 3 different datasets, highlighting the importance of decontamination when the signal of interest is TCR sharing or clonal expansion in longitudinal data. Lastly, in Chapter 5 we applied the non-neutral TCR clone detection framework to a dataset of longitudinal TCR repertoires from glioma patients treated with a IDH1 R132H neoantigen based peptide vaccine. We identified 291 vaccine-associated TCR (vaTCR) clones that are likely to be responding to this specific neoantigen and show evidence of convergent function across different people. By generating TCR repertoire data from an independent cohort of glioma patients made up of individuals with tumours that are positive or negative for IDH1 R132H, we test the ability of the 291 vaTCRs to stratify patients based on IDH1 mutational status. We found that, by scoring repertoires based on the frequency of their repertoire taken up by TCR clones with similar CDR3 sequences to the vaTCRs, we can successfully classify patients into those who have a IDH1 R132H mutation and those who do not. This work highlights the ways in which quantitative analysis of longitudinal TCR repertoires can detect individual TCRs of interest in different contexts. We present some statistical approaches that enable such analyses and minimise the impact of cross-contamination, and provide a proof-of-concept study for the potential diagnostic capabilities of likely neoantigen-specific TCRs in the context of cancer.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Ayestaran Basagoitia, Inigo

Advisor dc:contributor.advisor

• Blundell, Jamie

• T-cell repertoire
• immunology
• early cancer detection

Licence

• https://www.repository.cam.ac.uk/bitstreams/db45e992-8993-4968-aa38-5e89e82e41f1/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.118268

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/384175