Subsequently, longitudinal analysis revealed TCR distributions that suggested the presence of cytotoxic T cells which was further characterised in matched single-cell RNA sequencing data. A statistical framework was developed in order to efficiently distinguish leukaemic re-arrangements from the non- leukaemic TCR repertoire of B-ALL patients. Finally, the same methodology was applied to bone marrow samples harvested from B cell acute lymphoblastic leukaemia (B-ALL) patients. In addition, RNA-sequencing analysis identified a gene expression profile consistent with specific activation of T cells through TCR signalling. TCRs with frequency distribution characteristics similar to what was observed in NSCLC were maintained upon treatment and associated with clinical response. The impact of immune checkpoint blockade therapy on the TCR repertoire distribution was assessed in advanced renal cell carcinoma in the context of anti- PD1 treatment. A novel method was built in order to identify distinct TCR populations that spatially follow the pattern of the well-established clonal/subclonal mutational dichotomy. In treatment-naive non-small cell lung cancer (NSCLC) patients, multi-region TCR sequencing revealed a pattern of heterogeneity in the TCR repertoire resembling the heterogeneity observed in the mutational profile of these tumours and a range of clonotype frequency values associated with tumour specificity. In this thesis, I explore how T cell receptor (TCR) sequencing data in multi-omics contexts can be utilised to identify features associated with antigen exposure in cancer patients. In particular, the specificity of the anti-tumour immune response pre-existing in treatment-naive patients or induced by treatment remains poorly described. The clinical success of immunotherapies demonstrates the importance of the immune system in tumour control, but the response rates remain low and many biological mechanisms underlying how these therapies work are still uncharacterised. The figure shows the distribution obtained before (left) and after (right) error correction using UMIs. the leftmost point represents the number of TCRs that occur only once in the sample, the next point the number that occurs twice, etc. The number of TCRs with each abundance observed is plotted against the abundance itself (labeled TCR abundance), e.g. (B) The effects of error correction on sequence abundance data for a set of TCR alpha and beta sequences obtained from a sample of unfractionated peripheral blood. Optionally, barcodes within a specified molecular distance of each other (usually 1 or 2 Hamming units) can be clustered together. same TCR, different UMI) gives the corrected abundance count for that TCR. Minority variants within a cluster are similarly merged with the majority variant. with the same molecular barcode) are collapsed to a count of 1. Each TCR is associated with a UMI, which acts as a molecular barcode. (A) Schematic of the error-correction process. Computational analysis will provide the key to unlock the potential of the T-cell receptor repertoire to give insight into the fundamental biology of the adaptive immune system and to provide powerful biomarkers of disease.Įrror correction using UMIs. Sophisticated machine learning algorithms are being developed that can combine the paradoxical degeneracy and cross-reactivity of individual T-cell receptors with the specificity of the overall T-cell immune response. Finally, we discuss the major challenge of linking T-cell receptor sequence to function, and specifically to antigen recognition. High-level processing can measure the diversity of the repertoire in different samples, quantify V and J usage and identify private and public T-cell receptors.
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The latest generation of bioinformatics tools allows millions of DNA sequences to be accurately and rapidly assigned to their respective variable V and J gene segments, and to reconstruct an almost error-free representation of the non-templated additions and deletions that occur. We outline the major steps in processing of repertoire data, considering low-level processing of raw sequence files and high-level algorithms, which seek to extract biological or pathological information.
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However, the extraordinary heterogeneity of the immune repertoire poses significant challenges for subsequent analysis of the data. Massively parallel high-throughput sequencing allows millions of different T-cell receptor genes to be characterized from a single sample of blood or tissue. T-cell specificity is determined by the T-cell receptor, a heterodimeric protein coded for by an extremely diverse set of genes produced by imprecise somatic gene recombination.