T cell receptors for therapeutic application

PhD Student: Jing-Wun Li
Supervisor at TUD: Anne Eugster, Ezio Bonifacio
Supervisor at KCL: Timothy Tree, Stephanie Amiel 
Start Date: 01.05.2020

T cell receptors (TCRs) provide specificity to the adaptive immune system. TCR diversity arises from somatic recombination of multiple genes, the combination of alpha and beta or delta and gamma chains and the deletion and addition of templated nucleotides. Specificity is largely defined by the CDR3 region of the TCR. The strength or affinity of binding is related to the fit of this region to the antigenic peptide/HLA molecule complex. High affinity binding should result in specific and effective responses to infection, and aberrant cells such as tumour cells. Thus, even in patients with autoimmune diseases such as T1D, where increased numbers of autoreactive T cells leave the thymus, the affinity of self-reactive TCRs on these cells is reported to be relatively. We have developed and applied methods to understand TCR repertoires in general and against specific antigens and peptides revealing large diversity, but also extensive clonal expansion of CD8⁺ T cells with similar motifs for viruses such as CMV and COVID-19.

The project will develop a CAR-Treg approach for T1D. We propose that the combined study of TCR-peptide ‘coordinates’ and the engineering of regulatory T cells (Treg) to express high affinity TCR against β-cell antigens will provide novel therapeutic avenues for preserving β-cells in T1D. We will use an approach of combinatorial peptide pools with multiple dye combinations to al-low high throughput screening of T cell responsiveness to a large range of peptides. The technique includes identification of responsive CD8⁺ T cells using the CD137 and CD25 markers and simplified processing using dye and DNA tag combinations for multiple markers in wells stimulated with varying pools of viral peptides.

We have shown that such a strategy allows pooling and processing of a large number of samples and that after deconvolution of cell dyes and DNA tags, it can match TCRs to their stimulating peptide. It is expected that through machine learning, essential TCR amino acid sequence coordinates for responses to a range of peptides can be obtained and used to predict and design high affinity TCRs against theoretical peptides. Second, we will focus on identifying high affinity TCRs that are specific to pancreatic islet cell proteins. A source of high affinity cells is surgically removed thymus from infants as this will contain cells prior to thymic selection and therefore with high-affinity to self-proteins, includ-ing those expressed on human pancreatic islets. Peptide-responsive CD8⁺ T cells that respond independently from CD8 binding (CD8-independent TCRs) will be identified and sequenced. TCRs from these cells will be expressed in an in vitro system and validated against the islet protein peptides prior to transfer and expression in Tregs for in vitro testing. These TCRs will be modified to increase or decrease predicted binding of the TCR to HLA-presented peptide and thereby relates Treg regulatory function to TCR binding strength.