Adoptive cell transfer of TCR-transduced T cells has shown strong tumor rejection capacity of the TCR-transduced T cells, but also an inherent risk of cross reactivity that can induce lethal side effects. Hence, it is crucial to understand the precise recognition element of the TCR prior to clinical investigation, to foresee any potential cross-recognition of endogenous epitopes. Here, we present a novel tool that allows description of an affinity-based hierarchy of pMHC interaction using large libraries of peptide variants of the originally identified recognition element. We make use of DNA-barcode labeled MHC multimers that allow simultaneous screening for T cell recognition of multiple (> 1000) different peptide specificities in a single sample. Importantly, the relative contribution of different pMHC molecules can be assessed using this technology - a feature not possible by conventional flow-based MHC multimer analyses.
We have previously characterized CD8+ T cell responses of Merkel cell Carcinoma patients (MCC), directed towards the Merkel Cell Polyomavirus-encoded Large T Antigen (LTA). From these MCC responsive T cells we identified and sequenced two TCRs recognizing LTA-derived epitopes in the context of HLA-B*0702 and HLA-A*2402, respectively.
To characterize the recognition profiles of these TCRs we generated libraries of 191 peptides including 19 different amino acid substitutions for each position of the original LTA-derived sequence, and additional N- and C-term frame shift and length variants based on the full TLA sequence. DNA-barcode labelled MHC multimers were generated, mixed and subjected to interaction with TCR-transduced T cells. Based on the interaction hierarchy of these pMHC multimers we build a recognition profile of each TCR, showing that position 3,5,6 and 7,8,10 were essential for TCR recognition of the HLA-B*0702 and HLA-A*2402 restricted TCR, respectively. The recognition profile was independent of peptide-HLA binding affinity.
In summary, this provides proof-of-concept for the use of a novel technology to identify TCR recognition profiles, and may prove highly valuable for the assessment of TCRs prior to clinical investigations.
Credits: None available.
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