Problem/íssue being adressed:
Our current ability to map T-cell reactivity to certain molecular patterns poorly matches the huge diversity of T-cell recognition in humans. Our immune system holds approximately 107 different T-cell populations patrolling our body to fight intruding pathogens. Current state-of-the-art T-cell detection enables the detection of 45 different T-cell specificities in a given sample. Therefore comprehensive analysis of T-cell recognition against intruding pathogens, auto-immune attacked tissues or cancer is virtually impossible.
To gain insight into immune recognition and allow careful target selection for disease intervention, also on a personalized basis, we need technologies that allow detection of vast numbers of different T-cell specificities with high sensitivity in small biological samples.
I propose here a new technology based on multimerised peptide-major histocompatibility complex I (MHC I) reagents that allow detection of >1000 different T-cell specificities with high sensitivity in small biological samples. I will use this new technology to gain insight into the T-cell recognition of cancer cells and specifically assess the impact of mutation-derived neo-epitopes on T cell-mediated cancer cell recognition.
A major advantage of this new technology relates to the ability of coupling the antigen specificity to the T-cell receptor sequence. This will enable us to retrieve information about T-cell receptor sequences coupled with their molecular recognition pattern, and develop a predictor of binding between T-cell receptors and specific epitopes. It will ultimately enable us to predict immune recognition based on T-cell receptor sequences, and has the potential to truly transform our understanding of T cell immunology.
Advances in our understanding of T cell immunology are leading to massive advances in the treatment of cancer. The technologies I propose to develop and validate will greatly aid this process and have application for all immune related diseases.
Objectives
1) Develop a novel transformative technology for multiplex detection of antigen specific T cells using DNA barcode labelled MHC multimers
2) Apply the novel technology for decoding the T-cell mediated immune recognition in non-small-cell-lung cancer (NSCLC)
3) Establish a technical platform for collection of paired TCR and pMHC sequences, and use the knowledge to generate a predictor of molecular recognition characteristics based on the T-cell receptor sequence
Motivation and purpose
Immunotherapy has provided a major breakthrough in cancer treatment. Adoptive cell therapy and checkpoint blockade has proven successful for treatment of metastatic cancer – a clinical state where most other treatment strategies have failed to induce long-lasting clinical responses. Immunotherapy has a curative potential in many patient groups, which is rarely seen with other therapies for metastatic cancer. A critically important cellular component that mediates tumour cell killing are CD8 T cells; however our knowledge of the target sequences that these T cells recognize is currently very limited. Antigen targets of tumour-reactive T cells are currently discovered by screening predicted major histocompatibility complex (MHC) binding peptides in likely candidate proteins, but even with current state-of-the-art technology, the T cell target can be identified in only about 3% of tumour-infiltrating lymphocytes (TILs). This process is limited by the poor complexity of current state-of-the-art methods for identification of the targets recognized in bulk T cell populations. Although attempts have been made to develop unbiased experimental strategies, these currently work only for single T cell clones.
I present here a novel technology approach that will allow us to dissect the immune reactivity towards human tumours with a much greater complexity than currently possible with state of the art technologies. We will enable the detection of >1000 different T-cell specificities in a single sample. This will allow us to gain insights to immune recognition based on the gene-expression profile and mutational signature of a given tumour. I will apply this to understand the immune recognition of mutation-derived antigens in non-small-cell lung cancer (NSCLC), and the impact of mutation heterogeneity in detection of these responses. An addition asset of the proposed technology is the ability to identify antigen-specificity and T-cell receptor (TCR) sequence in a paired fashion following single-cell sorting. Through this mechanism will gain insight to the relationship between the TCR sequence and the recognition motif. I will develop a prediction algorithm to facilitate this translation. Because the specificity of a T cell is determined solely by its TCR sequence, it is theoretically possible to predict the peptide(s) targeted by each TCR, and thus characterize the target reactivity of a pool of T cells based solely on TCR sequencing. However, current knowledge of the relationship between TCR sequence and specificity is not sufficient to make such predictions, and there is currently very little data about which specific TCR sequences bind to which specific peptide sequences.
Impact: Advances in our understanding of T cell immunology are leading to massive advances in the treatment of cancer.
