Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - nextDART (Next-generation Detection of Antigen Responsive T-cells)

Teaser

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...

Summary

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 >10

Work performed

Within this project we have developed novel technologies that changes the paradigm for T cell detection by introducing the use molecular tagging systems (DNA barcoding) - this allows simultaneous detection of >1,000 different antigen-responding T cells (published in Nature Biotechnology). We have used this technology to unravel the immune reactivity towards cancer associate neo- and shared epitopes (published in Cancer Research, Science and Nature), as well as understanding the influence of cancer therapy on the immune recognition fingerprint in individual patients. Herein, we are determined to understand the relationship between tumor genetic heterogeneity and immune recognition (published in Science), and characterized clonal neoantigens as an important parameter for clinical outcome following immune therapies. We are building novel technology platforms that allow in-depth understanding of T cell recognition from a structural perspective, and can be used to evaluate clinical efficacy and safety profiles of T cell receptors for clinical use (Nature Biotechnology, 2018).

Final results

Work perform in the project has led to the development of novel technologies to interrogate T cells, to identify T cell targets and T cell recognition motifs
These technologies has been used to gain insight to neoepitope in cancer and the relationship between T cell recognition and tumor genetic heterogeneity

Next phase, which we are currently addressing is to developed a platform for multi-parametric single-cell T cell analyses providing information related to, T cell specificity, surface expression, transcriptomics, and T cell receptor usage. When such analyses are running at large-scale it will generate unique information to initiate the development of prediction algorithms for the interaction between T cells and their pMHC target.