Human TCR-MHC coevolution after divergence from mice includes increased nontemplate-encoded CDR3 diversity

For thymic selection and responses to pathogens, T cells interact through their {alpha}{beta} T cell receptor (TCR) with peptide-major histocompatibility complex (MHC) molecules on antigen-presenting cells. How the diverse TCRs interact with a multitude of MHC molecules is unresolved. It is also unclear how humans generate larger TCR repertoires than mice do. We compared the TCR repertoire of CD4 T cells selected from a single mouse or human MHC class II (MHC II) in mice containing the human TCR gene loci. Human MHC II yielded greater thymic output and a more diverse TCR repertoire. The complementarity determining region 3 (CDR3) length adjusted for different inherent V-segment affinities to MHC II. Humans evolved with greater nontemplate-encoded CDR3 diversity than did mice. Our data, which demonstrate human TCR-MHC coevolution after divergence from rodents, explain the greater T cell diversity in humans and suggest a mechanism for ensuring that any V-J gene combination can be selected by a single MHC II

The role of CD4 T cells in rejection of solid tumors

The focus in cancer immunotherapy has mainly been on CD8 T cells, as they can directly recognize cancer cells. CD4 T cells have largely been neglected, because most cancers lack MHC II expression and cannot directly be recognized by CD4 T cells. Yet, tumor antigens can be captured and cross-presented by MHC II-expressing tumor stromal cells. Recent data suggest that CD4 T cells act as a swiss army knife against tumors. They can kill cancer cells, if they express MHC II, induce tumoricidal macrophages, induces cellular senescence of cancer cells, destroy the tumor vasculature through cytokine release and help CD8 T cells in the effector phase. We foresee a great future for CD4 T cells in the clinic, grafted with tumor antigen specificity by T cell receptor gene transfer, either alone or in combination with engineered CD8 T cells

Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms' complexity is a reason for their increased performance, it also leads to the "black box" problem, consequently decreasing trust towards AI. In this regard, "Explainable Artificial Intelligence" (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability

Generation of TGFβR2(-1) neoantigen-specific HLA-DR4-restricted T cell receptors for cancer therapy

BACKGROUND: Adoptive transfer of patient’s T cells, engineered to express a T cell receptor (TCR) with defined novel antigen specificity, is a convenient form of cancer therapy. In most cases, major histocompatibility complex (MHC) I-restricted TCRs are expressed in CD8(+) T cells and the development of CD4(+) T cells engineered to express an MHC II-restricted TCR lacks behind. Critical is the choice of the target antigen, whether the epitope is efficiently processed and binds with high affinity to MHC molecules. A mutation in the transforming growth factor β receptor 2 (TGFβR2(-1)) gene creates a frameshift peptide caused by the deletion of one adenine (-1) within a microsatellite sequence. This somatic mutation is recurrent in microsatellite instable colorectal and gastric cancers and, therefore, is a truly tumor-specific antigen detected in many patients. METHODS: ABabDR4 mice, which express a diverse human TCR repertoire restricted to human MHC II molecule HLA-DRA/DRB1*0401 (HLA-DR4), were immunized with the TGFβR2(-1) peptide and TGFβR2(-1)-specific TCRs were isolated from responding CD4(+) T cells. The TGFβR2(-1)-specific TCRs were expressed in human CD4(+) T cells and their potency and safety profile were assessed by co-cultures and other functional assays. RESULTS: We demonstrated that TGFβR2(-1) neoantigen is immunogenic and elicited CD4(+) T cell responses in ABabDR4 mice. When expressed in human CD4(+) T cells, the HLA-DR4 restricted TGFβR2(-1)-specific TCRs induced IFNy expression at low TGFβR2(-1) peptide amounts. The TGFβR2(-1)-specific TCRs recognized HLA-DR4(+) lymphoblastoid cells, which endogenously processed and presented the neoantigen, and colorectal cancer cell lines SW48 and HCT116 naturally expressing the TGFβR2(-1) mutation. No MHC II alloreactivity or cross-reactivity to peptides with a similar TCR-recognition motif were observed, indicating the safety of the TCRs. CONCLUSIONS: The data suggest that HLA-DR4-restricted TCRs specific for the TGFβR2(-1) recurrent neoantigen can be valuable candidates for adoptive T cell therapy of a sizeable number of patients with cancer

Tc9 cells, a new subset of CD8(+) T cells, support Th2-mediated airway inflammation

Pathogenesis and treatment of chronic pulmonary disease