T Cell Receptor identification and provision

Abstract

A method of selecting candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics, comprising: a) generating single cell data from a plurality of T cells from a

Description

Field of the invention The present invention relates to methods of selecting candidate T cell receptors (TCRs) or T cells expressing candidate TCRs. The methods rely on single cell data from T cells taken from experimental animals immunized with an antigen. Paired TCR sequences can be clustered according to sequence similarity and, within those clusters, various useful selection parameters can be analysed to facilitate TCR or T cell selection. The selected candidate TCRs or T cells expressing candidate TCRs have defined cell type, cell status and TCR binding characteristics to an antigen of interest. These defined characteristics may identify the TCR or T cells as candidates for therapy, for example TCRT cell therapy, particularly in the treatment of cancer. The present invention further relates to methods of producing a TCR, TCRs and T cells expressing the TCRs, as well as further products relevant to the therapeutic application of selected candidate TCRs or T cells expressing candidate TCRs. Background of the invention T cells and B cells are well studied components of the adaptive immune system. They function to recognise antigens that are “foreign” to the body through T cell receptors (TCRs) and B Cell Receptors (BCRs) with an array of sequences that together form the functional adaptive immune repertoire. In humans, TCRs are expressed on the surface of T cells. T cell recognition and targeting of antigen presenting cells is mediated by TCRs. Target cells display fragments of foreign or self-proteins (antigens) complexed with the major histocompatibility complex (MHC; also referred to herein as complexed with an HLA molecule, e.g., an HLA class I molecule). The term "HU\," as used herein, refers to the human leukocyte antigen. HU\ genes encode the major histocompatibility complex (MHC) proteins in humans. MHC proteins are expressed on the surface of cells and are involved in activation of the immune response. HLA class I genes encode MHC class I molecules, which are expressed on the surface of cells in complex with peptide fragments (antigens) of self or non-self proteins. T cells expressing TCR and CD3 recognize the antigen:MHC class I complex and initiate an immune response to target and destroy antigen presenting cells displaying non-self proteins. A TCR recognizes and binds to the antigen:HLA complex and recruits CD3 (expressed by T cells), activating the TCR. The activated TCR initiates downstream signalling and an immune response, including the destruction of the target cell. Therefore, due to the nature by which TCRs bind presented fragments of antigens, they can advantageously bind to cell surface and intracellular antigenic epitopes (compared to antibodies for example). For example, in the context of disease TCRs can be used in therapies against cancer-specific and cancer-associated peptides that have undergone proteasomal, endosomal or lysosomal protein degradation which are then presented extracellularly by MHC proteins, including cancer specific neoantigens, cancer germline antigens and viral oncoproteins. TCRs structurally comprise two chains (also referred to as hemi chains) interconnected by disulphide bonds. Each of the two chains has a variable domain and a constant region. The variable domain is located distal to the cell membrane, and the variable domain interacts with an antigen. The constant region is located proximal to the cell membrane. The two chains are usually a and p chains. A TCR can further comprise a transmembrane region and a short cytoplasmic tail. TCRs have antigen binding regions encoded by Variable-Diversity-Junctional, V(D)J, and constant (C) gene sequences of the T cell receptor a, TCRa and TCRp hemi chains, encoding a heterodimer (a minority of T cells express TCRyO instead of TCRap). The V, D, J and C genes do not encode functional proteins in their germline configuration with each segment undergoing site specific recombination to assemble into a functional frame. TCR heterodimers have six complementary determining regions (CDRs) whose diversity is determined by the juxtaposition of different V(D)J gene segments as well as deletion and addition of non-template encoded nucleotides. The T cell receptor is a complex of proteins formed from clonotypic TCRap hemi chains and invariant CD3y, CD36, CD3e and CD3£. TCR based therapies can be widely exploited for the treatment of a variety of diseases, such as cancer, due to their ability to detect extracellular and intracellular antigens. Traditional techniques for matching antigens to corresponding TCRs (and BCRs) include hybridoma and phage display methods which are time consuming and technically challenging. The biology of how TCRs bind to antigens on major histocompatibility complex (MHC) proteins provides additional challenges relating to developing stable peptide-MHC complexes to isolate antigen-specific T cells. Various single cell-based methods for linking T cell phenotypes to T cell receptor sequences and their binding specificity are known. Chen et al., 2023 report a relationship between TCR sequence and T cell phenotype and persistence based on a longitudinal study of COVID-19 human patients and observe that this helps to explain why T cell phenotype often appears to be determined early in an infection. Techniques have been developed to capture cellular profiles of antigen-specific T and B cells, such as Chromium Single Cell 5’ Barcode Enabled Antigen Mapping (BEAM), a droplet-based method that is commercially available as a 10x Genomics platform. BEAM allows single cell antigen-specific cell profiling including full-length paired V(D)J sequencing, gene expression sequencing and cell surface protein sequencing to match antigens with corresponding B Cell Receptors or T Cell Receptors. A similar technique is cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq). CITE-seq is a sequencing-based method that enables the simultaneous detection and quantification of surface proteins, immune receptor binding specificity and transcriptomic data at single cell resolution. Alternative methods are also known that provide single cell separate DNA products that can be used for TCR cloning, allowing amplification of TCRs and subsequent cloning in individual wells (Wahl et al 2022). Methods are known which retain information about individual TCRap pairs where TCRs of interest can be expressed and used in functional studies based on nested PCR, barcoding and sequencing analysis for phenotypic parameters such as cytokines and transcription factors (Han et a / 2015). Methods are known relating to single cell sequencing assays enabling joint profiling of the TCR and transcriptome (Lagattuta et al 2023). These methods have been applied to human derived samples. Various sources have been used to identify cancer specific TCRs including tumourinfiltrating lymphocytes (TILs), circulating T cells in patients, healthy donors, HLA transgenic mice and humanized mice. TILs from human cancers have been used as a source for TCR gene sequences that confer anti-tumor immunity. However, the isolation of TILs alone is insufficient as most TILs are not cancer specific. TILs from human patients may be further characterised by phenotypic, functional and transcriptomic means (Klebanoff et a / 2023). Healthy human patients have a broad circulating TCR repertoire, however the frequency of tumour-reactive TCR clonotypes is very rare in the naive repertoire. Model organisms have also been developed that allow the identification of TCRs. Tumor reactive T cells can be generated through vaccination of human leukocyte antigen (HLA) transgenic (Tg) mice (Li et al 2010). TCRs retrieved from HLA Tg mice have immunogenic mouse variable sequences capable of triggering host-versus-graft rejection when infused into humans (Klebanoff et al 2023). Humanised mice have replaced murine TCRap variable regions, regions encoding the extracellular domains of co-receptors CD4 and CD8, and MHC class I and II with corresponding human sequences (Moore et a / 2021). These mice have been immunised with cancer testis antigens NY-ESO-1 and MAGE-A3 to validate the induction of TCRs specific to human tumor antigens. Additional relevant models have been produced, see for example WO2013063361A1. There is a need for methods that further allow and improve the identification of antigen specific T cell clonotypes with desirable phenotypic properties for specific types of TCR therapy. Description of the invention As described herein, the inventors have utilised experimental animals and single-cell technologies to select candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics. Following clustering of TCR sequences on the basis of sequence similarity, the inventors observed a striking correlation between sequence relatedness and defined characteristics (phenotypes), with the sequences within each cluster demonstrating similar characteristics, including cell type, cell status and TCR binding characteristics. Moreover, this correlation applied across a plurality of experimental animals. Thus, TCR sequences from different animals immunised with the same antigen could be clustered based on sequence similarity and moreover demonstrated similar characteristics, including cell type, cell status and TCR binding characteristics. Sequence clusters can thus be selected containing multiple similar TCR sequences that display similar characteristics, including at the level of the T cell. This provides a pool of candidate cells and sequences that are predicted to be useful, for example in TCR-based cell therapies (e.g. TCR-T cell therapies), based on the combination of demonstrated characteristics. It is therefore an object of the present invention to provide methods that identify and enable the selection of TCRs displaying favourable characteristics for defined TCR therapeutics. Reference to TCRs encompasses the sequences that encode the TCR and may include the terms “paired TCR sequences” or “clonotype”. As used herein, “clonotype” means a recombined nucleotide sequence of a T cell encoding a T cell receptor (TCR), or a portion thereof. The term clonotype when used refers to the relevant sequence or a portion thereof (e.g. the CDRs) that identifies the TCR in question. The relevant sequence or a portion thereof may comprise the hemi chain paired TCR sequences, for example the TCR a and p hemi chains. As used herein, the term “characteristics” may refer to any relevant phenotypic property of a TCR or cell expressing a TCR and in particular one or more of cell type, cell status and / or TCR binding characteristics. Thus, the term may be used interchangeably with “phenotypes”. These characteristics have particular relevance for selecting different TCR therapeutics. For example, for TCR cell therapies, TCR clonotypes for T cells that have intermediate / high binding affinity to the target antigen, good cell persistence and high cell expanding potential may be desirable. Alternatively, for soluble TCR biologic therapeutics, a TCR with high target antigen binding affinity and high target antigen binding specificity may be desirable. “High” in this context is a term used relative to the other TCR clonotypes within the tested population of TCR clonotypes. Selected TCRs may then be further improved in terms of desirable characteristics, for example through affinity maturation. TCRs are established antigen sensors that detect, amplify and coordinate cellular immune response from epitopes derived from cell surface and intracellular proteins. Once a target antigen (e.g. a protein or polypeptide) is known, for example a protein selectively expressed by cancer cells such as a neoantigen, cancer germline antigen or viral oncoprotein, TCRs can be identified that detect MHC presented portions of that antigen, such that said TCR or relevant sequences from that TCR (for example the complementary determining regions (CDRs) 1-3 of the TCR a and p variable hemi chains) can be used in TCR therapeutics to target the antigen in a subject. However, the characteristics of each TCR clonotype and T cell expressing that TCR clonotype (the phenotypes) will vary depending on the TCR which consequently impacts on the utility of the TCR clonotype for potential application as a TCR therapeutic. The methods of the invention provide and cluster TCR clonotypes by sequence similarity and, consequentially and advantageously, also by their cellular phenotype as well as TCR binding characteristics. Without wishing to be bound by theory a number of advantages may arise, at least in part, as a result of combining single cell technology with a transgenic experimental animal platform. The methods of the invention enable the selection of candidate T cell receptors (TCRs) orT cells expressing candidate TCRs with related TCR sequences and highly similar cell type / state composition, even when originating from different donors, based on those similar properties. The candidates may be used to develop a TCR therapeutic of interest to be used in the treatment of a disease of interest. For example, TCRs or T cells expressing TCRs can be specifically selected for their suitability as TCR-T cellular therapeutics, or as a soluble TCR therapeutic, as described in further detail herein. The present invention therefore provides a method of selecting candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics, comprising a) generating single cell data from a plurality of T cells from at least one experimental animal, preferably a transgenic experimental animal, that expresses TCRs and that has been immunized with an antigen, wherein the single cell data comprises: i) paired TCR sequences; ii) gene expression quantification; iii) antigen binding quantification; and optionally iv) cell surface protein (e.g., TCR, PD-1 and / or CD45RA / RO) quantification; b) clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters and determining cell type, cell status and TCR binding characteristics from the single cell data; and c) selecting candidate TCRs or cells expressing candidate TCRs with defined characteristics by analysing determined cell type, cell status, TCR binding characteristics and optionally cell surface protein quantification, within each of the plurality of clusters. Also provided are related methods of producing a TCR that binds an antigen of interest and expressing that TCR in a cell as well as producing a T cell receptor (TCR) compound wherein the TCR compound binds to an antigen of interest. The invention further provides TCRs, nucleic acids, recombinant vectors, recombinant host cells (especially T cells), pharmaceutical compositions and therapeutic applications of selected TCRs that bind an antigen of interest. The invention provides a method of selecting candidate T cell receptors (TCRs) orT cells expressing candidate TCRs with defined characteristics. The method comprises a step of generating single cell data from a plurality of T cells from at least one, and preferably a plurality of transgenic experimental animals that express TCRs, preferably humanised TCRs, and that have been immunized with an antigen. The single cell data comprises paired TCR sequences, gene expression quantification, antigen binding quantification, and optionally cell surface protein quantification. This is followed by a step of clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters and determining cell type, cell status and TCR binding characteristics from the single cell data. This is followed by a step of selecting candidate TCRs or cells expressing candidate TCRs with defined characteristics by analysing determined cell type, cell status, TCR binding characteristics, and optionally cell surface protein quantification (which may also be referred to as cell surface marker abundance), within each of the plurality of clusters. The methods of the invention advantageously enable the observation of a diverse population of expanded clonotypes from immunized, preferably transgenic, experimental animals and a striking correlation between TCR sequence similarities and expression phenotype similarities. This provides a diverse pool of clonotype clusters from which TCR clonotypes can be selected for TCR therapy, particularly TCR cell therapies involving a heterologously expressed candidate TCR. Applying the methods of the invention it has been shown that TCR clonotypes that are clustered together based on TCR sequence similarity, in particular TCR complementary determining region (CDR) sequence similarity, share similar cell type / state and binding characteristics. This is advantageous for selecting candidate T cells expressing TCRs and TCRs with desired characteristics. Observing shared / convergent clonotypes across donors is difficult / rare in human studies. In one embodiment of the methods of the invention, the selecting comprises selecting one or more clusters based on the combined cell type, cell status and TCR binding characteristics of the one or more clusters. Thus, defined characteristics may be displayed by one or more clusters. Those one or more clusters may be selected providing a set of TCR sequences and T cells expressing those sequences. The set of TCR sequences and T cells expressing those sequences can then be further investigated, for example using in silico characterisation and in vitro / in vivo testing. This may result in selection of a limited number of TCR sequences and T cells expressing those sequences for further development. Applying the methods of the invention has enabled the observation of a large number of shared / convergent TCR sequences across animals immunized with the same antigen. This advantageously provides the opportunity to assess phenotypic similarity (the characteristics of the TCRs) across donors. Applying the method of the invention, striking cross-donor animal similarity in expression phenotype of T cells sharing the same or similar TCR sequences has been shown. Surprisingly, clonotypes sharing the same sequence show highly similar cell type / state composition across donors. Therefore, the methods of the invention allow clustering of clonotypes by their cellular phenotype as well as TCR binding characteristics to produce a diverse set of clusters from which to select TCRs or T cells expressing candidate TCRs for TCR therapeutics. Methods utilising samples derived from humans can provide very different results. Without wishing to be bound by theory, each human from a plurality of humans has been exposed to different environmental factors for many years and the TCR selection process in humans provides a less diverse pool of TCRs with limitations on TCR binding affinity compared to experimental animals. As noted above, the method of the invention comprises a step of generating single cell data from a plurality of T cells from at least one, preferably transgenic, experimental animal that expresses TCRs and that has been immunized with an antigen. The single cell data comprises paired TCR sequences, gene expression quantification, antigen binding quantification and optionally cell surface protein quantification. Preferably, in this step the plurality of T cells is taken from a plurality of, preferably transgenic, experimental animals which express humanised TCRs. The single cell data characterises each of a plurality of T cells for cell type, cell status and TCR binding characteristics. This step is conducted using at least one, preferably a plurality of, experimental animals that have been immunized with an antigen. The step of immunisation may form part of the methods in some embodiments. An antigen of interest is used to obtain antigen responsive TCRs IT cells. The experimental animals are non-human animals. Utilisation of non-human experimental animals has many benefits. The methods of the invention exploit the antigen specific T cell response that results from immunization of the animal with the antigen. The experimental animals are able to generate antigen specific TCRs that can detect proteolytically degraded polypeptides (typically 9-12 amino acids in length) of the antigen that have been trafficked to the surface of cells for extracellular presentation on appropriate presentation molecules, such as MHC I or II proteins. Any suitable experimental animal may be used, with preferred examples being rodents, specifically mice. It is preferred that the experimental animals are transgenic, specifically that the animals express TCRs of another animal species. This may result in an enhanced / more diverse immunological response to the antigen. In such embodiments, typically the TCR constant regions will be those of / native to the experimental animal although this is not essential in all models (e.g. in animals expressing fully human TCRs). In a particularly preferred embodiment, the transgenic experimental animal expresses humanised TCRs. This provides a way to overcome the negative influence of thymic selection on the pool of TCRs targeting self-antigens in humans. However, alternative embodiments are envisaged, for example where non-human TCRs are desired. In one embodiment, the plurality of transgenic experimental animals express TCRs of another animal species, preferably a companion animal. For example, the plurality of transgenic experimental animals may express canine / caninised or feline / felinised TCRs (as an alternative to humanised TCRs). In one embodiment, the plurality of transgenic experimental animals are rodents. In a further preferred embodiment, the plurality of transgenic experimental animals are mice. The plurality of transgenic experimental animals may have TCRa and TCRp loci with human variable region gene segments, optionally fully human TCRa and TCRp loci. In one embodiment, the plurality of transgenic experimental animals do not express native TCRa and TCRp. In one embodiment, the plurality of transgenic experimental animals may be humanised at one or more, preferably all of the: a) TCRa variable region; b) TCRp variable region; c) region encoding the extracellular domain of co-receptor CD4; d) region encoding the extracellular domain of co-receptor CD8 e) region encoding major histocompatibility complex (MHC) class I; and f) region encoding major histocompatibility complex (MHC) class II. In a one embodiment, the plurality of transgenic experimental animals are mice with targeted genetic modification to replace endogenous TCR variable region gene segments of TCR alpha and beta loci with human TCR alpha and beta variable region gene segments. In one embodiment, the plurality of transgenic experimental animals are mice comprising an unrearranged T cell receptor (TCR) a variable gene locus comprising at least one human Va segment and at least one human Ja segment, wherein the TCRa variable gene is operably linked to a rodent TCRa constant gene sequence, and / or an unrearranged TCRp variable gene locus comprising at least one human vp segment, at least one human Dp segment, and at least one human Jp segment, wherein the unrearranged TCRp variable gene is operably linked to a rodent TCRp constant gene sequence, and wherein the unrearranged human T cell variable region gene segments are capable of rearranging to form genes that encode human T cell receptor variable domains, including domains that specifically bind an antigen of interest. In one embodiment the plurality of transgenic experimental animals have a genome of a laboratory animal and encode a human MHC class I gene encoding a human HLA. In one embodiment the plurality of transgenic experimental animals have a knockout of the host MHC class I gene, such that the plurality of transgenic experimental animals express only a human HLA class I gene product from a human MHC class I gene. In one embodiment the genome of a plurality of transgenic experimental animals encodes a human MHC class II gene encoding a human HLA class II. In embodiment the plurality of transgenic experimental animals has a knockout of the host MHC class II gene, such that the plurality of transgenic experimental animals express only a human HLA class II gene product from a human MHC class II gene. Mice expressing human HU\s are known in the art, for example as described in Obenaus et al 2015 and references therein, such as Li et al (2010). Further examples of class I HLA transgenes in mice are described in Pascolo et al., 1997, WO2014 / 130671, Wang et al., 2016, Moore etal., 2021 and WO2021 / 139799. Examples of class II transgenes in mice are described in WO2014 / 130671, and Ito et al., 1996, Yatsuda et al., 2013, Chen etal, 2017, Poncetteetal., 2019 and Mooreetal., 2021. In one embodiment the genome of a plurality of transgenic experimental animals encodes a fully human TCR. Mice containing human TCRs are known in the art, for example as described in Obenaus etal2015. In one embodiment the genome of a plurality of transgenic experimental animals encodes a chimeric TCR, a part of which is a human sequence and a part of which is a sequence of the laboratory animal TCR. The TCR in one embodiment comprises at least a human variable region. The laboratory animal genome may comprise, for example a replacement of an endogenous T cell receptor (TCR) variable a (Va) gene with unarranged human TCR Va segments and unrearranged human TCR Ja gene segments, wherein the unrearranged human TCR Va and Ja gene segments are operably linked to a TCR constant a (Ca) gene of the laboratory animal, and / or a replacement of an endogenous T-cell receptor (TCR) variable p (VP) gene with unrearranged human TCR vp gene segments, unrearranged human TCR Dp gene segments, and unrearranged human TCR jp gene segments, wherein the unrearranged human TCR Vp, Dp, and jp gene segments are operably linked to a TCR constant p(CP) gene of the laboratory animal. Mice encoding such chimeric TCR are described, inter alia, US9113616B2 and Moore et al., 2021. These mice are all suitable for use in the present invention as well as mice disclosed in WO2013063361A1 and the teachings are incorporated by reference. Another suitable mouse is disclosed in co-pending UK patent application GB2312191.6 and GB2405373.8. In one embodiment the genome of a plurality of transgenic experimental animals encodes a human TCR or variable region of a human TCR, and encodes a human MHC, as described herein. Where a plurality of experimental animals is used in the methods of the invention each animal is from the same species, for example all mice. Further, the plurality of experimental animals may all be of a similar or same defined genetic background, for example all congenic C57B6 / J mice or they may be of a hybrid genetic background. As used herein, an "antigen" refers to a molecule that elicits an immune response and / or is the immunogenic agent processed to produce a T cell epitope, e.g. a polypeptide. An "epitope", or “T cell epitope” as used herein, refers to a portion of an antigen that, in the case of a peptide derived from a protein / polypeptide antigen, is bound to MHC molecules and recognized by a TCR. Thus, where the antigen is a polypeptide, the T cell epitope is a peptide derived from the polypeptide. The antigen is typically a characterised antigen, e.g. a known polypeptide or a combination of characterised antigens. The antigen may be administered to the experimental animal in substantially purified form (which includes formulating the antigen appropriately for administration). An antigen can be endogenously expressed, i.e. expressed by genomic DNA, or can be recombinantly expressed. An antigen can be of exogenous origin. An antigen can possess modifications to the component amino acids if of polypeptide origin (e.g. phosphorylation, glycosylation, cysteinylation, deamidation, and / or other post-translational modifications to the amino acids within the antigen). An antigen can be specific to a certain tissue, such as a cancer cell, or it can be broadly expressed. In addition, fragments of larger molecules can act as antigens. In one embodiment, antigens used according to the invention are tumor antigens. The antigen may be an intracellular, cell surface or extracellular antigen. In some embodiments, an epitope of the antigen is complexed with a major histocompatibility complex (MHC; also referred to herein as an HLA molecule, e.g., an HLA class I molecule) and will typically be 9-12 amino acids in length. In principle, any antigen(s) may be used to immunise the plurality of transgenic experimental animals so long as the antigen can be presented by the appropriate MHC or HLA molecule. In one embodiment, the antigen is an intracellular antigen. In another embodiment the antigen is foreign to the transgenic experimental animals. In another embodiment, the antigen is a self-antigen, preferably a human self-antigen. A “self-antigen” means self with respect to the TCRs expressed by the experimental animal. Thus, in an experimental animal expressing humanised TCRs, the self-antigen would be a human antigen. The plurality of transgenic experimental animals may be immunized with more than one antigen in combination. In one embodiment, the plurality of experimental animals have been immunized with two, three, four or more antigens. The one or more antigens are administered in an appropriate regime to enable the isolation of responsive T cells. The antigen may be associated with any disease, such as cancer or autoimmune disorders. In one embodiment, the selection of candidate TCRs or T cells expressing candidate TCRs according to the present invention is directed towards selecting TCRs or T cells expressing candidate TCRs with cell type, cell status and TCR binding characteristics for TCR therapeutics that treat cancer, preferably TCR-T cell therapies. The cancer may be a solid cancer in some embodiments. The cancer may be a liquid or blood cancer, such as a leukaemia or lymphoma in some embodiments. Accordingly, in one embodiment, the selected candidate TCRs or T cells expressing candidate TCRs bind a cancer antigen. In these embodiments, a cancer antigen has been used to immunize the plurality of transgenic experimental animals. The antigen may be a tumor associated antigen, a cancer-germline antigen or a tumor specific antigen. Cancer-germline antigens derive from proteins that are normally only expressed in germ cells such as testis which lack HLA class I expression. The tumor associated antigen may be an overexpressed antigen (which arise from proteins highly overexpressed in cancer tissue as compared to normal tissue) or a cancer differentiation antigen (which are expressed by cancer cells and their expression is otherwise limited to only the normal cells of the same tissue origin as the cancer). The tumor-specific antigen may be a neoantigen derived from a tumor-specific mutation. The tumor-specific antigen may be a viral antigen from a viral oncogene. By way of example, the antigen may be selected from one or more of Melan-A melanoma antigen, MART-1, tyrosinase 369-378 peptide (YMD), NY-ESO-1, WT1, MAGE-A4, HPV viral oncogenes E6 and E7, EBV viral oncogenes LMP1 and LMP2, KRAS G12D / G12V, PIK3CA H1047L. A plurality of T cells is sampled from the experimental animals. The T cells may all be from a single tissue type. In one embodiment, the T cells are obtained from peripheral blood, peripheral blood mononuclear cells, bone marrow, lymph node tissue, cord blood, thymus tissue, tissue from a site of infection, ascites, pleural effusion, spleen tissue and / or tumor. In one embodiment, the T cells are from spleen and / or lymph node. In certain embodiments, T cells can be obtained from a unit of blood using any number of known techniques such as sedimentation, e.g., FICOLL™ separation. A single experimental animal may be sufficient to derive enough T cells having the desired properties, however preferably a plurality of experimental animals is used from which single cell data are obtained and processed according to the methods of the invention in order to select candidate T cell receptors (TCRs) orT cells expressing candidate TCRs. In one embodiment, the plurality of experimental animals consists of two or more, three or more, four or more, five or more or six or more animals. In one embodiment, the plurality of experimental animals consists of six or more animals. Convergence from multiple donor animals, may be advantageously relied upon to select clones. Preferably, the number of animals used generates a large number of clonotypes with diverse phenotypes that can be clustered according to the methods of the invention and used to select candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics. The plurality of T cells may be obtained with a pre-enrichment step. This may be based on negative selection, preferably using a negative selection kit. This may be completed if the percentage of T cells derived from the plurality of animals is low. In one embodiment the plurality of T cells are enriched for CD8+ T cells. In another embodiment the plurality of T cells are enriched for antigen-specific T cells. Alternatively, the expansion of clonotypes is relied upon in response to the antigen. In a further embodiment, cytotoxic T lymphocytes (TCTL) and T helper (TH) lymphocytes may be purified, CTL and TH lymphocytes may be sorted into naive (TN), memory (TMEM), and effector (TEFF) T cell subpopulations. Cell sorting methods are well known in the art and may, for example, rely on flow cytometry. Cell enrichment may be by cell sorting using fluorescence, optionally by using a fluorescently labelled barcoded antigen probe and optionally a negative control antigen probe. The negative control probe can have the same or different fluorescence from the target probe. The negative control probe can compete for cell surface TCR, so in the latter case, less specific TCR / cell will yield weaker target colour fluorescence and stronger negative colour fluorescence. In the former case, the fluorescence will not change if total binding remains the same. In both cases, the binding to target and negative control probes can be distinguished / quantified using sequenced barcodes at the stage of data analysis. In one example, antigens may be coupled to a uniquely barcoded conjugate complex that contains a fluorescence marker. This is used to stain T cells prior to flow sorting for enrichment, followed by isolation of stained antigen-specific clonotypes by checking for fluorescence. In this example, a negative control peptide assembly is included comprising a conjugate, MHC monomer and negative control peptide. The negative control peptide can be used to calculate an antigen binding specificity score. Such systems are available commercially, e.g. the Barcode Enabled Antigen Mapping (BEAM) system from 10x Genomics. Once the cells of interest have been obtained and following any optional enrichment steps, the plurality of T cells are separated into single cells to enable single cell data to be generated. In one embodiment the antigen-specific plurality of T cells undergo single cell partition, optionally wherein partition comprises attaching barcodes to molecules of single T cells. In a further embodiment, the single cell partition comprises partitioning the plurality of T cells into gel beads-in-emulsion (GEM) or by their own membrane using a fixation method. The workflow may be according to the BEAM workflow (10x Genomics). Single cell data is obtained from the plurality of T cells from the plurality of transgenic experimental animals that express humanised TCRs and that have been immunized with an antigen. As stated above, the single cell data comprises paired TCR sequences, gene expression quantification, antigen binding quantification and optionally cell surface protein quantification. The single cell data is derived by any appropriate means. Single cell data may be generated using sequencing, preferably next generation sequencing. In one embodiment, the paired TCR sequences are determined from a single cell V(D)J library. In one embodiment, gene expression quantification is determined by transcriptome sequencing. In one embodiment, obtaining cell surface protein quantification comprises labelling the plurality of T cells with specific binding reagents targeting cell surface proteins, wherein the specific binding reagents targeting cell surface proteins are barcoded, preferably wherein the specific binding reagents are antibodies. In one embodiment, cell surface protein quantification is determined by quantifying sequence reads from the barcodes. In one embodiment, obtaining antigen binding quantification comprises labelling the plurality of T cells with specific binding reagents comprising a barcoded MHC molecule loaded with the antigen. In a further embodiment, the antigen binding quantification comprises counting sequence reads from the barcodes conjugated to the MHC molecule. The barcoded MHC molecule may be loaded with the antigen further comprising a fluorescent marker. In one embodiment, single cell data is generated from a 5’ gene expression (GEX) library, V(D)J library, and / or barcode enabled antigen mapping (BEAM), preferably 5’ gene expression (GEX) library, V(D)J library and barcode enabled antigen mapping (BEAM).Therefore, the single cell data generated for paired TCR sequences, gene expression quantification, antigen binding quantification and optionally cell surface protein quantification according to the invention can be used to determine cell phenotype information for paired TCR sequences that can be clustered and assessed according to the next step of the methods of the invention. The workflow may be according to the BEAM workflow (10x Genomics). Relevant protocols that may be used in steps of the methods of the invention are provided in WO 2023 / 086824 A1, WO 2023 / 250422 A1, WO 2024 / 044703 A1 and WO 2024 / 050299 A1 which are incorporated by reference. As noted above, the methods of the invention comprise clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters. The term “clustering” refers to grouping or associating paired TCR sequences, in this invention on the basis of sequence similarity. Each cluster typically comprises highly similar sequences. Suitable sequence clustering algorithms have been described for clustering on the basis of sequence similarity, such as TCRdist (Dash et al 2017). The methods also comprise determining cell type, cell status and TCR binding characteristics from the single cell data. This can be done in parallel with clustering of paired TCR sequences, but this is not essential. It may be done before or after clustering. In one embodiment, the determined TCR binding characteristics comprise binding affinity and / or binding specificity. The binding affinity may be determined by any suitable means. For example, binding affinity may be determined by normalising abundance of barcodes against TCR copy number. The binding specificity may be determined by any suitable means. For example, binding specificity may be determined by comparing abundance of barcodes to abundance of a negative control, wherein the negative control comprises a barcoded MHC molecule loaded with a control peptide different from the antigen. The term “barcode” is used herein to refer to a label, or identifier, that conveys or is capable of conveying information (e.g. information about an analyte in a sample, a bead, and / or a nucleic acid barcode molecule). With reference to determining TCR binding affinity and / or specificity, unique barcodes may be used in BEAM conjugate complexes that are coupled to antigens which are bound by TCRs. The binding affinity and / or binding specificity of TCR to a target antigen can be determined based on the counts and / or amounts of target antigens and / or non-target antigens associated with the TCR. In some embodiments, the binding affinity and / or binding specificity of an antigen-binding molecule to a target antigen can be determined based on the proportion of target antigens and optionally non-target antigens that are associated with the antigen-binding molecule. Exemplary methods for determining binding affinity and binding specificity are disclosed in WO2022150662A1 which is incorporated by reference herein. The binding affinity and / or specificity may be determined according to the BEAM workflow (10x Genomics). Again, reference is made to WO 2023 / 086824 A1, WO 2023 / 250422 A1, WO 2024 / 044703 A1 and WO 2024 / 050299 A1 which are incorporated by reference. In one embodiment, the determined cell type comprises naive and / or memory types. The cell type may be determined by any suitable means. Cell type may be determined, at least in part, from transcriptome quantification and / or cell surface protein quantification. In one embodiment, the determined cell status comprises one or more of exhaustion status, cytotoxicity status and proliferation status. The cell status may be determined by any suitable means. Cell status may be determined, at least in part, from whole transcriptome quantification and / or cell surface protein quantification. TCR copy number / cell surface expression status is also determined. The cell types and cell states referred to herein are well defined in the art of T cells. For example, in mice naive type cells express Tcf7 and Igfbp4 marker genes. Memory type cells express Cd44 and Itm2a marker genes. Cytotoxic status (also referred to as effectorlike I cytotoxic) express Gzma, Gzmb, Prf 1 and Ifng marker genes. Exhaustion status (exhausted-like) cells express Pdcdl, Ctla4, Tigit, Lag3 and Tox marker genes. Proliferation status cells express Mki67, Cdk1, Top2a and Mcm2 marker genes. Based on paired TCR sequences, TCR clonotypes are defined and metrics including degree of expansion, sequence convergence, and CDR sequence similarity may be calculated. Based on whole transcriptome quantification, the type and state of the sequenced cells may be defined. Based on quantification antigen binding and optionally of cell surface proteins, metrics including antigen binding avidity I affinity and specificity may be calculated. By combining the different metrics, TCR clonotypes and their multiple phenotypes (cell type, cell status and TCR binding characteristics) are provided according to the methods of the invention. As the method produces a plurality of TCR clonotypes and single cell data that can be used to determine cell type, cell status and TCR binding characteristics, the methods of the invention provide a means for clustering and assessing candidate T cell receptors (TCRs) orT cells expressing candidate TCRs. Advantageously, a diverse population of expanded clonotypes from the immunized plurality of transgenic experimental animals has been demonstrated according to methods of the invention. The skilled person understands that various approaches to clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters may be employed. For example, clustering the paired TCR sequences on the basis of sequence similarity may comprise applying a TCRdist algorithm (Dash ef a / 2017). Alternatively, clustering the paired TCR sequences on the basis of sequence similarity may comprise applying a diversity metric TCRdiv that generalizes Simpson’s diversity index (Dash et al 2017). The method comprises clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters comprising use of any relevant TCR clonotype sequence. In one embodiment, clustering the paired TCR sequences on the basis of sequence similarity is based on TCR CDR sequence similarity. Here, the skilled person can employ any suitable method for assessing TCR CDR sequence similarity, such as using the combined TCRa and TCRp variable CDR1-3 sequences. Though all three CDRs on each hemi chain are involved in antigen binding, CDR3 is believed to be the primary antigen binding region. CDR1 and CDR2 are believed to primarily recognize the HLA complex. Therefore, sequence similarity may be based on CDR3 sequences alone. The skilled person may rely on the TCRa variable CDR1-3 sequences, TCR0 variable CDR1-3 sequences, TCRa variable domain sequence, TCRp variable domain sequence and / or additionally rely on TCRa and / or TCRp constant domain sequences. In one embodiment clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters comprises calculation of the degree of expansion, sequence convergence and / or CDR sequence similarity. In one embodiment, clustering the paired TCR sequences on the basis of sequence similarity comprises comparing distances among TCR CDR sequences and gene expression. Dimensionality reduction methods such as uniform manifold approximation and projection (UMAP), Stochastic Neighbour Embedding (SNE), multi-SNE, Locally Linear Embedding (LLE), or Isometric Feature Mapping (ISOMAP) may also be applied. According to methods of the invention, the clustering may be based on applying different hard distance thresholds on a nearest neighbour graph. The distance metric for clustering may be defined by Hamming distance, Levenschtein distance, Damerau-Levenschtein distance, or amino acid sequence alignment. Each cluster may comprise data for each cell concerning one or more of cell type, cell status and TCR binding characteristics. Examples of data that may be included in each cluster include naive cell type, memory cell type, exhaustion status, proliferation status, TCR binding affinity, TCR binding specificity, cytotoxicity status and TCR copy number / cell surface expression status. In one embodiment, each cluster comprises all of naive cell type, memory cell type, exhaustion status, proliferation status, TCR binding affinity, TCR binding specificity, cytotoxicity status and TCR copy number / cell surface expression status. This may be a particularly useful combination of parameters for selecting useful candidates. As noted above, the methods of the invention comprise selecting candidate TCRs or cells expressing candidate TCRs with defined characteristics by analysing determined cell type, cell status, TCR binding characteristics and optionally cell surface protein quantification (marker abundance) within each of the plurality of clusters. Through applying the methods of the invention, striking correlation between TCR sequence similarities and expression phenotype similarities has been observed. Clonotypes that are clustered together based on TCR CDR sequence similarity have been shown to share similar cell type / state and binding characteristics. As the methods of the invention show that TCR CDR sequence similarity clusters have common cell type, cell status, TCR binding characteristics and optionally cell surface protein quantification (marker abundance), a set of clustered TCR sequence clusters or individual TCR sequences within a cluster can be selected to suit different therapeutic applications. For each cluster, the determined cell type, cell status, TCR binding characteristics and optional cell surface protein quantification (marker abundance), of each clonotype may be presented in a manner that facilitates selection of the candidate TCRs or cells expressing candidate TCRs. For example, radar plots (see Figure 7) may be utilised to facilitate assessment of the determined cell type, cell status and TCR binding characteristics of the members of each cluster. Other visual aids may be used but are not essential. Tabulation of values may be assessed in automated fashion. The selection of the candidate TCRs or cells expressing candidate TCRs will be determined by the cell type, cell status, TOR binding characteristics and optionally cell surface protein quantification (marker abundance), of the TCRs and their relevance to the TCR therapeutic of interest. Comparisons can be made between TCRs within a cluster and / or between clusters. In one embodiment, the candidate TCRs or T cells expressing candidate TCRs are selected for intermediate to high affinity, high persistence and high expanding potential. These are relevant phenotypes for TCR-T cell therapies. TCR-T cell therapies may involve expressing the TCRs by exogenous means (for example, viral transfection) or endogenous modification (for example TCR introduced by transposon, zinc finger nuclease or CRISPR methods). According to the methods of the invention, the selection of candidate TCRs or cells expressing candidate TCRs may be the result of selection of one or more clusters of TCR sequences or one or more specific TCR sequences within a particular cluster of closely related sequences. In one embodiment, the candidate TCRs orT cells expressing candidate TCRs are TCRap or TCRyC, preferably TCRap. In some embodiments, a selection is made for TCR sub-sequences within the candidate TCRs or T cells expressing candidate TCRs. In one embodiment the complementarity determining regions (CDR) are selected from one or more of, preferably all of, Va CDR1, Va CDR2, Va CDR3, Vp CDR1, VP CDR2 and Vp CDR3 of a candidate TCR or cell expressing a candidate TCR. In one embodiment, the selection is made for the TCRa chain variable domain Va. In one embodiment, the selection is made for the TCRp chain variable domain Vp. In a further embodiment, the selection is made for the TCRa and TCRp chain variable domains Va and vp. As part of the methods of the invention, the distances among clonotypes in the TCR CDR sequence space and the gene expression space may be calculated so that a smaller distance indicates higher similarity. For any individual TCR or cluster of TCRs, the most dominant cell type / state of it and its top ten nearest neighbours may be assessed. It has been advantageously observed in the experiments performed according to the methods of the invention that the same cell type and cell status as the TCR in question is the most frequent among its nearest neighbours. In addition, TCRs belonging to different TCR CDR sequence distance bins may be compared. It has been advantageously observed in the experiments performed according to the invention that an increase in TCR CDR sequence distance is accompanied by a significant increase in gene expression phenotype (cell type, cell status and TCR binding characteristics) distance. It has also advantageously been observed that T cells sharing the same paired TCR sequence (“intra-clonal”) have significantly lower gene expression phenotype distance than those with different TCR sequences (“inter-clonal”). These observations quantitatively demonstrate the correlation between TCR sequence similarity and expression phenotype similarity. Considering the pivotal role of TCR to the function of T cells, the results derived from experiments performing methods of the invention in experimental animals support the hypothesis that the sequence (and hence the intrinsic properties) of a TCR likely drives the cellular phenotypes of the T cells that express the TCR. The methods of the invention may comprise further steps following the selection of candidate TCRs or cells expressing candidate TCRs. In one embodiment the methods further comprise in vitro and / or in silico testing of the candidate TCRs or T cells expressing candidate TCRs. The methods may further comprise in vivo testing of the candidate TCRs or T cells expressing candidate TCRs, typically following in vitro and / or in silico testing. Thus, in vitro and / or in silico testing may provide for further selection of candidate TCRs or T cells expressing candidate TCRs and thus a subset of the original tested candidates may be taken into in vivo testing. The methods may also involve processes for improving the binding properties of selected TCRs, for example through affinity maturation. Further modifications that may be made are described herein below in the context of producing TCRs and associated cell therapies. According to the methods of the invention, it may be beneficial to incorporate a functional test of the T cells, preferably antigen specific T cells, that are produced by immunizing the experimental animals with the antigen of interest. In these aspects, the antigen is a disease related antigen. Thus, following immunization, T cells are recovered and preferably enriched / selected for antigen binding, as described herein (e.g. using appropriate cell sorting methods). The T cells may then be screened in an animal model of the disease to select those T cells that provide some level of treatment of the animal model. T cells may then be recovered from the animals where treatment was demonstrated. This may provide additional beneficial information on TCR in vivo function and persistence. In one aspect, this approach is used to pre-select potential candidate T cells before performing the methods of the invention. Thus, in this aspect, single cell data are generated from the plurality of T cells recovered from the animals where treatment was demonstrated. In another aspect, this approach is used in parallel implementations of the methods of the invention to provide simultaneous selection and validation. According to this aspect, recovered T cells, preferably sorted antigen specific T cells are separated into two aliquots. A second aliquot is used to generate single cell data and perform the methods of the invention to select candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics. A first aliquot is screened in the animal model of the disease to select those T cells that provide some level of treatment of the animal model. T cells are then recovered from the animals where treatment was demonstrated. Those T cells are then used to generate single cell data and perform the methods of the invention to select candidate T cell receptors (TCRs) orT cells expressing candidate TCRs with defined characteristics. A comparison can then be made between the selected candidates resulting from both methods. The comparison permits the selection of a subset of candidates with defined characteristics that were also effective in the animal model. Note the terms “first” and “second” do not imply an order of processing of the aliquots. Accordingly, the invention provides a method of selecting candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics, comprising a) separating a plurality of T cells obtained from at least one, and preferably a plurality of, experimental animals (preferably that are transgenic and express non-native, most preferably humanised TCRs) that have been immunized with a disease-associated antigen into two aliquots; b) using a first aliquot: i. administering the first aliquot to a plurality of experimental animals expressing the disease-associated antigen, preferably wherein the plurality of experimental animals have tumors that display the antigen ii. obtaining a plurality of T cells from the animals treated, optionally successfully treated, using the first aliquot iii. generating single cell data from the plurality of T cells (obtained in b) ii.) wherein the single cell data comprises: 1. paired TCR sequences; 2. gene expression quantification; 3. antigen binding quantification; and optionally 4. cell surface protein quantification; iv. clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters and determining cell type, cell status and TCR binding characteristics from the single cell data; and c) using a second aliquot: i. generating single cell data from the plurality of T cells wherein the single cell data comprises: 1. paired TCR sequences; 2. gene expression quantification; 3. antigen binding quantification; and optionally 4. cell surface protein quantification; ii. clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters and determining cell type, cell status and TCR binding characteristics from the single cell data d) identifying candidate TCRs or cells expressing candidate TCRs from the first aliquot and second aliquot with defined characteristics by analysing determined cell type, cell status, TCR binding characteristics and optionally cell surface protein quantification, within each of the plurality of clusters, and analysing changes in the expressed antigen, preferably changes in the tumors expressing the antigen from candidate TCRs or cells expressing candidate TCRs from the first aliquot; e) comparing the identified candidate TCRs or cells expressing candidate TCRs with defined characteristics obtained from each aliquot to select candidate TCRs or cells expressing candidate TCRs. It will be understood that the T cell receptors (TCRs) or T cells expressing candidate TCRs processed from the first and second aliquots can be processed together or separately according to the method. For example, the obtained plurality of T cells from the animals treated, optionally successfully treated, using the first aliquot (according to step b) ii) may be pooled with the plurality of T cells in the second aliquot so that the steps of generating single cell data, b) ill and c) ii are performed on a combined (pooled) sample. The subsequent clustering can also be completed using results from the combined (pooled) sample. Alternatively, the steps of generating single cell data (b) iii and c) ii) and subsequent clustering (b) iv and c) ii) can be performed on parallel processed (non pooled) samples. In either case, the identification and comparison steps (steps d and e) result in a comparison that is made on TCRs derived from both aliquots because the second aliquot represents status before transplantation and the first aliquot represents status after the T cells have encountered for example a tumour, post transplantation. The method allows the identification of candidate TCRs or cells expressing candidate TCRs with desirable properties from both aliquots. Those candidates represented in both aliquots (sets) and which persisted and were able to effect treatment may be selected. Preferably the selected candidates are expanded from the first aliquot. This may result in an improved subset of candidates compared with using either method in isolation. This implementation of the invention incorporates all relevant features and disclosures concerning the general methods of the invention mutatis mutandis and they are not repeated for reasons of conciseness. For example, enrichment and sorting steps are described herein and are not reproduced in full. Cell enrichment may be by cell sorting using fluorescence, optionally by using a fluorescently labelled barcoded antigen probe and optionally a negative control antigen probe. For the avoidance of doubt, the disease related antigen used to immunize the experimental animals is the same as the antigen expressed in the disease model. The animals used for both steps are typically of the same type. Preferably, all animals are rodents, most preferably mice. Suitable disease models are well known in the art, especially in mice. Metrics to observe and measure disease treatment (to effect treatment) in those models are similarly well known. For example, measuring tumor changes are known to the skilled person and may include measuring shrinkage, for example through measuring the dimensions, area or volume of the tumor or tumors. Successfully treated mice may have positive tumor changes compared to control animals in one or more tumor change measurements. For example, successfully treated mice may show a reduction tumor growth compared with control animals. Without wishing to be bound by theory, tumors that express the antigen in the animals, preferably mice, and do not grow or reduce in size in response to the administered T cells provide an in vivo readout that effective T cells are present and that they persist in vivo. Harvesting a plurality of T cells from these animals, preferably mice, and performing the selection methods on these T cells is useful in combination with performing the selection methods on the first aliquot of T cells. The TCR sequences with defined characteristics determined by analysing determined cell type, cell status, TCR binding characteristics, both before and after transplantation (and in vivo tumor encounter) can be compared so that clonotypes that are persisted (preferably also expanded) and with desirable phenotypes are selected. The invention further provides a method of producing a T cell receptor (TCR) that binds to an antigen of interest comprising performing a method of selecting candidate TCRs or T cells expressing candidate TCRs according to the invention and expressing a selected candidate TCR in a cell, typically a T cell. This method may function to provide a TCR cell therapy, particularly a TCR-T cell therapy, to treat a disease of interest, for example cancer. Reference to expressing a selected candidate TCR in a cell relates to either expressing a whole sequence of the selected candidate TCR or expressing sub sequences of the selected candidate TCR, such as sequences selected from one or both of the paired TCR hemi chains of the selected candidate TCR. In one embodiment, the sub sequences comprise one or more of, preferably all of, Va CDR1, Va CDR2, Va CDR3, Vp CDR1, Vp CDR2 and vp CDR3 sequences. Such sub sequences may be grafted onto suitable framework sequences. In one embodiment, the sub sequences comprise a TCRa chain variable domain Va. In one embodiment, the sub sequences comprise a TCRp chain variable domain Vp. In one embodiment, the sub sequences comprise a TCRa chain variable domain Va and the TCRp chain variable domain Vp. The TCR that is expressed in a cell, whether expressed as a whole sequence of the selected candidate TCR or expressing sub sequences of the selected candidate TCR, may comprise variations or further modifications. In some embodiments, the expressed TCRs include one or more amino acid variations, e.g., substitutions, deletions, insertions, and / or mutations, compared to the sequence of the selected TCR. Amino acid substitutions can be introduced into a binding molecule of interest and the products screened for a desired activity, e.g., decreased immunogenicity, improved half-life, CD8-independent binding or activity, surface expression, promotion of TCR chain pairing and / or other improved properties or functions for cell type, cell status and TCR binding characteristics defined herein. In some embodiments, one or more residues within a CDR of a selected TCR is / are substituted. In some embodiments, the substitution is made to revert a sequence or position in the sequence to a germline sequence, such as a binding molecule sequence found in the germline (e.g., human germline), for example, to reduce the likelihood of immunogenicity, e.g., upon administration to a human subject. Substitutions, insertions, or deletions can be made to one or more CDRs so long as such alterations do not substantially reduce the ability of the binding molecule, e.g., TCR or antigen-binding fragment thereof, to bind the antigen. For example, conservative alterations (e.g., conservative substitutions as provided herein) that do not substantially reduce binding affinity can be made in CDRs. Such alterations can, for example, be outside of antigen contacting residues in the CDRs. In certain embodiments of the variable sequences provided herein, each CDR either is unaltered, or contains no more than one, two or three amino acid substitutions. In some embodiments, the cell expressing the candidate TCR is for use as an adoptive cell therapy. Non limiting examples of adoptive cell therapies include those involving administering to a subject, for example a subject with cancer relevant to an antigen targeted by the TCR, an effective amount of cells, preferably recombinant cells (e.g., recombinant immune cells such as T cells) that express the selected TCR according to methods of the invention. TCR therapies modify the patient’s T lymphocytes ex vivo before being administered back into the patient’s body, similar to CAR-T therapies. The methods of the invention identify candidate TCR sequences based on the variable regions. The constant domains in the experimental animals may be non-native (as compared to the variable regions, but native to the experimental animal). Thus, when expressed in a cell, such as a T-cell, for cell therapy purposes, the candidate TCR sequences are expressed with an appropriate constant domain based on the therapy and recipient in question. In one embodiment, the selected candidate TCR comprises a human TCR constant domain and is expressed as a human TCR in a cell. Methods of expressing candidate TCRs in a cell, in particular a human T cell, are well known, in particular to provide cell therapies. Biological methods for introducing nucleic acids into a host cell (to express TCRs) can include the use of DNA and RNA vectors. Viral vectors, and in particular retroviral, especially lentiviral vectors, are widely used for inserting nucleic acids into human cells. Other viral vectors can be derived from poxviruses, herpes simplex vims I, adenoviruses and adeno-associated viruses. Lentivirus is generally preferred. Examples of vectors are plasmids, autonomously replicating sequences, and transposable elements. In one embodiment, the selected TCR may be expressed by endogenous modification of the cell or expressed exogenously. In some embodiments, such methods include transfecting or transducing cells with a nucleic acid or expression vector of the present disclosure, e.g., an expression vector comprising a nucleic acid that encodes a selected candidate TCR. Further exemplary vectors can include, without limitation, plasmids, phagemids, cosmids, artificial chromosomes such as yeast artificial chromosome (YAC), bacterial artificial chromosome (BAG), or PI -derived artificial chromosome (PAG), bacteriophages such as lambda phage or M13 phage, and animal viruses. Examples of animal viruses useful as vectors can include, without limitation, retro vims (including lentivims), adenovirus, adeno-associated vims (AAV), herpesvirus (e.g., herpes simplex vims), poxvirus, baculovims, papillomavirus, and papovavirus (e.g., SV40). In certain embodiments, the vector comprising the TCR can be a retroviral vector, in particular a lentiviral vector. The term “transfection” or “transduction” is used to refer to the introduction of foreign DNA into a cell. A cell has been “transfected” when exogenous DNA has been introduced inside the cell membrane. A number of transfection techniques are generally known in the art. Such techniques can be used to introduce one or more exogenous DNA moieties into suitable host cells. The term refers to both stable and transient uptake of the genetic material. In some embodiments, a cell of the present disclosure is produced by transfecting the cell with a viral vector encoding the selected TCR. In some embodiments, such methods include activating a population of T cells (e.g., T cells obtained from an individual to whom a TCR T cell therapy will be administered), stimulating the population of T cells to proliferate, and transducing the T cell with a viral vector encoding the selected TCR. In some embodiments, the T cells are transduced with a retroviral vector, e.g., a gamma retroviral vector or a lentiviral vector, encoding the selected TCR. In some embodiments, the T cells are transduced with a lentiviral vector encoding the selected TCR. Cells produced by methods of the present invention may be autologous / autogeneic (“self’) or non-autologous (“non-self,” e.g., allogeneic, syngeneic or xenogeneic). “Autologous” as used herein, refers to cells from the same individual. “Allogeneic” as used herein refers to cells of the same species that differ genetically from the cell in comparison. “Syngeneic,” as used herein, refers to cells of a different individual that are genetically identical to the cell in comparison. In a further aspect, the invention provides a method of producing a T cell receptor (TCR) compound wherein the TCR compound binds to an antigen of interest. The method comprises performing the method of selecting candidate T cell receptors (TCRs) orT cells expressing candidate TCRs according to the invention to select one or more candidate TCRs. The method further comprises producing a TCR compound comprising paired TCR sequences of the selected candidate one or more TCRs. The method may comprise selecting sub sequences of the selected candidate T cell receptors (TCRs) or T cells to produce the TCR compound comprising paired TCR sequences. The sub sequences may be any sequence described herein that enables the TCR compound to bind the antigen of interest. In one embodiment, the sub sequences comprise one or more of, preferably all of, Va CDR1, Va CDR2, Va CDR3, Vp CDR1, Vp CDR2 and Vp CDR3. In another embodiment, the sub sequences comprise the CDR3 sequences of both hemi chains (for example op or y6). In one embodiment, the sub sequences comprise a TCRa chain variable domain Va. In one embodiment, the sub sequences comprise a TCRp chain variable domain Vp. In one embodiment, the sub sequences comprise a TCR a chain variable domain Va and the TCR p chain variable domain vp. As outlined above, the paired TCR sequences may be used or sub sequences can be used to produce the TCR compound. The TCR compound may be provided in any suitable form, preferably therapeutic forms. The TCR compound includes but is not limited to naturally occurring and non-naturally occurring TCRs; full-length TCRs and antigen binding portions thereof, chimeric TCRs; TCR fusion constructs; and synthetic TCRs. In one embodiment, the TCR compound is an antigen binding derivative or fragment of the TCR. An “antigen binding derivative” or “fragment of the TCR” refers to any portion of a TCR less than the whole that retains the ability to bind the antigen. The TCR compound, whether comprising a whole TCR or antigen binding derivative or fragment of the TCR, may comprise variations or further modifications over the selected TCR or cells expressing TCRs according to methods of the invention. In some embodiments, the binding molecules, e.g., TCRs or antigen-binding fragments thereof, include one or more amino acid variations, e.g., substitutions, deletions, insertions, and / or mutations, compared to the sequence of a binding molecule, e.g., any TCR described herein. Exemplary variants include those designed to improve the binding affinity and / or other biological properties of the binding molecule identified herein. This may also include modifications with regards to cell type, cell status and TCR binding characteristics associated with a selected TCR. Amino acid sequence variants of a binding molecule can be prepared by introducing appropriate modifications into the nucleotide sequence encoding the binding molecule, or by peptide synthesis. Such modifications include, for example, deletions from, and / or insertions into and / or substitutions of residues within the amino acid sequences of the binding molecule. Any combination of deletion, insertion, and substitution can be made to arrive at the final construct, provided that the final construct possesses the desired characteristics, e.g., specifically bind to the antigen. Various TCR compounds, including antigen binding derivatives or fragments of the TCR, can be made from the TCR selected accorded to the methods of the invention. The TCR compounds, e.g., TCRs or antigen-binding fragments thereof, can include one or more amino acid substitutions, e.g., as compared to the originally selected candidate TCR molecule. Sites of interest for substitutional mutagenesis include the CDRs, FRs and / or constant regions. Amino acid substitutions can be introduced into a binding molecule of interest and the products screened for a desired activity, e.g., retained / improved antigen affinity or specificity, decreased immunogenicity, improved half-life, CD8-independent binding or activity, surface expression, promotion of TCR chain pairing and / or other improved properties or functions for cell type, cell status and TCR binding characteristics defined herein. In some embodiments, one or more residues within a CDR of a selected TCR is / are substituted. In some embodiments, the substitution is made to revert a sequence or position in the sequence to a germline sequence, such as a binding molecule sequence found in the germline (e.g., human germline), for example, to reduce the likelihood of immunogenicity, e.g., upon administration to a human subject. In some embodiments, a functional variant is made from a selected TCR or a TCR-derived binding molecule. The term "functional variant," as used herein, refers to a binding molecule having an adequate or significant sequence identity to a selected TCR. Further, the functional variant retains the same biological activity, in particular ability to bind the antigen, as the selected TCR. The functional variant encompasses those variants of the TCR described herein (the parent TCR, polypeptide, or protein) that retain the ability to specifically bind to a target antigen for which the selected TCR has antigenic TCR binding characteristics or to which the selected TCR specifically binds. In reference to the selected TCR, polypeptide, or protein, the functional variant can, for instance, be at least about 30%, 50%, 75%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or more identical in amino acid sequence to the selected TCR, polypeptide, or protein. Substitutions, insertions, or deletions can be made to one or more CDRs so long as such alterations do not substantially reduce the ability of the binding molecule, e.g., TCR or antigen-binding fragment thereof, to bind the antigen. For example, conservative alterations (e.g., conservative substitutions as provided herein) that do not substantially reduce binding affinity can be made in CDRs. Such alterations can, for example, be outside of antigen contacting residues in the CDRs. In certain embodiments of the variable sequences provided herein, each CDR either is unaltered, or contains no more than one, two or three amino acid substitutions. In one embodiment, the antigen binding derivative or fragment of the TCR comprises CDRs 1-3 from the TCRa and TCRp chains of the selected candidate TCRs. In another embodiment, the antigen binding derivative or fragment of the TCR comprises CDR3 of the TCRa and TCRp chains of the selected candidate TCRs. The antigen binding derivative may be a soluble TCR encoding the TCRa and TCRp chains and lacking the transmembrane and intracellular domains of the selected candidate TCRs. The soluble TCR may further comprise a CD3 binding domain, preferably a scFv. The soluble TCR may further comprise a half-life extending domain, preferably a Fc domain. Additional TCR-based therapeutics established in the art are envisaged as being produced based on candidate T cell receptors (TCRs) or T cells expressing candidate TCRs selected from the methods described herein. In one embodiment the antigen binding derivative is a TCR mimic comprising two antibody-derived scFvs covalently linked through a peptide linker. In one embodiment the antigen binding derivative is a T cell antigen coupler (TAC) comprising a first binding domain for antigen recognition and a second binding domain for recruitment of signalling components from an endogenous TCR complex. In one embodiment the antigen binding derivative is a TCR fusion construct (TRuC) comprising an antibody binding domain fused to selected TCR domains. In one embodiment the antigen binding derivative is a synthetic TCR antigen receptor / HLA-independent TCR (STAR / HIT) wherein the variable domains of the selected candidate TCRs have been replaced with the Vh / Vl domains of an antibody. In one embodiment the antigen binding derivative is a bispecific T cell engager (BiTE). BiTEs are a bispecific antibody construct with a unique function, simultaneously binding an antigen on tumor cells and a surface molecule on T cells to induce tumor lysis. Importantly, the molecular architecture of the TCR therapeutics envisaged according to the invention differs and performing the methods of the invention enables the selection of favourable TCR sequences and their sub sequences with phenotypes (cell type, cell status and TCR binding characteristics) to suit to TCR therapeutic of interest. For example, the TCR compound may be an antigen binding derivative or fragment of the TCR, wherein the antigen binding derivative is a soluble TCR, preferably wherein the soluble TCR has high binding affinity and high binding specificity for the target antigen (that is attainable from the experimental animal). Further affinity maturation on the selected TCR may be performed thereafter to provide the final candidate TCR. The produced TCR compounds may be expressed in a cell, optionally according to methods described herein. In one embodiment, the selected candidate TCR, antigen binding derivative or fragment of the TCR are expressed in a T lymphocyte or T lymphocyte progenitor. In one embodiment, the cell is a CD4+ or a CD8+ positive T-cell. In one aspect, the invention provides a TCR or T cell selected according to the methods of the invention. In one aspect, the invention provides an isolated nucleic acid encoding a selected candidate TCR selected according to the methods of the invention or TCR compound produced according to the methods of the invention. As used herein, the term "nucleic acid" refers to a polymer comprising multiple nucleotide monomers (e.g., ribonucleotide monomers or deoxyribonucleotide monomers). "Nucleic acid" includes, for example, genomic DNA, cDNA, RNA, and DNA-RNA hybrid molecules. Nucleic acid molecules can be naturally occurring, recombinant, or synthetic. In addition, nucleic acid molecules can be single- stranded, double-stranded or triple- stranded. In some embodiments, nucleic acid molecules can be modified. In the case of a double-stranded polymer, "nucleic acid" can refer to either or both strands of the molecule. The nucleic acid may be provided in an expression vector. Examples of expression vectors include, but are not limited to, pCIneo vectors (Promega) for expression in mammalian cells; pLenti4A / 5-DEST™, pLenti6A / 5- DEST™, murine stem cell virus (MSCV), MSGV, moloney murine leukemia virus (MMLV), and pLenti6.2 / V5-GW / lacZ (Invitrogen) for lentivirus-mediated gene transfer and expression in mammalian cells. In one aspect, the invention provides a recombinant vector comprising a nucleic acid of the invention. Expression control sequences, control elements, or regulatory sequences present in the recombinant vector are those non-translated regions of the vector - origin of replication, selection cassettes, promoters, enhancers, translation initiation signals (Shine Dalgarno sequence or Kozak sequence), introns, a polyadenylation sequence, 5' and 3' untranslated regions, and / or the like - which interact with host cellular proteins to carry out transcription and translation. Such elements may vary in their strength and specificity. Depending on the vector system and host utilized, any number of suitable transcription and translation elements, including ubiquitous promoters and inducible promoters may be used. Components of the recombinant vector are operably linked such that they are in a relationship permitting them to function in their intended manner. In some embodiments, the recombinant vector is an episomal vector or a vector that is maintained extra chromosomally. As used herein, the term “episomal” refers to a vector that is able to replicate without integration into the host cell’s chromosomal DNA and without gradual loss from a dividing host cell also meaning that said vector replicates extra chromosomally or episomally. Such a vector may be engineered to harbor the sequence coding for the origin of DNA replication or “ori” from an alpha, beta, or gamma herpesvirus, an adenovirus, SV40, a bovine papilloma virus, a yeast, or the like. The host cell may include a viral replication transactivator protein that activates the replication. Alpha herpes viruses have a relatively short reproductive cycle, variable host range, efficiently destroy infected cells and establish latent infections primarily in sensory ganglia. Illustrative examples of alpha herpes viruses include HSV 1, HSV 2, and VZV. Beta herpesviruses have long reproductive cycles and a restricted host range. Infected cells often enlarge. Non-limiting examples of beta herpes viruses include CMV, HHV-6 and HHV-7. Gammaherpesviruses are specific for either T or B lymphocytes, and latency is often demonstrated in lymphoid tissue. Illustrative examples of gamma herpes viruses include EBV and HHV-8. As already mentioned, methods for introducing nucleic acids into a host cell (to express TCRs) can include the use of DNA and RNA vectors. Viral vectors, and in particular retroviral, especially lentiviral vectors, are widely used for inserting nucleic acids into human cells. Other viral vectors can be derived from poxviruses, herpes simplex vims I, adenoviruses and adeno-associated viruses. Lentivirus is generally preferred. Examples of vectors are plasmids, autonomously replicating sequences, and transposable elements. Further exemplary vectors can include, without limitation, plasmids, phagemids, cosmids, artificial chromosomes such as yeast artificial chromosome (YAC), bacterial artificial chromosome (BAG), or PI -derived artificial chromosome (PAG), bacteriophages such as lambda phage or M13 phage, and animal viruses. Examples of animal viruses useful as vectors can include, without limitation, retro vims (including lentivims), adenovirus, adeno-associated vims (AAV), herpesvirus (e.g., herpes simplex vims), poxvirus, baculovims, papillomavirus, and papovavirus (e.g., SV40). In certain embodiments, the vector comprising the TCR-encoding nucleic acid can be a retroviral vector, in particular a lentiviral vector. In one aspect, the invention provides a recombinant host cell comprising a TOR selected according to the method of the invention, or TCR compound produced according to the method of the invention, or a nucleic acid of the invention, or a vector of the invention. Preferably, the host cell is a lymphocyte, T lymphocyte or T lymphocyte progenitor, a CD4 positive or a CD8 positive T-cell. “Recombinant host cells,” “host cells,” “cells,” “cell lines,” “cell cultures,” and other such terms denoting microorganisms or higher eukaryotic cell lines, refer to cells which can be, or have been, used as recipients for a recombinant vector or other transferred DNA, and include the progeny of the cell which has been transfected. Host cells may be cultured as unicellular or multicellular entities (e.g., tissue, organs, or organoids) including an expression vector of the present disclosure. In one embodiment, the recombinant host cell is an immune cell. Non-limiting examples of recombinant immune cells which may include any of the expression vectors of the present disclosure include? cells, B cells, natural killer (NK) cells, macrophages, monocytes, neutrophils, dendritic cells, mast cells, basophils, and eosinophils. In some embodiments, the immune cell is a T cell. Examples of T cells include naive T cells (TN), cytotoxic T cells (TCTL), memory T cells (TMEM), T memory stem cells (TSCM), central memory T cells (TCM), effector memory T cells (TEM), tissue resident memory T cells (TRM), effector T cells (TEFF), regulatory T cells (TREGS), helper T cells (TH, TH1 , TH2, TH 17) CD4+ T cells, CD8+ T cells, virus-specific T cells, alpha beta T cells (Tap), and gamma delta T cells (TUd). In another aspect, the cells provided herein comprise stem cells that can differentiate into an immune cell, in particular a T-cell, e.g., an embryonic stem cell or an adult stem cell (such as hematopoietic stem cells). In one aspect, the invention provides a pharmaceutical composition. The pharmaceutical composition comprises the candidate TCR selected according to the method of the invention, or TCR compound produced according to method of the invention, or nucleic acid of the invention, or vector of the invention, or host cell of the invention, and a pharmaceutical acceptable carrier, stabilizer and / or excipient. Pharmaceutically acceptable carriers, diluents, adjuvants and excipients are well known in the pharmaceutical art and are described, for example, in Remington's Pharmaceutical Sciences, 15th or 18th Ed. (Alfonso R. Gennaro, ed.; Mack Publishing Company, Easton, PA, 1990); Remington: the Science and Practice of Pharmacy 19th Ed.(Lippincott, Williams &Wilkins, 1995); Handbook of Pharmaceutical Excipients, 3rd Ed. (Arthur H. Kibbe, ed.; Amer. Pharmaceutical Assoc, 1999); Pharmaceutical Codex: Principles and Practice of Pharmaceutics 12th Ed. (Walter Lund ed.; Pharmaceutical Press, London, 1994); The United States Pharmacopeia: The National Formulary (United States Pharmacopeial Convention); Fiedler’s “Lexikon der Hilfstoffe” 5th Ed., Edition Cantor Verlag Aulendorf 2002; “The Handbook of Pharmaceutical Excipients”, 4th Ed., American Pharmaceuticals Association, 2003; and Goodman and Gilman's: the Pharmacological Basis of Therapeutics (Louis S. Goodman and Lee E. Limbird, eds.; McGraw Hill, 1992), the disclosures of which are hereby incorporated by reference. The carriers, diluents, adjuvants and pharmaceutical excipients can be selected with regard to the intended route of administration and standard pharmaceutical practice. In one aspect, the invention provides a compound or composition for use in medicine, wherein the compound or composition comprises a TCR selected according to the method of the invention, or TCR compound produced according to the method of the invention, or a nucleic acid of the invention, or a vector of the invention, or a host cell of the invention, or a pharmaceutical composition of the invention. In one embodiment, the compound or composition is for use in the treatment of a disease selected from a proliferative disorder, autoimmune disease or infection. Preferably the treatment is of a human subject. However, in alternative embodiments the treatment may be of a non-human subject, for example a companion animal such as a cat or a dog. The compound or composition may be a cell or engineered cell disclosed herein. Such medical uses can be expressed in any form, including as methods of treatment of the subject. All embodiments herein apply to such methods or medical uses, however expressed. Thus, in one aspect, the use of a compound or composition according to the invention is provided for the manufacture of a medicament treating a disease selected from a proliferative disorder, autoimmune disease or infection. Similarly, in one aspect, provided herein are methods of treatment of a condition associated with a proliferative disorder, autoimmune disease or infection. The methods may comprise administering an effective amount of the compound or composition described herein to a subject in need thereof. As used herein, “treatment” (and grammatical variations thereof such as “treat” or “treating”) refers to clinical intervention in an attempt to alter the natural course of the individual being treated and can be performed either for prophylaxis or during the course of clinical pathology. Desirable effects of treatment include, but are not limited to, preventing occurrence or recurrence of disease or disorder or abnormality, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis. In some embodiments, compounds or composition of the invention are used to delay development of a disease or to slow the progression of a disease, disorder or abnormality. In one embodiment the proliferative disorder is cancer. A "cancer" refers to a broad group of various diseases characterized by the uncontrolled growth of abnormal cells in the body. Unregulated cell division and growth results in the formation of malignant tumors that invade neighbouring tissues and may also metastasize to distant parts of the body through the lymphatic system or bloodstream. A "cancer" or "cancer tissue" can include a tumor. Therefore the cancer may be any appropriate form of cancer such as solid and liquid cancers. Specific examples include ovarian cancer, melanoma, non-specific metastatic cancer, metastatic renal cancer, leukaemia, lymphoma or an advanced melanoma. In one embodiment, the compound or composition is for use in the treatment of a disease comprising treatment as an immune therapy, preferably adoptive autologous or heterologous T-cell therapy. The T-cell therapy may involve the use of a TCR according to the method of the invention, or a nucleic acid of the invention. Description of the Figures Figure 1: Populations of expanded clonotypes discovered from immunized mice Each panel represents the TCR repertoire discovered from a cohort of mice immunised with the same target peptide(s), where each hexagon represents a clonotype with the size indicates the degree of expansion and the colour indicates level of mRNA expression of the TCR. Figure 2: Overview of sequencing based multi-modality TCR phenotyping. Overview of performance steps, clustering and multi-modal TCR selection. Figure 3: Example of clonotypes with similar CDR sequences sharing similar cellular phenotypes The top left panel shows TCR clonotypes discovered from immunised mice as a graph with a force-directed layout, where each node is a clonotype, the size of the node indicates the degree of expansion and connected nodes share similar CDR sequences. Circled are examples of cluster of clonotypes sharing similar CDR sequences. In each example, the left of the two panels pointed to indicates the location of T cells belonging to those clonotypes in the UMAP representation derived from single cell gene expression, and the right panel indicates the location of those cells in the target-antigen-binding-vs-negative-control-biding (x-axis-vs-y-axis) scatter plot derived from single cell antigen binding data. The three examples, from top to bottom, are a) clustered clonotypes showing exclusively memory-like expression phenotype with medium-low avidity / specificity, b) those showing exclusively exhausted-like expression phenotype with high avidity / specificity, and c) those showing a mixture of effector / proliferating / exhausted-like expression phenotype with medium-high-to-high avidity / specificity. Figure 4: Example of T cells of the same clonotype showing similar expression phenotype in different mouse donors Top left panel is a histogram of clonotypes found in multiple mouse donors where x-axis shows the number of donors the clonotypes were discovered and the y-axis shows the number of such clonotypes. Circled is a clonotype discovered in 12 donors. The top middle panel shows the location of the clonotype in the clonotype sequence similarity graph and the corresponding T cells in the single cell gene expression UMAP. The top right panel shows the cell type / state composition of the clonotypes in 11 different donors where each vertical bar indicates a donor with at least 5 cells of the clonotype. The bottom panel shows the cell type / state composition of all clonotypes found in multiple donors each with at least 5 cells, where each vertical bar indicates a donor and the different colours of the boxes below indicate different clonotypes. Figure 5: Expression phenotype of neighbouring CD8 T cell clonotypes in the sequence similarity space The left panel shows the clonotype sequence similarity graph of CD8 T cells in a force-directed layout where each clonotype is coloured by its most common cell type / state. The right panel shows the proportion of cell type / state of a clonotype’s top 10 nearest neighbour clonotypes in the sequence similarity graph where the neighbours’ cell type / state is indicated by colour and the cell type / state of the clonotype in question is on the x-axis. Figure 6: Correlation between clonotype sequence similarity and expression phenotype similarity The left panel shows the distribution of expression phenotype similarity represented as distance in the gene expression space between pairs of clonotypes aggregated by bins of clonotype sequence similarity represented as distance in the CDR sequence space between the same pairs of clonotypes. The right panel shows the distribution of expression phenotype similarity represented as distance in the gene expression space between pairs of cells belonging to the same clonotypes (left box) or different clonotypes (right box). Figure 7: Clusters of CD8 T cell clonotypes with different properties Clusters of CD8 T cell clonotypes with their properties shown as radar plots. See methods for the determination of each of the properties and the generation of clusters. The invention will be further understood with reference to the following non-limiting examples. Examples Animals and immunization methods Peptide / adjuvant based immunization of humanized TCR (hTCR) transgenic animals has been described previously. See, for example, Li et al., Nature Medicine 16(9):1029 2010, Obenaus etal., Nature Biotechnology 33(4):402-407 2015, Poncette et al., J Clin Invest 129(1):324-335 2019, and Moore et al., Science Immunology 6(66) 2021. hTCR transgenic mice are described in these documents and in co-pending GB patent applications GB2312191.6 and GB2405373.8. The computational work described below used T cells obtained from immunized hTCR transgenic mice. The genome of these mice contained targeted insertions of human DNA at the endogenous TCR a and p loci. Human TCR variable region sequences were generated through recombination of inserted unrearranged human beta v, d and j segments at the endogenous mouse beta locus and recombination of inserted unrearranged human alpha v and j segments at the endogenous mouse alpha locus. Immunogen: Purified peptides (>95% purity, Genscript) Adjuvants: Incomplete Freund’s adjuvant (IFA, Invivogen), CpG ODN 1826 (Invivogen) Prime dose (per mouse): 100pg of peptide and 50pg of CpG ODN / 50pl PBS emulsified with 50pl of IFA Boosts dose (per mouse): 50pg of peptide and 50pg of CpG ODN / 50pl PBS emulsified with 50pl of IFA Peptide based immunogens given via S.C. route of administration. Prime and two to three boosts schedule are typically used for these studies and with 21 days gap between prime and boost and 14 days gap between each boost. Tissues were typically collected day 7 post each boost. In the present example, the method was conducted following the work flow in Figure 2. Tissue samples were collected from the spleen and lymph nodes of mice immunized with target antigen. After enriching for CD8+ T cells, the cells were labelled with barcoded antibodies targeting cell surface proteins of interest and were sorted by fluorescently labelled barcoded target antigen probe and negative control antigen probe to enrich for target-antigen-specific cells. The sorted cells then underwent single cell partition where unique barcodes were attached to molecules in individual partitions. Next, whole transcriptome (GEX), immune profiling (VDJ) and cell surface protein (CSP) libraries were made according to the 10X commercial protocol. Paired TCR sequences and abundance of gene expression and cell surface protein were read by sequencing the cell's cDNA and various barcodes. Single cell partition in these experiments was performed using Chromium X apparatus from 10X genomics, which sorts a population of cells to physically separate single cells into individual droplets. Alternative systems include the Parse Evercode platform where cells are partitioned by their own membrane and unique barcodes are attached by combinatorial barcoding using multiple rounds of pool and split on plates. Any method of single cell partition may be used to provide an addressable population of indexed cells. Overview The method started with tissue samples collected from the spleen and lymph nodes of mice immunized with target antigen. After enriching for CD8+ T cells, the cells were labelled with barcoded antibodies targeting cell surface proteins of interest and were sorted by fluorescently labelled barcoded target antigen probe and negative control antigen probe to enrich for target-antigen-specific cells. The sorted cells then underwent single cell encapsulation where unique barcodes were attached to individual cells. Next, the paired TCR sequences, whole transcriptome and cell surface protein abundance was read by sequencing the cell’s cDNA and various barcodes. Based on paired TCR sequences, TCR clonotypes were defined and metrics including degree of expansion, sequence convergence, and CDR sequence similarity were calculated. Based on whole transcriptome quantification, the type and state of the sequenced cells was defined. Based on quantification of cell surface proteins and antigen binding, metrics including antigen binding avidity I affinity and specificity were calculated. Combining the different metrics, a multimodal depiction of the TCR clonotypes was obtained. Mapping raw sequences The raw sequencing reads were obtained in FASTQ format, where the structure of the reads conform to that as specified by 10X. The reads were mapped to a set of custom-made reference sequences using 10X CellRanger running in multi-modality mode. VDJ and GEX reference sequences were made based on the human TCR sequences actually inserted into the transgenic mice and CSP reference sequences based on the barcoding kit used (BioLegend TotalSeq C and Abeam lightning). The obtained mapped data included, for example: for VDJ library, single cell TCR chain / contig nucleotide sequences with annotation of V(D)J genes, position of framework regions (FWR) and complementary determining regions (CDR), and raw quantification of chain abundance as in the number of unique molecular identifiers (UMI); for GEX library, calling of cell-containing partitions (droplets) and single-cell raw quantification of gene expression (UMI); for CSP library, single-cell raw quantification of antigen-probe- / antibody-conjugated barcodes (UMI). QC, cell type annotation and expression phenotype scoring Cells were assigned to each mouse donors by demultiplexing using hashtag as part of the CSP library. Cell QC were performed to remove low quality cells (cite Seurat / Scanpy), potential doublets defined by gene expression (cite Scrublet) or hashtag (cite HashSolo) profile, and cells without a productive alpha chain or beta chain or with more than one beta chains. For early datasets (samples), the gene expression data were normalised and clustered in a K-nearest-neighbour (KNN) graph (see later section) using standard single-cell analysis workflow and clusters were annotated as broad cell types using well-known marker genes. The T cells were further re-clustered and annotated into finer cell types / states again using marker genes well-known in the field. Separate multinomial logistic regression classifiers were trained on the broad cell types and higher-resolution T cell types / states. These classifiers were used to call broad as well as high-resolution cell types / states from the normalised gene expression data for the later datasets (samples). Expression phenotype scoring was generated for each cell by comparing the abundance of a set of signature genes associated with certain phenotype against a set of randomly selected genes with matching abundance distribution in the total sample. Calling clonotypes TCR clonotypes were defined as sharing the same V and J gene and the same CDR3 amino acid sequence for both alpha and beta chain. Exact sub-clonotypes were defined as sharing the same V and J gene and the same CDR3 nucleotide sequence for both alpha and beta chain. For the purpose of clonotype selection, we used amino-acid-sequence-based clonotype definition as the functions and properties of a TCR is carried by its protein. Calculating single cell expression phenotype similarity After normalisation of raw gene expression quantification (where normalisation size factors were calculated with or without the top 5% most highly expressed genes), a set of 1000-5000 genes with highest expression variability in the sample were used for dimensionality reduction (for example by scaling to unit variance followed by principle component analysis to keep top 10-50 features or 70%-95% of total variance). The distances between pairs of cells in this dimensionality reduced space were calculated using a certain metric (Euclidean or cosine or other), which represented the expression phenotype similarity among cells. The top 10-50 nearest-neighbours of each cell in this same space was used to construct a KNN graph for cell type / state clustering as mentioned in the earlier section. Quantifying antigen specificity I affinity The raw quantification of binding to target antigen probe, negative control probe and the abundance of cell surface TCR were normalised by either the total abundance of CSP barcodes, or the total abundance of both CSP barcodes and GEX molecules, or a method that normalises the background level of non-specific barcodes. The cell's antigen specificity was calculated as the ratio of normalised abundance between the bound target-antigen probe and the bound negative control probe, and the cell's antigen affinity was calculated as the ratio of normalised abundance between the bound target-antigen probe and the cell surface TCR. Quantifying and visualising clonotype sequence similarity Clonotype sequence similarity were calculated on the basis of CDR amino acid sequences. We used a method that performed pairwise sequence alignment and conservation-based distance scoring. The distance between a pair of TCR clonotypes was calculated as the weighted sum of alignment distances score between CDR1s, CDR2s and CDR3s of the same chain, where CDR3s were given higher weight than CDR1s and CDR2s (using the TCR dist algorithm as described in Dash et al 2017). Pairs with larger distance were considered less similar, therefore, the inverse of such distance (or adjacency) was used as a measure of similarity. Treating each clonotype as a node and the similarity / adjacency as the weight of the edge linking the nodes, the relationship between the clonotypes was represented as a weighted undirected graph. Using a force-directed graph layout, clonotypes with high sequence similarity were placed close to each other forming tight connected components whereas clonotypes unrelated to each other were placed far apart. Quantifying clonotype expression phenotype similarity The similarity of expression phenotype between clonotypes was defined in two methods. In the first method, the median position in the dimensionality reduced of all cells belonging to the same clonotype was used to represent its expression phenotype in that space, and the distances between such median positions were calculated to represent the similarity (the larger the distance the lower the similarity). In the second method, the distance was defined as the (higher resolution) cell type / state compositional difference between two clonotypes, that clonotypes with similar cell type / state composition are considered more similar. Comparing phenotype similarity between cells of different clonotypes and between cells of the same clonotypes In this analysis, clonotypes discovered from mice immunised with the same peptide antigen were considered. Expression phenotype similarity I distance between single cells were calculated as previously described. The distribution of such distance between cells belonging to the same clonotype was compared with that between cells belonging to different clonotypes. Mann-Whitney U test was conducted to determine the statistical significance of the observed difference between the two groups. Correlating clonotype sequence similarity and expression phenotype similarity In this analysis, again, clonotypes discovered from mice immunised with the same peptide antigen were considered. The sequence distances and phenotype distances between all pairs of clonotypes were calculated as previously described. The pairs of clonotypes were binned into sequence distance bins and distributions of phenotype distance were compare across bins. Kruskal-Wallace test was conducted to determine the statistical significance of the observed difference across bins. Investigating the cell type / state composition of nearest neighbours of a clonotype in sequence similarity space Sequence similarity between clonotypes were calculated as previously described. For each clonotype, the most common cell type / state of it and that of the top 10 nearest neighbours in the sequence similarity space were found. For each cell type / state, the frequency of finding the same cell type / state in the clonotype of interest and its neighbours is compared to the frequency of finding any other cell type / state in the neighbours. Mann-Whitney U test was conducted to determine the statistical significance of the observed difference in frequency. Selecting TCR clonotypes Clustering and selecting TCRs based on multi-modality phenotyping Each TCR clonotype was encoded as a numeric vector using features from antigen specificity / affinity, gene expression and cell surface protein quantifications. The used features include VDJ-based degree of expansion, GEX-based scoring of naive-like, memory-like, effector-1 ike, exhaustion-like and proliferation expression signature, CSP-based antigen specificity and affinity, and CSP-based cell surface TCR copy number. This feature vectors were centred and scaled to zero mean and unit variance (termed as “pre-processed”) before used for clustering. Several clustering methods such as K-means, hierarchical and DBSCAN have been tested and Leiden clustering, a modularity-based graph clustering method was preferred in which a KNN graph was first generated from the pre-processed feature vectors and then the clustering that maximise within-cluster modularity was found. Clonotypes were selected from several phenotype clusters including: 1) characterised by high effector-like signature, medium memory-like signature, medium-low exhaustion-like signature, high specificity and medium-high-to-high affinity, low cell surface TCR copy number, 2) characterised by medium-low effector-like signature, low memory signature, low exhaustion-like signature, high proliferation signature, high-to-medium-high specificity and affinity, low cell surface TCR copy number, 3) high exhaustion-like signature, medium-low effector-like signature, low memory-like signature, low proliferation signature, medium-high affinity and medium-high-to-high affinity, medium-low cell surface TCR copy number, 4) medium memory-like signature, medium-low effector-like signature, low exhaustion-like signature, low-proliferation signature, high specificity and medium-high affinity, low cell surface TCR copy number. Verification of cell phenotype retention Desirably, TCRs selected using methods of the present invention are cloned into human T cells to generate TCR-T cell therapies. Mouse models may be used to confirm that TCRs selected according to the methods of the present invention are able to generate suitable TCR-T therapies. In brief, selected clonotypes are synthesized and transfected into mouse T cells, in which the endogenous host TCR is preferably knocked down / out. The transfected T cells, with or without prior stimulation, are transplanted into mice bearing tumours that display the pMHC target recognised by the T cells. Tumour growth is monitored for a period. At the end of the monitoring period, the positive target-antigen-binding T cells are again sorted and subjected to the same sequencing based multimodality phenotyping for comparison of persisted (and / or expanded) clonotypes and phenotype. Method for simultaneous TCR selection and verification After positive target-antigen-binding (and preferably negative control-antigen-binding) T cells are sorted from a first cohort of immunised mice, an aliquot of them are subjected to the sequencing based multi-modality phenotyping whereas another aliquot are transplanted directly into a second cohort of mice bearing tumours that display the same peptide target used to immunise the first cohort. Tumour growth is monitored for a period. At the end of the monitoring period, from mice with controlled / shrinked tumour, the positive target-antigen-binding T cells are again harvested and sorted and subjected to the same sequencing based multi-modality phenotyping. The clonotypes and the phenotype of the clonotypes before and after transplantation (and in vivo tumour encounter) are compared. Clonotypes that are persisted (and preferably expanded) and with desirable phenotypes are selected. Results from the present examples are provided in Figures 1 to 7. In Figure 1, a diverse population of expanded clonotypes from our immunized mice was observed. Figure 2 provides an overview of the approach adopted in the Example and magnified panels show that TCR Sequence similarity correlates with T cell phenotype similarity. Figure 3 shows examples of striking correlation between TCR sequence similarities and expression phenotype similarities. Clonotypes that are clustered together based on TCR CDR sequence similarity share similar cell type / state and binding characteristics. Figure 4 shows that a large number of TCR clonotypes were found in multiple mice. Cells of a multi-donor clonotype localise in similar positions in the gene expression space, regardless of the donor they are from. Similar cell type composition across donors is observed. Therefore a large number of shared / convergent clonotypes across mice immunized with the same antigen were observed, which provides the opportunity to assess their phenotypic similarity across donors. Striking cross-donor (mice) similarity in expression phenotype of T cells sharing the same TCR sequences was observed. Figure 4 shows clonotypes sharing the same sequence show highly similar cell type / state composition across donors. Figure 5 shows that by comparing clonotypes belonging to different TCR CDR sequence distance bins, it was observed that the increase in TCR CDR sequence distance is accompanied by a significant increase in gene expression phenotype distance. Also observed was T cells sharing the same paired TCR sequence (“intra-clonal”) have significantly lower gene expression phenotype distance than those with different TCR sequences (“inter-clonal”). Therefore figure 5 shows that clonotypes with similar expression phenotype cluster together in TCR sequence space. Figure 6 shows a quantitative comparison of expression phenotype similarity between same or different clonotypes. Clonotypes with more similar CDR sequences have more similar expression phenotype. T cells carrying same TCR are more similar in expression phenotype than those carrying different TCR sequences. This supports that the sequence and the intrinsic properties of the TCR likely drives the cellular phenotype of the T cells. Figure 7 shows clusters of CD8 T cell clonotypes with different properties. Clusters of CD8 T cell clonotypes with their properties shown as radar plots. See methods for the determination of each of the properties and the generation of clusters. Utilising the multimodal single cell sequencing data and the phenotypic metrics calculated from them enables clustering clonotypes by their defined characteristics and provides a diverse set of clusters from which candidate T cell receptors (TCRs) or T cells expressing candidate TCRs can be selected, particularly for therapeutic applications. References Chen et al (2017) J Exp Med. 214(11):3417-3433. Chen et al 2023 Cell Rep. 28;42(11):113279. Dash et al (2017) Nature. 547(7661):89-93. Ito et al (1996) J Exp Med. 183:2635-2644. Lagatutta et al (2013) bioRxiv. Preprint 2023 Jul 23. Han et al (2015) Nature Biotechnology. 32(7) 684-692. Klebanoff et al (2023) Nat Rev Drug Discov. 22(12):996-1017. Li et al (2010) Nat Med. 16(9):1029-34. Moore et al (2021) Sci Immunol. 17;6(66):eabj4026. Obenaus etal (2015) Nature Biotechnology. 33(4):402-407. Pascolo et al (1997) J Exp Med. 185(12):2043-2051. Poncette et al (2019) J Clin Invest. 129(1):324-335. Wahl etal (2022) Eur J Immunol. 52:237-246. Wang et al (2016) Cancer Immunology Research. 4(3):204. Yatsuda et al (2013) PloS ONE. 8(12):e84908. Remington's Pharmaceutical Sciences, 15th or 18th Ed. (Alfonso R. Gennaro, ed.; Mack Publishing Company, Easton, PA, 1990) Remington: the Science and Practice of Pharmacy 19th Ed.(Lippincott, Williams &Wilkins, 1995) Handbook of Pharmaceutical Excipients, 3rd Ed. (Arthur H. Kibbe, ed.; Amer. Pharmaceutical Assoc, 1999) Pharmaceutical Codex: Principles and Practice of Pharmaceutics 12th Ed. (Walter Lund ed.; Pharmaceutical Press, London, 1994) The United States Pharmacopeia: The National Formulary (United States Pharmacopeial Convention) Fiedler’s “Lexikon der Hilfstoffe” 5th Ed., Edition Cantor Verlag Aulendorf 2002 “The Handbook of Pharmaceutical Excipients”, 4th Ed., American Pharmaceuticals Association, 2003 Goodman and Gilman's: the Pharmacological Basis of Therapeutics (Louis S. Goodman and Lee E. Limbird, eds.; McGraw Hill, 1992), Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art to which this invention belongs. All publications and patents specifically mentioned herein are incorporated by reference in their entirety for all purposes in connection with the invention. The present invention is not to be limited in scope by the specific embodiments described herein. Indeed, various modifications of the invention in addition to those described herein will become apparent to those skilled in the art from the foregoing description and accompanying figures. Such modifications are intended to fall within the scope of the appended claims. Moreover, all aspects and embodiments of the invention described herein are considered to be broadly applicable and combinable with any and all other consistent embodiments, including those taken from other aspects of the invention (including in isolation) as appropriate.

Claims

1. A method of selecting candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics, comprising:a) generating single cell data from a plurality of T cells from at least one, and preferably a plurality of, transgenic experimental animals that express humanised TCRs and that have been immunized with an antigen, wherein the single cell data comprises:i) paired TCR sequences;ii) gene expression quantification;iii) antigen binding quantification; and optionallyiv) cell surface protein (e.g., TCR, PD-1 and / or CD45RA / RO) quantification;b) clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters and determining cell type, cell status and TCR binding characteristics from the single cell data; andc) selecting candidate TCRs or cells expressing candidate TCRs with defined characteristics by analysing determined cell type, cell status, TCR binding characteristics and optionally cell surface protein quantification, within each of the plurality of clusters.

2. The method of claim 1, wherein the selecting comprises selecting one or more clusters based on the combined cell type, cell status and TCR binding characteristics of the one or more clusters.

3. The method of claim 1 or claim 2, wherein:a) the determined TCR binding characteristics comprise binding affinity and / or binding specificity;b) the determined cell type comprises naive and / or memory; and / orc) the determined cell status comprises one or more of exhaustion status, cytotoxicity status, proliferation status and TCR copy number / cell surface expression status.

4. The method of any one of the preceding claims, wherein each cluster comprises data on naive cell type, memory cell type, exhaustion status, proliferation status, TCR binding affinity, TCR binding specificity, cytotoxicity status and TCR copy number / cell surface expression status.

5. The method of any one of the preceding claims, wherein clustering the paired TCR sequences on the basis of sequence similarity is based on TCR CDR sequence similarity.

6. The method of any one of the preceding claims, wherein the transgenic experimental animals comprise TCRa and TCRp loci with human variable region gene segments, optionally fully human TCRa and TCRp loci.

7. The method of any one of the preceding claims, wherein the transgenic experimental animals are humanised at one or more, preferably all of the:a) TCRa variable region;b) TCRp variable region;c) region encoding the extracellular domain of co-receptor CD4;d) region encoding the extracellular domain of co-receptor CD8e) region encoding major histocompatibility complex (MHC) class I; andf) region encoding major histocompatibility complex (MHC) class II.

8. The method of any one of the preceding claims, wherein the transgenic experimental animals are mice.

9. The method of any one of the preceding claims, wherein the antigen is a selfantigen, preferably a human self-antigen.

10. The method of any one of the preceding claims, wherein the antigen is a cancer antigen, a tumor associated antigen, a cancer-germline antigen or a tumor specific antigen.

11. A method of selecting candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics, comprising:a) separating a plurality of T cells obtained from at least one, and preferably a plurality of, experimental animals (preferably that are transgenic and express non-native, most preferably humanised TCRs) that have been immunized with a disease-associated antigen into two aliquots;b) using a first aliquot:i. administering the first aliquot to a plurality of experimental animals expressing the disease-associated antigen, preferably wherein the plurality of experimental animals have tumors that display the antigenii. obtaining a plurality of T cells from the animals treated, optionally successfully treated, using the first aliquotiii. generating single cell data from the plurality of T cells (obtained in b) ii.) wherein the single cell data comprises:

1. paired TCR sequences;2. gene expression quantification;3. antigen binding quantification; and optionally4. cell surface protein quantification;iv. clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters and determining cell type, cell status and TCR binding characteristics from the single cell data; andc) using a second aliquot:i. generating single cell data from the plurality of T cells wherein the single cell data comprises:

1. paired TCR sequences;2. gene expression quantification;3. antigen binding quantification; and optionally4. cell surface protein quantification;ii. clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters and determining cell type, cell status and TCR binding characteristics from the single cell data d) identifying candidate TCRs or cells expressing candidate TCRs from the first aliquot and second aliquot with defined characteristics by analysing determined cell type, cell status, TCR binding characteristics and optionally cell surface protein quantification, within each of the plurality of clusters, and analysing changes in the expressed antigen, preferably changes in the tumors expressing the antigen from candidate TCRs or cells expressing candidate TCRs from the first aliquot;e) comparing the identified candidate TCRs or cells expressing candidate TCRs with defined characteristics obtained from each aliquot to select candidate TCRs or cells expressing candidate TCRs.

12. The method of claim 11, wherein candidates that persisted in both aliquots and were able to effect treatment are selected, preferably the candidates expanded from the first aliquot.

13. The method of any one of the preceding claims, wherein the candidate TCRs or T cells expressing candidate TCRs are selected for intermediate to high affinity, high persistence and high expanding potential.

14. A method of producing a T cell receptor (TCR) that binds to an antigen of interest comprising performing the method of any one of claims 1 to 13 and expressing a selected candidate TCR in a cell.

15. A method of producing a T cell receptor (TCR) compound wherein the TCR compound binds to an antigen of interest comprising:a) performing the method of any one of claims 1 to 13 to select a candidate T cell receptor (TCR); andb) producing a TCR compound comprising paired TCR sequences of the selected candidate TCR.

16. The method of claim 15, wherein the paired TCR sequences comprise one or more of, preferably all of, Va CDR1, Va CDR2, Va CDR3, Vp CDR1, Vp CDR2 and Vp CDR3.

17. The method of claim 15 or claim 16, wherein the TCR compound is an antigen binding derivative or fragment of the TCR.

18. A TCR selected according to the method of any one of claims 1 to 14 or TCR compound produced according to the method of any one of claims 15 to 17.

19. An isolated nucleic acid encoding a candidate TCR according to the method of any one of claims 1 to 14 or TCR compound produced according to the method of one of claims 15 to 17.

20. A recombinant vector comprising a nucleic acid of claim 19.

21. A recombinant host cell comprising a TCR selected according to the method of any one of claims 1 to 14, or TCR compound produced according to the method of any one of claims 15 to 17, or a nucleic acid of claim 19, or a vector of claim 20, wherein the host cell is a lymphocyte, T lymphocyte or T lymphocyte progenitor, a CD4 or a CDS positive T-cell.

22. A pharmaceutical composition comprising:a TCR selected according to the method of any one of claims 1 to 14, or TCR compound produced according to the method of any one of claims 15 to 17, or a nucleic acid of claim 19, or a vector of claim 20, or a host cell of claim 21, and a pharmaceutical acceptable carrier, stabilizer and / or excipient.

23. A compound or composition for use in the treatment of a proliferative disorder, preferably cancer, wherein the compound or composition comprises a TCR selected10according to the method of claims 1 to 14, orTCR compound produced according to the method of any one of claims 15 to 17, or a nucleic acid of claim 19, or a vector of claim 20, or a host cell of claim 21, or a pharmaceutical composition of claim 22."Amendments to the claims have been filed as follows."21 05 25Claims1. A method of selecting candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics, comprising:5 a) generating single cell data from a plurality of T cells from at least one or aplurality of transgenic experimental animals that express humanised TCRs and that have been immunized with an antigen, wherein the single cell data comprises:i) paired TCR sequences;10 ii) gene expression quantification; andiii) antigen binding quantification;b) clustering paired TCR sequences on the basis of sequence similarity to form a plurality of clusters and determining cell type, cell status and TCR binding characteristics from the single cell data; and15 c) selecting candidate TCRs or cells expressing candidate TCRs with definedcharacteristics by analysing determined cell type, cell status, TCR binding characteristics, within each of the plurality of clusters.

2. The method of claim 1, wherein the single cell data further comprises:iv) cell surface protein quantification;20 and wherein the method comprises:c) selecting candidate TCRs or cells expressing candidate TCRs with defined characteristics by analysing determined cell type, cell status, TCR binding characteristics and cell surface protein quantification, within each of the plurality of clusters.25 3. The method of claim 2, wherein the quantified cell surface proteins comprise TCR,PD-1 and / or CD45RA / RO.

4. The method of any one of claims 1 to 3, wherein the selecting comprises selecting one or more clusters based on the combined cell type, cell status and TCR binding characteristics of the one or more clusters.30 5. The method of any one of claims 1 to 4, wherein:a) the determined TCR binding characteristics comprise binding affinity and / or binding specificity;b) the determined cell type comprises naive and / or memory; and / or10152025c) the determined cell status comprises one or more of exhaustion status, cytotoxicity status, proliferation status and TCR copy number / cell surface expression status.

6. The method of any one of the preceding claims, wherein each cluster comprises data on naive cell type, memory cell type, exhaustion status, proliferation status, TCR binding affinity, TCR binding specificity, cytotoxicity status and TCR copy number / cell surface expression status.

7. The method of any one of the preceding claims, wherein clustering the paired TCR sequences on the basis of sequence similarity is based on TCR CDR sequence similarity.

8. The method of any one of the preceding claims, wherein the transgenic experimental animals comprise TCRa and TCRp loci with human variable region gene segments.

9. The method of claim 8, wherein the transgenic experimental animals comprise fully human TCRa and TCRp loci.

10. The method of any one of the preceding claims, wherein the transgenic experimental animals are humanised at one or more of the:a) TCRa variable region;b) TCRp variable region;c) region encoding the extracellular domain of co-receptor CD4;d) region encoding the extracellular domain of co-receptor CD8e) region encoding major histocompatibility complex (MHC) class I; andf) region encoding major histocompatibility complex (MHC) class II.

11. The method of any one of the preceding claims, wherein the transgenic experimental animals are mice.

12. The method of any one of the preceding claims, wherein the antigen is a selfantigen.

13. The method of claim 12, wherein the antigen is a human self-antigen.

14. The method of any one of the preceding claims, wherein the antigen is a cancer antigen, a tumor associated antigen, a cancer-germline antigen or a tumor specific antigen.

15. A method of selecting candidate T cell receptors (TCRs) or T cells expressing candidate TCRs with defined characteristics, comprising:a) separating a plurality of T cells obtained from at least one or a plurality of experimental animals that have been immunized with a disease-associated antigen into two aliquots;b) using a first aliquot:5 i. administering the first aliquot to a plurality of experimental animalsexpressing the disease-associated antigen,ii. obtaining a plurality of T cells from the animals treated using the first aliquotill. generating single cell data from the plurality of T cells (obtained in10 b) ii.) wherein the single cell data comprises:

1. paired TCR sequences;2. gene expression quantification; and3. antigen binding quantification;iv. clustering paired TCR sequences on the basis of sequenceI Pl** * 15 similarity to form a plurality of clusters and determining cell type,C\Jcell status and TCR binding characteristics from the single cellLOdata; andc) using a second aliquot:"1“ i. generating single cell data from the plurality of T cells wherein the20 single cell data comprises:

1. paired TCR sequences;2. gene expression quantification; and3. antigen binding quantification;ii. clustering paired TCR sequences on the basis of sequence25 similarity to form a plurality of clusters and determining cell type,cell status and TCR binding characteristics from the single cell datad) identifying candidate TCRs or cells expressing candidate TCRs from the first aliquot and second aliquot with defined characteristics by analysing determined cell type, cell status, TCR binding characteristics, within each of 30 the plurality of clusters, and analysing changes in the expressed antigenfrom candidate TCRs or cells expressing candidate TCRs from the first aliquot;e) comparing the identified candidate TCRs or cells expressing candidate TCRs with defined characteristics obtained from each aliquot to select35 candidate TCRs or cells expressing candidate TCRs.

16. The method of claim 15, wherein the animals are transgenic and express nonnative, humanised TCRs.

17. The method of claim 15 or claim 16, comprising administering the first aliquot to a plurality of experimental animals expressing the disease-associated antigen, wherein the 5 plurality of experimental animals have tumors that display the antigen.

18. The method of any one of claims 15 to 17, comprising obtaining a plurality of T cells from animals successfully treated using the first aliquot.

19. The method of any one of claims 15 to 18, wherein the single cell data generated in step b) ill. and c) i. further comprises:10 4. cell surface protein quantification;and wherein the method comprises:d) identifying candidate TCRs or cells expressing candidate TCRs from the first aliquot and second aliquot with defined characteristics by analysing determined cell type, cell status, TCR binding characteristics and cell surface protein quantification, within each ofI Pl** * 15 the plurality of clusters.C\J20. The method of any one of claims 15 to 19, wherein step d) comprises analysing LO changes in the tumors expressing the antigen from candidate TCRs or cells expressingcandidate TCRs from the first aliquot.1—20 21. The method of any one of claims 15 to 20, wherein candidates that persisted in bothaliquots and were able to effect treatment are selected.

22. The method of claim 21, further wherein candidates expanded from the first aliquot are selected.

23. The method of any one of the preceding claims, wherein the candidate TCRs or T25 cells expressing candidate TCRs are selected for intermediate to high affinity, highpersistence and high expanding potential.

24. The method of any one of claims 1 to 23, further comprising providing an isolated nucleic acid encoding a selected candidate TCR.

25. The method of claim 24, wherein the isolated nucleic acid is a recombinant vector.30 26. The method of claim 25, wherein the recombinant vector is provided in a host cell.

27. The method of any one of claims 24-26, wherein the isolated nucleic acid encodes a TCR compound.

28. The method of any one of claims 1-27, further comprising providing a selected candidate TCR in a pharmaceutical composition together with a pharmaceutical 35 acceptable carrier, stabilizer and / or excipient.

29. A method of producing a T cell receptor (TCR) that binds to an antigen of interest comprising performing the method of any one of claims 1 to 23 and expressing a selected candidate TCR in a cell.

30. The method of claim 29, wherein the selected candidate TCR has been modified5 with one or more amino acid substitutions, deletions, insertions and / or mutations.

31. The method of claim 29 or 30, wherein the cell expressing the selected candidate TCR is for use as an adoptive cell therapy.

32. The method of any one of claims 29 to 31, wherein the selected candidate TCR comprises a human TCR constant domain.10 33. The method of any one of claims 29 to 32, wherein expressing a selected candidateTCR comprises transfecting or transducing cells with a nucleic acid or expression vector that encodes the selected candidate TCR.

34. A method of producing a T cell receptor (TCR) compound wherein the TCR compound binds to an antigen of interest comprising:I Pl^*15 a) performing the method of any one of claims 1 to 23 to select a candidate TC\Jcell receptor (TCR); andLO b) producing a TCR compound comprising paired TCR sequences of theselected candidate TCR.

35. The method of claim 34, wherein the paired TCR sequences comprise one or more, 20 or all of, Va CDR1, Va CDR2, Va CDR3, Vp CDR1, Vp CDR2 and Vp CDR3.

36. The method of claim 34 or claim 35, wherein the TCR compound is an antigen binding derivative or fragment of the TCR.

37. The method of claim 36, wherein the antigen binding derivative is a soluble TCR.

38. The method of claim 37, wherein the soluble TCR further comprises:25 a) a CD3 binding domain and / orb) a half-life extending domain.

39. The method of claim 38, wherein the CD3 binding domain is an scFv and the halflife extending domain is an Fc.

40. The method of claim 36, wherein the antigen binding derivative is a bispecific T cell 30 engager.

41. The method of any one of claims 34 to 40, wherein the selected candidate TCR has been modified with one or more amino acid substitutions, deletions, insertions and / or mutations.

42. The method of any one of claims 34 to 41 comprising performing affinity maturation 35 on the selected candidate TCR to provide a final candidate TCR.

43. The method of any one of claims 34 to 42 wherein producing a TCR compound comprises providing an isolated nucleic acid encoding the TCR compound.

44. The method of claim 43, wherein the nucleic acid is provided in a recombinant vector.5 45. The method of claim 44, wherein the recombinant vector is provided in a host cell.

46. The method of any one of claims 34 to 45, wherein the TCR compound is provided in a pharmaceutical composition together with a pharmaceutical acceptable carrier, stabilizer and / or excipient.1021 05 25Application No: GB2405657.4Claims searched: 1-17, 19-23Examiner:Date of search:Dr Greg Krick-Pridgeon18 September 2024Patents Act 1977: Search Report under Section 17Documents considered to be relevant:Category Relevant to claims Identity of document and passage or figure of particular relevance A - Current Opinion in Biotechnology, vol. 65, 2020, Chiffelle Johanna et al., T-cell repertoire analysis and metrics of diversity and clonality, pages 284-295. See Fig. 2; pg. 286; first para, on pg. 288 A Nature Methods, vol. 18, no. 8, 2021, Pai Joy A. et al., High-throughput and single-cell T cell receptor sequencing technologies, pages 881-892. See Fig. 3-4; Column 2, 3rd para, on pg. 883; first full para, on pg., 887; section ‘Strategies for mapping TCR sequence to antigen specificity on Pg- 888 A - Journal of Immunological Methods, vol. 439, 2016, Sprouse Maran L et al., Rapid identification and expression of human TCRs in retrogenic mice, pages 29-36. See Fig. 1; abstract A - Nature Biotechnology, vol. 40, no. 1, 2021, Schattgen Stefan A. et al., Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA), pages 54-63. See abstract; Fig. 1 A - Cancers, vol. 14, no. 7, 2022, Grunert Corinna et al., Isolation of Neoantigen-Specific Human T Cell Receptors from Different Human and Murine Repertoires., article no. 1842. See summary; abstract; second to last para of introduction A Journal of immunotherapy of cancer, vol. 9, suppl. 2, 2021, Gawovidis et al., 225 Optimal-affinity MAGE-Al-specific T cell receptors (TCRs) generated using the humanized TCR-transgenic mouse platform HuTCR are superior to human donor-derived TCRs See whole document A - Nature medicine, vol. 16, no. 9, 2010, Li et al., Transgenic mice with a diverse human T cell antigen receptor repertoire, p. 1029 See abstractCategories:____________________________________X Document indicating lack of novelty or inventive A stepY Document indicating lack of inventive step if P combined with one or more other documents of same category.Document indicating technological background and / or state of the art.Document published on or after the declared priority date but before the filing date of this invention.& Member of the same patent family E Patent document published on or after, but with priority dateearlier than, the filing date of this application.Field of Search:International Classification:Subclass Subgroup Valid From GOIN 0033 / 53 01 / 01 / 2006 AO IK 0067 / 0278 01 / 01 / 2024 C07K 0014 / 725 01 / 01 / 2006 C12N 0015 / 10 01 / 01 / 2006 C12Q 0001 / 6804 01 / 01 / 2018 C12Q 0001 / 6827 01 / 01 / 2018 GOIN 0033 / 50 01 / 01 / 2006 G16B 0020 / 20 01 / 01 / 2019 G16B 0035 / 20 01 / 01 / 2019

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