MAGE-A10 specific T cell receptor and application thereof in immunotherapy

Abstract

The invention discloses an MAGE-A10 specific T cell receptor (MAGE-A10TCR) and an application of the MAGE-A10 specific T cell receptor in immunotherapy, and belongs to the technical field of tumor immunotherapy. The MAGE-A10TCR is screened from a tumor reactive T cell of an HCC patient, can specifically recognize an MAGE-A10 antigen and is used for constructing the MAGE-A10TCR-T cell, and a cytotoxicity test shows that the MAGE-A10TCR-T cell has a clearing effect on the HCC cell.

Description

Technical Field

[0001] The present application belongs to the technical field of tumor immunotherapy, and specifically relates to MAGE-A10 specific T cell receptor and its application in immunotherapy, especially in immunotherapy of hepatocellular carcinoma. Background Art

[0002] Melanoma-associated antigen family A member 10 (MAGE-A10) is a nuclear protein with a molecular weight of approximately 70 kDa. It plays a role in embryonic development and tumor transformation or tumor progression. It is a cancer-testis antigen (CTA), that is, it is not expressed in normal healthy tissues except testis and placenta.

[0003] The high incidence of hepatocellular carcinoma (HCC) is mainly due to the high prevalence of chronic viral hepatitis caused by hepatitis B virus (HBV) infection. It is one of the leading causes of cancer-related deaths worldwide, and its related treatment methods have attracted much attention. Since HCC is usually diagnosed in the late stage, the treatment of HCC is extremely challenging. In addition to surgery and interventional treatment, identification drugs (including sorafenib and lenvatinib) and immune checkpoint inhibitors (ICIs) (including nivolumab and pembrolizumab) have shown certain efficacy in patients with advanced HCC. In addition, a variety of new treatments and drugs are in clinical trials.

[0004] The immune role of T cells and the potential of HCC-related antigens as therapeutic targets make T cell receptor-engineered T cell (TCR-T) immunotherapy an important possibility. Researchers are gradually advancing TCR-T cell therapy that recognizes alpha-fetoprotein (AFP) and hepatitis B antigens. Due to the inherent properties of T cell receptors (TCRs), TCR-T cells can recognize membrane proteins and intracellular proteins presented by major histocompatibility complexes (MHCs), which greatly enriches the antigen peptide library recognized by TCRs. Through the recognition of specific TCRs, these modified T cells are able to recognize and eliminate tumor cells expressing target antigens. The high specificity of TCR-T cells avoids significant toxicity to normal cells. In addition, TCR-T cells can survive for a long time in patients and maintain their ability to attack tumor cells, thereby providing a lasting therapeutic effect. The molecular structure of TCRs helps to more accurately understand the basic principles of TCR identification. The part of TCR that directly interacts with peptide-major histocompatibility complexes (p-MHCs) is the complementarity determining region (CDR), which includes CDR1, CDR2, and CDR3. CDR1 and CDR2 are encoded by germline DNA, while CDR3 is encoded by V(D)J genes. Therefore, the key to constructing TCR-T cells is a pair of CDR3 sequences on the α and β chains that represent a specific TCR.

[0005] MAGE-A10 was detected in hepatocellular carcinoma (HCC) with a specific expression rate of 36.7% and was correlated with AFP levels. However, there are few reports on TCR-T cells that target (recognize) this antigen. Summary of the invention

[0006] 1. Purpose of the Invention

[0007] The invention of this application aims to provide a MAGE-A10 specific T cell receptor (MAGE-A10 TCR) and its application in immunotherapy, especially immunotherapy of hepatocellular carcinoma. The MAGE-A10 TCR is screened in tumor-reactive T cells of HCC patients, and the TCR that specifically recognizes the MAGE-A10 antigen obtained by single-cell sequencing is used to construct MAGE-A10TCR-T cells. Cytotoxicity tests show that MAGE-A10TCR-T cells have a clearing effect on HCC cells.

[0008] 2. Technical solution

[0009] In order to achieve the above-mentioned invention object, the technical solution adopted in this application is as follows:

[0010] The present application provides a MAGE-A10-specific T cell receptor MAGE-A10 TCR, which includes a TCRα chain and a TCRβ chain, wherein the TCRα chain includes three variable regions: CDR1-α, CDR2-α and CDR3-α, and the TCRβ chain includes three variable regions: CDR1-β, CDR2-β and CDR3-β, wherein: the amino acid sequence of CDR3-α is shown in SEQ ID NO.1; the amino acid sequence of CDR3-β is shown in SEQ ID NO.2; or the amino acid sequence of CDR3-α is shown in SEQ ID NO.12; the amino acid sequence of CDR3-β is shown in SEQ ID NO.13.

[0011] Furthermore, the amino acid sequence of the above CDR3-α is shown in SEQ ID NO.1; the amino acid sequence of CDR3-β is shown in SEQ ID NO.2.

[0012] Furthermore, the amino acid sequence of the above CDR1-α is shown in SEQ ID NO.3; the amino acid sequence of CDR2-α is shown in SEQ ID NO.4.

[0013] Furthermore, the amino acid sequence of the above CDR1-β is shown in SEQ ID NO.5; the amino acid sequence of CDR2-β is shown in SEQ ID NO.6.

[0014] Furthermore, the amino acid sequence of the above-mentioned TCRα chain is shown in SEQ ID NO.7; the amino acid sequence of the TCRβ chain is shown in SEQ ID NO.8.

[0015] The present application also provides a nucleic acid encoding the above-mentioned MAGE-A10-specific T cell receptor MAGE-A10TCR.

[0016] Furthermore, the nucleotide sequence of the above-mentioned nucleic acid includes a nucleotide sequence as shown in SEQ ID NO.9 and a nucleotide sequence as shown in SEQ ID NO.10, the nucleotide sequence shown in SEQ ID NO.9 encodes an amino acid sequence such as the TCRα chain shown in SEQ ID NO.7, and the nucleotide sequence shown in SEQ ID NO.10 encodes an amino acid sequence such as the TCRβ chain shown in SEQ ID NO.8.

[0017] Furthermore, the nucleotide sequence of the above nucleic acid is shown in SEQ ID NO.11, encoding TCRα chain and TCRβ chain.

[0018] The present application also provides a recombinant expression vector, which comprises the above-mentioned nucleic acid encoding MAGE-A10 TCR.

[0019] The present application also provides a host cell, which comprises the above-mentioned nucleic acid encoding MAGE-A10 TCR, or the above-mentioned recombinant expression vector.

[0020] Furthermore, the above host cell is a T cell.

[0021] The present application also provides a MAGE-A10TCR-T cell, which includes the above-mentioned MAGE-A10 TCR and can specifically recognize melanoma-associated antigen family A member 10 (MAGE-A10).

[0022] The present application also provides the use of the above-mentioned MAGE-A10 TCR, nucleic acid encoding MAGE-A10 TCR, recombinant expression vector, host cell and / or MAGE-A10TCR-T cell in the preparation of a drug for treating cancer, wherein the above-mentioned cancer cells express MAGE-A10.

[0023] Furthermore, the above-mentioned cancers include hepatocellular carcinoma, melanoma, lung cancer and / or urothelial carcinoma, etc.

[0024] Furthermore, the above cancer includes hepatocellular carcinoma.

[0025] The present application also provides a pharmaceutical composition, which comprises the above-mentioned MAGE-A10 TCR, a nucleic acid encoding MAGE-A10 TCR, a recombinant expression vector, a host cell and / or MAGE-A10TCR-T cell, and a pharmaceutically acceptable carrier.

[0026] 3. Beneficial effects

[0027] Compared with the prior art, the present application has the following beneficial effects:

[0028] The present application provides a MAGE-A10-specific T cell receptor (MAGE-A10 TCR) and its use in immunotherapy. The MAGE-A10 TCR is derived from tumor-reactive T cells of HCC patients. It is determined that it can specifically recognize MAGE-A10 and is used to construct MAGE-A10 TCR-T cells. Cytotoxicity tests show that MAGE-A10 TCR-T cells have a clearing effect on HCC cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a comparison chart of single-cell sequencing data of hepatocellular carcinoma patients before and after removing the batch effect, where: the left side is before removing the batch effect; the right side is after removing the batch effect.

[0030] Figure 2 A single-cell atlas of a patient with hepatocellular carcinoma, where:

[0031] A is the UMAP map, with different colors representing different cell types;

[0032] B is a bubble plot showing the marker genes of each cell population;

[0033] C is a bar graph showing the percentage of each cell type in each sample, P01N represents the normal tissue sample of patient 01, P01T represents the tumor tissue sample of patient 01, and P02N, P02T, P03N, and P03T are similar;

[0034] D is an alluvial map showing the percentage of each cell type in normal tissue (Normal) and tumor tissue (Tumor);

[0035] E is the UMAP map, with different colors representing different tissue sources;

[0036] F is the number of each cell type between normal tissue and tumor tissue.

[0037] Figure 3 This is the UMAP diagram of the T cell and TCR matching results, where:

[0038] A shows the expression of four marker genes (CD3D, CD3E, CD4, and CD8A) in matched T cells;

[0039] B shows a sample of matched T cells;

[0040] C shows the tissue origin of the matched T cells;

[0041] D shows the frequency of matched T cells.

[0042] Figure 4 CD4 in reactive T cells +A map of T cells, where:

[0043] A is the UMAP graph, different colors represent different CD4 + T cell subtypes;

[0044] B is a bubble plot showing the marker genes of each cell population;

[0045] C is for CD4 + Pseudo-time trajectory of T cells on pseudo-time trajectory;

[0046] DG are UMAP plots showing non-viral reactive (D), amplified (E), tumor-specific (F), and reactive (G) CD4 + T cells.

[0047] Figure 5 CD8 in reactive T cells + A map of T cells, where:

[0048] A is the UMAP map, different colors represent different CD8 + T cell subtypes;

[0049] B is a bubble plot showing the marker genes of each cell population;

[0050] C is CD8 + Pseudo-time trajectory of T cells on pseudo-time trajectory;

[0051] DG are UMAP images showing non-viral reactive (D), amplified (E), tumor-specific (F), and reactive (G) CD8 + T cells.

[0052] Figure 6 is a comparison of reactive T cells and bystander T cells, where:

[0053] In A, the significantly differentially expressed genes of reactive T cells are marked in the enhanced volcano plot relative to bystander T cells, and these genes meet both P < 0.05 and |Log2FC| > 1;

[0054] In B, the gene ontology biological processes and KEGG pathways of differentially expressed genes up-regulated or down-regulated in reactive T cells;

[0055] C and D bar graphs show the proportions of V (C) and J (D) genes in TCRβ chains in bystander T cells and responder T cells, *P < 0.05.

[0056] Figure 7 is the result of in vitro cytotoxicity assay of MAGE-A10 TCR-T cells, where:

[0057] A is the IFN-γ secretion detected by ELISA after MAGE-A10 TCR-T cells were stimulated with MCF-7 cells loaded with different concentrations of control and MAGE-A10 peptides (n=3);

[0058] B is MAGE-A10 + After the interaction between MCF-7 cells and Mock-T and TCR-T cells at different E / T ratios, the secretion of IFN-γ was detected by ELISA ( n = 4);

[0059] C is the lysis rate of tumor cells measured by LDH assay (n=4);

[0060] D is PI staining showing dead tumor cells;

[0061] Mock-T: T cells transfected with helper plasmids without encoding vectors;

[0062] After two-way ANOVA, ***P<0.001, ****P<0.0001. DETAILED DESCRIPTION

[0063] The present application is further described below in conjunction with specific embodiments.

[0064] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the term "and / or" used in this application includes any and all combinations of one or more related listed items.

[0065] If the specific conditions are not specified in the examples, the experiments were carried out under conventional conditions or conditions recommended by the manufacturer. If the manufacturers of the reagents or instruments are not specified, they are all conventional products that can be purchased commercially.

[0066] As used herein, the term "about" is used to provide flexibility and imprecision associated with a given term, measurement or value. The degree of flexibility for a particular variable can be easily determined by one skilled in the art.

[0067] As used in this application, the term "at least one of" is intended to be synonymous with "one or more of." For example, "at least one of A, B, and C" explicitly includes only A, only B, only C, and combinations of each thereof.

[0068] Concentration, amount and other numerical data can be presented in the application in a range format. It should be understood that such a range format is only used for convenience and simplicity, and should be flexibly interpreted as not only including the numerical value clearly described as the range limit, but also including all individual numerical values ​​or sub-ranges included in the range, just as each numerical value and sub-range are clearly described. For example, the numerical range of about 1 to about 4.5 should be interpreted as not only including the limit value of 1 to about 4.5 clearly described, but also including individual numbers (such as 2,3,4) and sub-ranges (such as 1 to 3, 2 to 4, etc.). The same principle is applicable to the scope of only narrating a numerical value, such as "less than about 4.5", which should be interpreted as including all the above-mentioned values ​​and ranges. In addition, no matter how the breadth of the described range or feature is, this explanation should be applicable.

[0069] As used in this application, antigenic epitope, also known as antigenic determinant, refers to a special chemical group in an antigen molecule that determines the antigen specificity.

[0070] In this application, paired samples of hepatocellular carcinoma (HCC) cancer tissue (tumor tissue) and adjacent normal tissue (paracancerous tissue) were obtained from 3 tumors that were resected at the Third Affiliated Hospital of Soochow University from June 2021 to November 2022. Informed consent from all participants and approval from the hospital ethics committee (No. 2023-048) have been obtained. The clinical characteristics of the 3 patients are shown in Table 1:

[0071] Table 1 Clinical characteristics of the three patients

[0072]

[0073] In this application, HEK-293T cells were used for vector packaging and cultured in Dulbecco's Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS) and 1% penicillin / streptomycin (PEST). MCF-7 cells were cultured in DMEM supplemented with 10% FBS, 1% PEST, and 10 μg / mL insulin. HepG2 cells were grown in DMEM supplemented with 10% FBS and 1% PEST. Peripheral blood mononuclear cells (PBMCs) and CD8 + T cells were cultured in RPMI-1640 supplemented with 10% human AB serum, 10 mM HEPES, 2 mM L-glutamine, and 100 IU / mL IL-2. Unless otherwise stated, all cell culture reagents were from Invitrogen. Cells were maintained under standard culture conditions.

[0074] In this application, statistical analysis was performed using R software (version 4.4.1) and GraphPad Prism (version 10.2.3); the comparison between the two groups was performed using a paired t-test, and P < 0.05 was considered statistically significant.

[0075] Example 1

[0076] This example provides the screening of T cell receptors (MAGE-A10TCR) that recognize the MAGE-A10 antigen.

[0077] The screening process for MAGE-A10TCR included:

[0078] Paired tumor tissue and adjacent normal tissue (paracancerous tissue) samples from HCC patients were collected and prepared into single-cell suspensions;

[0079] After the samples passed quality control, scRNA-seq and scTCR-seq were performed respectively;

[0080] The scRNA-seq data were analyzed using the R software package Seurat, retaining cells with UMI counts <15,000 and mitochondrial gene counts <20%, and using DoubletFinder to remove doublet cells, followed by the Harmony package to eliminate batch effects. The analysis results showed that a total of 51,734 cells were obtained, which were divided into 8 cell clusters through UMAP dimensionality reduction and clustering, including immune-related cells (natural killer cells, T cells, B cells, plasma cells, and myeloid cells) and non-immune cells (hepatocytes, endothelial cells, and fibroblasts). Further analysis showed that T cells were the most important cell type in the samples, and the infiltration levels of T cells, hepatocytes, B cells, and plasma cells in tumor samples were significantly higher than those in normal tissues, reflecting the high infiltration characteristics of T cells in the tumor microenvironment and their dominant role in the immune response.

[0081] After extracting all T cells from the single-cell atlas, the scRNA-seq data of T cells were matched with the scTCR-seq data using the scRepertoire software package, obtaining 11,429 matched T cells; T cells were identified by the expression of CD3D and CD3E, and were divided into CD4+ and CD8+ cells based on the expression of CD4+ and CD8A+. + and CD8 + The results showed that there was a clear boundary between the two subgroups; further analysis showed that CD8 + T cells are the main population of clonal expansion, and most CD4 + T cells are monoclonal, and CD8 +T cells expanded significantly, indicating that they were activated to fight tumor cells and maintain long-term immune protection;

[0082] Since shared TCRs were absent in tumor samples from three patients, T cells were divided into CD4 + and CD8 + The reactive T cells were identified based on three conditions: non-viral reactivity, clonal expansion, and tumor specificity. The analysis results showed that the reactive T cells were mainly enriched in CD4_CD69_Th, CD4_FOXP3_Treg, CD4_CXCL13_Treg, and CD4_CD69_Th. EX and CD8_CXCL13_T EX ; There are obvious differences between reactive T cells and bystander T cells. Differential gene analysis showed that key genes such as GNG8 and CCR8 were significantly upregulated in reactive T cells, which are related to T cell function enhancement and tumor immunotherapy; GO and KEGG pathway enrichment analysis further showed that the upregulated genes of reactive T cells are mainly involved in T cell proliferation, regulation and cytokine-cytokine receptor interaction, showing their functional characteristics related to tumor control and elimination; In addition, there are significant differences in TCR diversity between reactive T cells and bystander T cells;

[0083] In order to determine the possibility of reactive T cells recognizing shared p-MHCs, GLIPH2 was used to cluster TCRs and 12 catalogs were obtained, containing a total of 31 CDR3 sequences. The results showed that the most frequent sequence was CDR3b10, followed by CDR3b27. Subsequently, the TCRβCDR3 sequences were matched to cell types and phenotypes were verified. Due to the immunosuppressive effect of Tregs, 10 CDR3 sequences located on Tregs were excluded, and the candidate TCRs were finally narrowed down to 21 sequences, which were called reactive TCRs.

[0084] The TCRMatch tool was used to perform epitope prediction and validation on 21 TCRs to explore their potential to recognize HCC-related antigens; the analysis showed that CDR3b12 shared in catalogs 6 and 11 was predicted to be able to recognize MAGE-A10 through the "GLYDGMEHL" epitope, which is a tumor-specific antigen expressed in HCC and many other tumors (such as melanoma, lung cancer, and urothelial carcinoma). Due to its high immunogenicity, it has become a potential therapeutic target for HCC and many other tumors.

[0085] Specifically include:

[0086] (1) Sample collection and processing

[0087] Paired tumor tissue and adjacent normal tissue (paracancerous tissue) samples were collected from 3 HCC patients, and the collected tissue samples were prepared into single cell suspensions.

[0088] (2) scRNA-seq and scTCR-seq

[0089] After the activity of the single cell suspension was determined, single-cell RNA sequencing (scRNA-seq) and TCR sequencing (scTCR-seq) were performed on the 10X Genomics (https: / / www.10xgenomics.com) platform to obtain scRNA-seq data and scTCR-seq data.

[0090] (3) Processing scRNA-seq data

[0091] The data processed by CellRanger were imported into R software, and the scRNA-seq data were subjected to standard analysis using the R software package Seurat (version 5.1.0). Cells with <15,000 unique molecular identifier (UMI) counts and <20% mitochondrial gene counts were retained from the analysis. Doublets were removed based on 20% parameter identification using the R software package DoubletFinder (version 2.0.4) for subsequent quality control and normalization. Batch effects were eliminated using the Harmony package (version 1.2.0) based on default parameters. The results of eliminating batch effects are shown in Figure 2. Figure 1 shown.

[0092] (4) Drawing single-cell maps

[0093] Based on the results of scRNA-seq data analysis, a total of 51,734 cells were obtained. Combined with established marker genes, dimensionality reduction and clustering were performed. Uniform manifold approximation and projection (UMAP) was used to visualize the cell clusters and draw a single-cell atlas.

[0094] The results are as follows Figure 2 As shown in A and B, there are a total of 8 cell clusters, including immune clusters composed of natural killer cells (NK cells), T cells (T cells), B cells (B cells), plasma cells (Plasma cells) and myeloid cells (Myeloid cells), which play a dominant role in immune responses; and non-immune clusters composed of hepatocytes (Hepatocytes), endothelial cells (Endothelial cells) and fibroblasts (Fibroblasts);

[0095] Analyzing the composition of cells in each sample, T cells were the most abundant type in all five samples and also made up a large portion of sample "P03N" ( Figure 2 Middle C);

[0096] Analysis of cell types in tissues showed that T cells, hepatocytes, plasma cells, and fibroblasts were more common in tumors, while the proportion of myeloid cells and NK cells decreased in tumors ( Figure 2 Middle D);

[0097] The changes in the number of cells in each cluster were studied and it was found that the cells of tumor tissues were mostly concentrated in T cells, hepatocytes and plasma cells ( Figure 2 Middle E);

[0098] Compared with normal tissues, tumor tissues have relatively more T cells, hepatocytes, B cells, and plasma cells ( Figure 2 Middle (F), showing the characteristics of tumor samples and the high level of T cell infiltration in the tumor microenvironment (TME).

[0099] (5) Extracting T cells and matching them with scTCR-seq data

[0100] All T cells were extracted based on the single-cell profile;

[0101] After ensuring the consistency of barcodes, the combineExpression function of the scRepertoire software package (version 2.0.5) was used to match the scTCR-seq data with the scRNA-seq data of T cells based on default parameters, and cells lacking clonal information were filtered out to obtain 11,429 matched T cells;

[0102] T cells were identified by the expression of CD3D and CD3E, and the expression of CD4 and CD8A represented CD4 + and CD8 + Subgroups ( Figure 3 Middle A); CD4 + and CD8 + There is a clear boundary between the two subpopulations; T cells are evenly distributed in all samples ( Figure 3 However, these two subsets showed preferences in different tissue sources, with CD8 + T cells are dominant, while CD4 + T cells ( Figure 3 Middle C); CD8 + T cells are the main cells of clonal expansion, and most CD4 + T cells are monoclonal ( Figure 3 (middle D).

[0103] from Figure 3 It can be seen that most CD8 + The T cells expanded, indicating that they were activated to resist damage from tumor cells and ensure long-term protective immunity. Unfortunately, we were unable to identify shared TCRs in the tumor samples from the three patients; therefore, the inventors took an alternative approach and separated the T cells into two subsets, identifying CD4 + T cells and CD8 + Responsive T cells were isolated from T cells and TCRs that could recognize HCC-related antigens were subsequently identified to construct TCR-T cells.

[0104] (6) CD4 + T cells and CD8 + Identification of Responsive T Cells in T Cells

[0105] (a) CD4 + Identification of Responsive T Cells in T Cells

[0106] Reference to established marker genes, 7,094 CD4 + T cells were clustered into five types, including helper (CD4_c0_CD69_Th), effector memory (CD4_c1_ANXA1_T EM ), regulatory (CD4_c2_FOXP3_Treg), naive (CD4_c3_SELL_T N ) and depletion (CD4_c4_CXCL13_T EX )T cells( Figure 4 A and B).

[0107] CD4 + T cell trajectory analysis: The R package Monocle2 (version 2.32.0) was used to analyze scRNA-seq data, and genes expressed in <10 cells were filtered out. The DDRTree method was used for dimensionality reduction to obtain and visualize the differentiation process of two T cell subsets, which mainly differentiated into three branches: Th, T EM and Treg( Figure 4 Middle C).

[0108] Reactive CD4 + T cell recognition:

[0109] The following three conditions were set to identify reactive CD4 + T cells, specifically:

[0110] (i) Non-viral reactivity: All β-chain CDR3 amino acid sequences corresponding to viral epitopes were obtained from IEDB (Immune Epitope Database and Analysis Resource) and compared with CD4 + Compared with the β-chain CDR3 of T cells, T cells with inconsistent sequences cannot recognize known viruses ( Figure 4 Middle D); however, they have the potential to specifically recognize tumor antigens, thus, retaining these T cells as non-viral reactive cells;

[0111] (ii) Expansion: CD4 clones with a size greater than 1 + T cells were considered to have completed clonal expansion and were therefore selected into the expansion cell ( Figure 4 Middle E), indicating resistance to tumor antigens;

[0112] (iii) Tumor specificity: CD4 + T cells were sorted into tumor-specific populations ( Figure 4 Middle F);

[0113] The intersection of cells that met the 3 criteria was considered tumor-reactive CD4 + T cells ( Figure 4 G), while the rest of the cells are considered bystanders and do not contribute significantly to tumor clearance.

[0114] Binding to CD4 + T cell atlas found that CD4 + Reactive T cells in T cells were mainly enriched in CD4_CD69_Th, CD4_FOXP3_Treg and CD4_CXCL13_Treg. EX , two of which are CD4 + The major fates of T cells.

[0115] (b) CD8 + Identification of Responsive T Cells in T Cells

[0116] Reference CD4 + Identification of reactive T cells in T cells, 4,335 CD8 + T cells were clustered into 6 types, including effector memory (CD8_c0_CXCR4_T EM ), stress response (CD8_c1_HSPA1A_T STR ), depletion (CD8_c2_CXCL13_T EX), cytotoxicity (CD8_c3_GNLY_CTL), mucosal-associated invariance (CD8_c4_KLRB1_MAIT), and effector (CD8_c5_KLRD1_T EFF )T cells( Figure 5 In addition, CD8 + T cells also have T EM and T EX Two phenotypes. Trajectory analysis showed that CD8 + T cells mainly differentiate into T EM , T STR and T EX Branches Figure 5 C) with defined reactive CD4 + Same criteria for T cells, non-viral reactive, expanded, and tumor-specific CD8 + T cells are considered reactive cells ( Figure 5 (middle D~F).

[0117] Binding to CD8 + T cell profile, CD8 + Reactive T cells in T cells are mainly enriched in CD8_CXCL13_T EX ( Figure 5 Middle G), which is one of the cell fates.

[0118] In summary, reactive T cells are mainly enriched in CD4_CD69_Th, CD4_FOXP3_Treg, CD4_CXCL13_T EX and CD8_CXCL13_T EX .

[0119] (7) Differences between reactive T cells and bystander T cells

[0120] The inventors identified 1,673 reactive T cells and 9,756 bystander T cells.

[0121] (a) Differences in gene expression between reactive T cells and bystander T cells

[0122] To test the rationality of the reactive T cells defined according to our principles, the differentially expressed genes of reactive T cells relative to bystanders were analyzed using the DESeq2 software package (version 1.44.0), and enhanced volcano plots were created using SRplot (http: / / www.bioinformatics.com.cn / SRplot). Figure 6Figure 5 (A). The corrected P value was calculated using the Benjamini and Hochberg (BH) method, and differences were considered statistically significant when <0.05. Among the significantly upregulated genes, GNG8, which encodes a G protein subunit, has been reported to be involved in regulating intracellular signaling pathways and is an active factor in immunotherapy for liver cancer. In addition, CCR8 is known to be expressed in a variety of immune cells and mainly regulates the migration and activation of T cells. Reactive T cells showed enhanced function compared to bystanders, as confirmed by the upregulation of key genes such as GNG8 and CCR8.

[0123] The inventors also performed GO and KEGG pathway enrichment analysis, using ClusterProfile (version 4.12.3) to perform gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis on differentially expressed genes, and using SRplot to create enrichment bar graphs ( Figure 6 Middle B). In the pathways of the reactive group, the upregulated genes mainly play an active role in the proliferation and regulation of T cells and cytokine-cytokine receptor interaction. In summary, reactive T cells show gene signatures related to the control and elimination of tumor cells, indicating the potential to promote immunotherapy of HCC.

[0124] (b) Differences in TCR diversity between reactive T cells and bystander T cells

[0125] To explore the diversity of TCRs in reactive T cells and bystander T cells, the usage of V and J genes was compared between the two groups.

[0126] A total of 44 different V genes were counted; however, the responding T cells lacked TRBV16 ( Figure 6 (C) The gene with the highest proportion in both groups was TRBV20-1. TRBV6-2 and TRBV14 were significantly reduced in the reactive T cell group, and TRBV15 showed the greatest difference between the two groups.

[0127] Among the J genes, TRBJ2-7 had the highest proportion in bystander T cells. TRBJ2-1 was the gene with the highest proportion in reactive T cells and the gene with the greatest difference between the two groups ( Figure 6 (middle D).

[0128] The TCR repertoire is a diverse collection of TCRs that determines the ability of T cells to recognize different antigens. Therefore, our analysis of TCR diversity is beneficial for monitoring the immune response to tumors, which can provide some insights for developing personalized immunotherapy.

[0129] (8) Recognition of reactive TCRs via GLIPH2

[0130] To determine the potential of reactive T cells to recognize shared p-MHCs, we utilized GLIPH2 to provide significant TCRs based on global similarity and local motifs. The web tool GLIPH2 (Grouping of Lymphocyte Interactionsby Paratope hotspot version 2, http: / / 50.255.35.37:8080 / ) can analyze very similar TCRs or TCRs that share CDR3 motifs to determine the potential of TCRs to recognize shared p-MHCs. GLIPH2 clusters TCRs based on global similarity and local motifs and identifies significant clusters.

[0131] According to CD4 + and CD8 + The above annotations on T cells ( Figure 4 Middle A and Figure 5 In A), the TCRβ CDR3 sequence was matched with the cell type for phenotype verification, as shown in Table 2. Among them, the sequence "CASSQTRNSYNEQFF" has two IDs (CDR3b05 and CDR3b06) because they correspond to different TCRα CDR3 sequences.

[0132] Table 2

[0133]

[0134]

[0135] The list contained 12 catalogs and 31 CDR3 sequences, and theoretically each catalog could recognize the same p-MHCs. Catalogs 1, 3, and 4 contained the same CDR3, as did catalogs 6 and 11, albeit in different patterns. The most frequent sequence was CDR3b10, followed by CDR3b27. Ten CDR3 sequences located on Tregs were excluded because Tregs suppress immune responses. Therefore, the range of candidate TCRs was narrowed to 21 sequences, called reactive TCRs.

[0136] (9) TCRMatch predicted that MAGE-A10 is a potential therapeutic target

[0137] To investigate the antigens recognized by the 21 reactive TCRs, epitope predictions were validated for each TCR using the IEDB analysis tool TCRMatch to identify potential epitopes that T cells can recognize. The web tool TCRMatch (http: / / tools.iedb.org / tcrmatch / ) can score the similarity of the selected β-chain CDR3 and the Immune Epitope Database (IEDB) (https: / / www.iedb.org / home_v3.php) through a comprehensive k-mer comparison. The epitopes recognized by CDR3 in IEDB are known, so matching T cell antigen epitopes can be provided based on similarity. Enter the CDR3 sequence, choose to trim the conserved C / F+W motif, and set the threshold to 0.90 before starting the analysis.

[0138] After removing viral and non-human antigens from the result list, it was found that the most common CDR3b10 in catalog 5 had no predicted matching epitope. However, CDR3b12 shared by catalogs 6 and 11 was predicted to recognize MAGE-A10 through the "GLYDGMEHL" epitope (Table 3). MAGE-A10 is a shared tumor-specific antigen; in addition to being detected in HCC, it is often highly expressed in other tumors, including melanoma, lung cancer, and urothelial carcinoma. In addition, MAGE-A10 has high immunogenicity, making it a potential therapeutic target for HCC and even a variety of tumors.

[0139] Table 3

[0140]

[0141]

[0142] Example 2

[0143] This example provides verification of the affinity of the MAGE-A10 antigen epitope "GLYDGMEHL" to the MHC molecule.

[0144] MAGE-A10 protein is mainly located in the nucleoplasm. The nuclear protein can be directly processed by the proteasome in the nucleoplasm and presented to the MHC-I molecule. Considering that HLA-A*02:01 is the most common HLA subtype in the Chinese population, it is necessary to verify the affinity of the antigen epitope and MHC molecule, which will have a significant impact on antigen presentation.

[0145] Predicting the affinity of MAGE-A10 to HLA-A*02:01 involves first entering the complete sequence of MAGE-A10 in the initial box of the web utility MHC-I binding predictions (version 2.24), and then selecting the recommended NetMHCpanBA4.1 method. Considering that the most common human leukocyte antigen (HLA) subtype in the Chinese population is HLA-A*02:01, the MHC allele was set to HLA-A*02:01. Finally, the expected peptides were set to contain 9 and 10 amino acids, respectively.

[0146] The full-length amino acid sequence of MAGE-A10 was obtained in TANTiGEN 2.0 (http: / / projects.met-hilab.org / tadb / cgi / displayAntigen.pl?ACC=Ag000032):

[0147] MPRAPKRQRCMPEEDLQSQSETQGLEGAQAPLAVEEDASSSTSTSSSFPSSFPSSSSSSSCYPLIPSTPEEVSADDETPNPPQSAQIACSSPSVVASLPLDQSDEGSSSQKEESPSTLQVLPDSESLPRSEIDEKVTDLVQFLLFKYQMKEPITKAEILESVIKNYEDHFPLLFSEASECMLL VFGIDVKEVDPTGHSFVLVTSLGLTYDGMLSDVQSMPKTGILILILSIIIFIEGYCTPEEVIWEALNMMGLYDGMEHLIYGEPRKLLTQDWVQENYLEYRQVPGSDPARYEFLWGPRAHAEIRKMSLLKFLAKVNGSDPRSFPLWYEEALKDEEERAQDRIATTDDTTAMASASSSATGSFSYPE.

[0148] Peptides predicted by NetMHCpan according to IC 50 Sort from low to high, IC 50Peptides <50nM were considered to have high affinity, and based on this principle, the top 6 peptides in the total table were determined (Table 4). In addition, Mp02 was also observed in the TCRMatch results (Table 3), indicating that this peptide or the entire MAGE-A10 antigen can be a therapeutic target. The T2 cell-peptide binding assay in "Identification of a new MAGE-A10 antigenic peptide presented by HLA-A*0201 on tumor cells" has confirmed that the HLA-A*02:01 molecule has binding affinity to the epitopes Mp01 and Mp02.

[0149] Table 4

[0150]

[0151]

[0152] Example 3

[0153] This example provides verification of the affinity of the MAGE-A10 antigen epitope "GLYDGMEHL" to TCR.

[0154] In this example, the affinity of TCR to antigen epitope was verified by DLpTCR. DLpTCR (http: / / jianglab.org.cn / DLpTCR / ) is a multimodal integrated deep learning framework with high predictive performance for the interaction between TCR and epitope. Paired CDR3 and Mp02 files were submitted and the analysis was started in three different ways using DLpTCR.

[0155] High affinity between antigen epitopes and MHC molecules is not enough. Therefore, affinity validation between TCR and antigen epitopes must be performed to ensure successful recognition and initiation of immune responses in vivo. CDR3b27 and CDR3b28 can also recognize MAGE-A10 because they belong to the same catalog as CDR3b1211, although they were not predicted to have this property. Therefore, these three CDR3s are all within the scope of affinity validation. DLpTCR can be used to detect the interaction between TCR and epitopes. In addition, the prediction results show the interaction between single and double chains of TCR and Mp02 (Table 5). CDR3b12 can bind to Mp02, which is consistent with the results of TCRMatch; however, its corresponding α chain CDR3 cannot bind. Fortunately, the other two pairs of TCR chains can bind to Mp02, indicating the reliability of GLIPH2 analysis. On this basis, because CDR3b27 ranks second in frequency (Table 2), it and CDR3a27 are preferred candidate TCRs.

[0156] Table 5

[0157]

[0158] Example 4

[0159] This example provides the preparation of MAGE-A10TCR-T cells.

[0160] In this embodiment, the amino acid sequence or nucleotide sequence involved is shown in Table 6, CDR1-α, CDR2-α and CDR3-α are three variable regions of TCRα chain, and their amino acid sequences are shown in SEQ ID NO.3, SEQ ID NO.4 and SEQ ID NO.1 respectively; CDR1-β, CDR2-β and CDR3-β are three variable regions of TCRβ chain, and their amino acid sequences are shown in SEQ ID NO.5, SEQ ID NO.6 and SEQ ID NO.2 respectively. The amino acid sequences of TCRα chain and TCRβ chain are shown in SEQ ID NO.7 and SEQ ID NO.8 respectively; the nucleotide sequences of their encoding nucleic acids are shown in SEQ ID NO.9 and SEQ ID NO.10.

[0161] Table 6

[0162]

[0163]

[0164]

[0165] The preparation of MAGE-A10 TCR-T cells includes the following steps:

[0166] (1) Generation of TCR genes and recombinant lentivirus

[0167] The nucleotide sequence of the TCR gene is shown in SEQ ID NO. 11, including the nucleotide sequence encoding the TCRα chain and the TCRβ chain and the nucleotide sequence encoding the P2A sequence. During the translation process, the TCRα chain and the TCRβ chain are obtained through the ribosome jumping mechanism. The sequence was synthesized by Shanghai Genetech Co., Ltd. (GENE);

[0168] The TCR gene was cloned into the vector. The element sequence of the complete recombinant lentiviral vector plasmid was: pRRLSIN-cPPT-SFFV-TCRα-P2A-TCRβ-E2A-EGFP-SV40-puromycin; TCRα-P2A-TCRβ was the TCR gene;

[0169] HEK-293T cells were selected as the packaging cell line, and the recombinant lentiviral vector plasmid and auxiliary plasmids (gag-pol plasmid (225961, Addgene) and VSV-G plasmid (11912, Addgene)) were co-transfected into HEK-293T cells using a liposome transfection reagent;

[0170] The supernatant was collected 48 hours after transfection, centrifuged and filtered in sequence to remove cell debris and impurities, and then ultracentrifuged at 25,000 rpm for 2 hours at 4°C; after centrifugation, the supernatant was discarded, and the obtained vesicular stomatitis virus (VSV)-G pseudotyped lentiviral particles (containing the MAGE-A10-TCR gene) were resuspended in PBS and stored at -80°C for future use.

[0171] (2) Generation of MAGE-A10-TCR engineered T cells (MAGE-A10TCR-T)

[0172] Peripheral blood mononuclear cells (PBMCs) from HLA-A*02:01-positive healthy donors were obtained using Ficoll-Paque, and then PBMCs were activated for 48 h in the presence of 100 ng / mL OKT-3 antibody and 100 IU / mL IL-2. CD8 + CD8 T cell isolation kit (11348D, Invitrogen) was used to isolate CD8 T cells from PBMCs. + Subsequently, 1 million CD8 T cells were transduced with 50 μL of concentrated MAGE-A10-TCR lentivirus. + T cells were transduced for 4 hours. The obtained T cells were transduced again 24 hours later. After screening, T cells were amplified according to the rapid expansion protocol in "Ex vivo stimulation of cyto-megalovirus (CMV)-specific T cells using CMV pp65-modified dendritic cells as stimulators".

[0173] Example 5

[0174] This example provides verification of the function of MAGE-A10TCR-T cells.

[0175] Although we observed the affinity of Mp02 to the candidate TCR through DLpTCR, the actual affinity between MAGE-A10TCR-T cells and MAGE-A10 still needs further exploration.

[0176] This example verifies the actual affinity between MAGE-A10TCR-T cells and MAGE-A10 through cell experiments.

[0177] In this example, the cell lines used include:

[0178] TCR-T cells: engineered T cells expressing MAGE-A10-specific TCR, i.e., MAGE-A10TCR-T cells;

[0179] Mock-T cells: T cells transduced with helper plasmids (gag-pol plasmid and VSV-G plasmid) that do not contain the MAGE-A10-TCR gene;

[0180] MCF-7 cells: human breast cancer cell line, does not express MAGE-A10;

[0181] MAGE-A10 + MCF-7 cells: MCF-7 cells stably expressing MAGE-A10 were prepared by referring to the preparation of MAGE-A10TCR-T cells, specifically: the MAGE-A10 gene was synthesized into the lentiviral vector plvx (125839, Addgene); HEK-293T cells were then co-transfected with the auxiliary plasmids PxpAx2 (12260, Addgene) and PMD2g (12259, Addgene); the produced lentivirus was transduced into MCF-7 cells to obtain a cell line stably transduced with the MAGE-A10 gene, and the stable expression of the gene MAGE-A10 was verified by PCR and Western blot methods;

[0182] MAGE-A10 + HepG2 cells: HepG2 cells stably expressing MAGE-A10 were prepared by referring to the preparation of MAGE-A10TCR-T cells, specifically: the MAGE-A10 gene was synthesized into the lentiviral vector plvx; HEK-293T cells were then co-transfected with the auxiliary plasmids PxpAx2 and PMD2g; the produced lentivirus was transduced into HepG2 cells to obtain a cell line stably transduced with the MAGE-A10 gene, and the stable expression of the gene MAGE-A10 was verified by PCR and Western blot methods.

[0183] In this example, peptide loading of MCF-7 cells includes:

[0184] Preparation of peptide solution: MAGE-A10 peptide or MAGE-B6 peptide (control peptide) was dissolved in dimethylsulfoxide (DMSO) to prepare peptide solutions of different concentrations (0.001 μM, 0.01 μM, 0.1 μM, 1 μM, 10 μM, 100 μM, respectively);

[0185] Peptide loading: MCF-7 cells were seeded in 6-well plates at a density of 1×10 6 cells / well; different concentrations of MAGE-A10 peptide or control peptide solution were added and incubated in an incubator for 2 hours; after incubation, the cells were washed 3 times with PBS to remove unbound peptide.

[0186] (1) Ability of TCR-T cells to specifically recognize MAGE-A10

[0187] (a) IFN-γ secretion to detect the ability of TCR-T cells to specifically recognize MAGE-A10

[0188] T cells were co-incubated with tumor cells, and the supernatant was collected overnight. The secretion of IFN-γ in the supernatant was detected using an enzyme-linked immunosorbent assay (ELISA) kit (EHIFNG, Invitrogen) according to the manufacturer's instructions, specifically:

[0189] TCR-T cells and peptide-loaded MCF-7 cells were co-cultured in a 96-well plate at a ratio of 1:1, placed in an incubator overnight, and the supernatant was collected; the required ELISA plate was taken out, and 50 μL of standard preparation solution or supernatant was added to each well; 50 μL of biotinylated antibody reagent was added to each well, the reaction well was sealed with a sealing film, and incubated at room temperature for 2 hours; the plate was washed 3 times, 100 μL of Streptavidin-HRP solution was added to each well, the reaction well was sealed with a sealing film, and incubated at room temperature for 30 minutes; the plate was washed 3 times, 100 μL of color developer TMB solution was added to each well, the reaction well was sealed with a sealing film, and incubated at room temperature in the dark for 30 minutes; 100 μL of stop solution was added to each well, mixed, and the absorbance value was measured at 450 nm using an enzyme reader.

[0190] Result analysis:

[0191] MCF-7 cells are breast cancer cells that do not express MAGE-A family proteins. MCF-7 cells were loaded with MAGE-A10 and co-cultured with TCR-T cells. As the concentration of MAGE-A10 peptide increased, the secretion of IFN-γ measured by ELISA also increased ( Figure 7 (A), indicating that TCR-T cells successfully recognized MAGE-A10 in vitro.

[0192] (b) IFN-γ secretion assay to detect the ability of TCR-T cells to specifically recognize MAGE-A10 compared with ordinary T cells

[0193] TCR-T cells or Mock-T cells were incubated with MAGE-A10 + MCF-7 cells were co-cultured in a 96-well plate at a ratio of 1:1, placed in an incubator overnight, and the supernatant was collected; the required ELISA plate was taken out, and 50 μL of standard preparation solution or supernatant was added to each well; 50 μL of biotinylated antibody reagent was added to each well, the reaction well was sealed with a sealing film, and incubated at room temperature for 2 hours; the plate was washed 3 times, 100 μL of Streptavidin-HRP solution was added to each well, the reaction well was sealed with a sealing film, and incubated at room temperature for 30 minutes; the plate was washed 3 times, 100 μL of color developer TMB solution was added to each well, the reaction well was sealed with a sealing film, and incubated at room temperature in the dark for 30 minutes; 100 μL of stop solution was added to each well, mixed, and the absorbance value was measured at 450 nm using an enzyme reader.

[0194] Result analysis:

[0195] Compared with Mock-T cells, TCR-T cells showed a significant increase in the expression of MAGE-A10 + The effect of MCF-7 cells was significantly enhanced ( Figure 7 (middle B).

[0196] (2) Study on the difference in the effects of MAGE-A10TCR-T cells and ordinary T cells.

[0197] MAGE-A10TCR-T cells were co-cultured with tumor cells at a specified effector-to-target (E / T) ratio overnight. Lactate dehydrogenase (LDH) was released when cells were damaged or dead. The activity of LDH was detected according to the manufacturer's instructions to reflect the lysis of tumor cells. At the same time, propidium iodide (PI) was used to stain dead tumor cells. Specifically, it includes:

[0198] (a) Lactate dehydrogenase (LDH) release assay

[0199] Refer to the instructions of the lactate dehydrogenase cytotoxicity detection kit (C0016, Beyotime) to detect cytotoxicity:

[0200] Divide the 96-well plate into the following groups: culture medium wells without cells (background blank control wells), untreated control cell wells (sample control wells), untreated cell wells for subsequent lysis (sample maximum enzyme activity control wells), and treated cell wells (sample wells), and mark them;

[0201] Except for the "background blank control well", 1 × 10 4 MAGE-A10 - MCF-7 cells, MAGE-A10 + MCF-7 cells or MAGE-A10 + HepG2 cells, then add an equal amount of culture medium to the “background blank control well”;

[0202] TCR-T cells and tumor cells were added to the “sample wells” at the specified E / T ratios (0.125:1, 0.25:1, 0.5:1, 1:1, and 2:1, respectively), and an equal amount of culture medium was added to the remaining labeled wells and placed in an incubator overnight;

[0203] One hour before the scheduled detection time, add the LDH release reagent provided by the kit to the "sample maximum enzyme activity control well" at an amount of 10% of the original culture volume. Repeatedly pipette several times to mix, and then continue to incubate in the incubator;

[0204] After the predetermined time, the cell culture plate was centrifuged at 400 × g for 5 min using a multiwell plate centrifuge;

[0205] Take the supernatant from each well and add it to the corresponding wells of a new 96-well plate; add LDH detection working solution, mix well, and incubate at room temperature in the dark for 30 minutes;

[0206] The absorbance value was measured at 490 nm using an enzyme reader.

[0207] Calculation (the absorbance of each group measured should be subtracted from the absorbance of the background blank control well):

[0208] Lysis rate = (absorbance of sample well - absorbance of sample control well) / (absorbance of sample maximum enzyme activity control well - absorbance of sample control well) × 100%.

[0209] (b) Hoechst 33342 / PI staining

[0210] Staining was performed according to the instructions of the Hoechst 33342 / PI double staining kit (40744ES60, Yeasen):

[0211] Integrating TCR-T cells with MAGE-A10 - MCF-7 cells, MAGE-A10 + MCF-7 cells or MAGE-A10 + HepG2 cells were co-cultured at the specified E / T ratio (1:1) and placed in an incubator overnight;

[0212] Carefully remove the original cell culture medium, add PBS to carefully wash the cells once, add cell staining buffer, Hoechst33342 staining solution and PI staining solution, and incubate in an incubator away from light for 20 minutes;

[0213] After staining, add PBS and carefully wash the cells once, and observe the cells under a fluorescence microscope.

[0214] Result analysis:

[0215] The lysis of tumor cells by TCR-T cells was evaluated indirectly and directly by LDH test and PI staining. TCR-T cells could hardly recognize MCF-7 ( Figure 7 (C and D) Compared with MCF-7, TCR-T cells showed a significant difference in expression of MAGE-A10 + MCF-7 showed stronger cytotoxicity, indicating that there is specific recognition between TCR-T cells and MAGE-A10 ( Figure 7 In addition, TCR-T cells respond to MAGE-A10 + HepG2 had the highest lysis rate ( Figure 7 C), resulting in the death of about half of the tumor cells, which is higher than MAGE-A10 + MCF-7( Figure 7 (D), which may be due to the additive effect of TCR-T cells on the recognition of AFP expressed by HepG2.

[0216] In summary, TCR-T cells can specifically recognize and eliminate MAGE-A10-positive tumor cells. In addition, when facing HCC cells, it can produce stronger lethality by recognizing other HCC-related antigens. This powerful and precise cytotoxic ability of MAGE-A10 TCR-T cells against HCC cells is expected to be incorporated into new treatment strategies for HCC.

Claims

1. A MAGE-A10 specific T cell receptor MAGE-A10 TCR, characterized in that: The MAGE-A10 TCR comprises a TCR α chain and a TCR β chain, wherein the TCR α chain comprises three variable regions: CDR1-α, CDR2-α and CDR3-α, and the TCR β chain comprises three variable regions: CDR1-β, CDR2-β and CDR3-β, wherein: The amino acid sequence of CDR3-α is shown in SEQ ID NO.1, and the amino acid sequence of CDR3-β is shown in SEQ ID NO.2; or The amino acid sequence of CDR3-α is shown in SEQ ID NO.12, and the amino acid sequence of CDR3-β is shown in SEQ ID NO.

13.

2. The MAGE-A10 specific T cell receptor MAGE-A10 TCR according to claim 1, characterized in that: The amino acid sequence of the CDR1-α is shown in SEQ ID NO.3; the amino acid sequence of the CDR2-α is shown in SEQ ID NO.4; and / or The amino acid sequence of the CDR1-β is shown in SEQ ID NO.5; the amino acid sequence of CDR2-β is shown in SEQ ID NO.

6.

3. The MAGE-A10 specific T cell receptor MAGE-A10 TCR according to claim 2, characterized in that: The amino acid sequence of the TCRα chain is shown in SEQ ID NO.7; the amino acid sequence of the TCRβ chain is shown in SEQ ID NO.

8.

4. A nucleic acid, characterized in that The nucleic acid encodes the MAGE-A10-specific T cell receptor MAGE-A10 TCR according to any one of claims 1-3.

5. The nucleic acid according to claim 4, characterized in that The nucleotide sequence of the nucleic acid includes: a nucleotide sequence as shown in SEQ ID NO.9 and a nucleotide sequence as shown in SEQ ID NO.

10.

6. The nucleic acid according to claim 5, characterized in that The nucleotide sequence of the nucleic acid is shown in SEQ ID NO.

11.

7. A recombinant expression vector, characterized in that: The recombinant expression vector comprises the nucleic acid according to any one of claims 4 to 6.

8. A host cell, characterized in that The host cell comprises the nucleic acid according to any one of claims 4 to 6, or the recombinant expression vector according to claim 7.

9. A MAGE-A10 TCR-T cell, characterized in that: The MAGE-A10 TCR-T cell comprises the MAGE-A10 specific T cell receptor MAGE-A10 TCR according to any one of claims 1-3.

10. Use of the MAGE-A10 specific T cell receptor MAGE-A10 TCR according to any one of claims 1 to 3, the nucleic acid according to any one of claims 4 to 6, the recombinant expression vector according to claim 7, the host cell according to claim 8 and / or the MAGE-A10 TCR-T cell according to claim 9 in the preparation of a drug for treating cancer.

11. The use according to claim 10, characterized in that: The cancers include hepatocellular carcinoma, melanoma, lung cancer and / or urothelial carcinoma.

12. A pharmaceutical composition comprising the MAGE-A10 specific T cell receptor MAGE-A10 TCR according to any one of claims 1 to 3, the nucleic acid according to any one of claims 4 to 6, the recombinant expression vector according to claim 7, the host cell according to claim 8 and / or the MAGE-A10 TCR-T cell according to claim 9, and a pharmaceutically acceptable carrier.

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