MAGE-a10-specific t cell receptors and their use in immunotherapy

Abstract

The application discloses a MAGE-A10 specific T cell receptor (MAGE-A10 TCR) and application thereof in immunotherapy, and belongs to the technical field of tumor immunotherapy. The MAGE-A10 TCR is screened from tumor reactive T cells of HCC patients, can specifically recognize MAGE-A10 antigens, and is used for constructing MAGE-A10 TCR-T cells. Cytotoxicity tests show that the MAGE-A10 TCR-T cells have a clearing effect on HCC cells.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of tumor immunotherapy, and particularly relates to MAGE-A10 specific T cell receptors and their application in immunotherapy, especially in hepatocellular carcinoma immunotherapy. BACKGROUND

[0002] Melanoma-associated antigen family A member 10 (MAGE-A10) is a nuclear protein with a molecular weight of about 70 kDa, which plays a role in embryonic development and tumor transformation or tumor progression, and is a cancer-testis antigen (CTA) that 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, which is one of the main causes of global cancer-related deaths, and the related treatment methods are of great concern. Since HCC is usually diagnosed at an advanced stage, the treatment of HCC is extremely challenging. In addition to surgery and interventional therapy, 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 treatment methods and drugs are in clinical trials.

[0004] The immunological role of T cells and the potential of HCC-associated 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 intrinsic 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 the body of a patient and maintain their ability to attack tumor cells, thus providing a lasting therapeutic effect. The molecular structure of TCRs helps to more accurately understand the basic principles of TCR identification. The part of the TCR that directly interacts with the peptide-major histocompatibility complexes (p-MHCs) is the complementarity determining region (CDR), including 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 alpha and beta 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 associated with AFP levels. However, there are few reports of TCR-T cells targeting (recognizing) this antigen. SUMMARY

[0006] 1. OBJECTIVES

[0007] The application aims to provide a MAGE-A10 specific T cell receptor (MAGE-A10 TCR) and its application in immunotherapy, especially hepatocellular carcinoma immunotherapy. The MAGE-A10 TCR is screened from tumor-reactive T cells of HCC patients, and the specific TCR recognizing the MAGE-A10 antigen obtained by single-cell sequencing is used to construct MAGE-A10 TCR-T cells, and the cytotoxicity test shows that the MAGE-A10 TCR-T cells have a clearance effect on HCC cells.

[0008] 2. Technical solution

[0009] In order to achieve the above-mentioned application purposes, the technical solutions adopted by the present application are as follows:

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

[0011] Further, the amino acid sequence of CDR3-alpha is as shown in SEQ ID NO. 1; and the amino acid sequence of CDR3-beta is as shown in SEQ ID NO. 2.

[0012] Further, the amino acid sequence of CDR1-alpha is as shown in SEQ ID NO. 3; and the amino acid sequence of CDR2-alpha is as shown in SEQ ID NO. 4.

[0013] Further, the amino acid sequence of CDR1-beta is as shown in SEQ ID NO. 5; and the amino acid sequence of CDR2-beta is as shown in SEQ ID NO. 6.

[0014] Further, the amino acid sequence of the TCR alpha chain is as shown in SEQ ID NO. 7; and the amino acid sequence of the TCR beta chain is as 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-A10 TCR.

[0016] Further, the nucleotide sequence of the nucleic acid described above 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 as shown in SEQ ID NO. 9 encodes a TCRa chain of an amino acid sequence as shown in SEQ ID NO. 7, and the nucleotide sequence as shown in SEQ ID NO. 10 encodes a TCRb chain of an amino acid sequence as shown in SEQ ID NO. 8.

[0017] Further, the nucleotide sequence of the nucleic acid described above is as shown in SEQ ID NO. 11, encoding a TCRa chain and a TCRb chain.

[0018] The present application also provides a recombinant expression vector comprising the nucleic acid encoding the MAGE-A10 TCR described above.

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

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

[0021] The present application also provides a MAGE-A10 TCR-T cell comprising the MAGE-A10 TCR described above, which is capable of specifically recognizing melanoma-associated antigen family A member 10 (MAGE-A10).

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

[0023] Further, the cancer described above includes hepatocellular carcinoma, melanoma, lung cancer, and / or urothelial carcinoma, etc.

[0024] Further, the cancer described above includes hepatocellular carcinoma.

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

[0026] 3. Beneficial effects

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

[0028] The application provides a MAGE-A10 specific T cell receptor (MAGE-A10 TCR) and application thereof in immunotherapy. The MAGE-A10 TCR is derived from a tumor reactive T cell of an HCC patient, and is determined to be capable of specifically recognizing MAGE-A10. The MAGE-A10 TCR is used to construct a MAGE-A10 TCR-T cell, and a cytotoxicity test shows that the MAGE-A10 TCR-T cell has a clearance effect on HCC cells. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 FIG. 1 is a comparison chart of single cell sequencing data of a hepatocellular carcinoma patient before and after removing batch effects, wherein the left side is before removing batch effects, and the right side is after removing batch effects.

[0030] Figure 2 FIG. 2 is a single cell map of a hepatocellular carcinoma patient, wherein:

[0031] A is a UMAP chart, and different colors represent different cell types;

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

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

[0034] D is a scatter chart showing the percentage of each cell type in normal tissue (Normal) and tumor tissue (Tumor);

[0035] E is a UMAP chart, and different colors represent different tissue sources;

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

[0037] Figure 3 FIG. 3 is a UMAP chart of T cells and TCR matching results, wherein:

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

[0039] B shows samples of matched T cells;

[0040] C shows tissue sources of matched T cells;

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

[0042] Figure 4 FIG. 4 is a CD4 +T cell atlas, where:

[0043] A is a UMAP plot, different colors represent different CD4 + T cell subsets;

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

[0045] C is CD4 + T cell pseudo-time trajectory plot on pseudo-time trajectory;

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

[0047] Figure 5 is the proportion of CD8 + T cell atlas, where:

[0048] A is a UMAP plot, different colors represent different CD8 + T cell subsets;

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

[0050] C is CD8 + T cell pseudo-time trajectory plot on pseudo-time trajectory;

[0051] D-G are UMAP plots showing non-virus reactive (D), expanded (E), tumor specific (F), and reactive (G) CD8 + T cells.

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

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

[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 charts show the proportion of V(C) and J(D) genes in bystander T cells and reactive T cells in TCR beta chain, *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 secretion of IFN-γ detected by ELISA after stimulating MAGE-A10 TCR-T cells with MCF-7 cells loaded with different concentrations of control and MAGE-A10 peptides (n=3);

[0058] B is MAGE-A10 + The secretion of IFN-γ detected by ELISA after MCF-7 cells interacted with Mock-T and TCR-T cells at different E / T ratios (n=4);

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

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

[0061] Mock-T: T cells transfected with helper plasmid, without encoding vector;

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

[0063] The present application will be further described below in conjunction with specific examples.

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

[0065] Unless otherwise noted, conventional conditions or manufacturer's recommended conditions were used in the examples. Unless otherwise noted, the reagents or instruments used were conventional products available commercially.

[0066] As used herein, the term "about" is used to provide flexibility to a given term, measurement, or value. The degree of flexibility of a particular variable will be readily determined by one of skill in the art.

[0067] As used herein, the term "at least one of" is intended to mean one or more of the listed items. For example, "at least one of A, B, and C" is intended to mean A alone, B alone, C alone, as well as combinations thereof.

[0068] Concentrations, amounts, and other numerical data can be presented in a range format in this application. It is to be understood that such a range format is used merely for convenience and brevity and should be interpreted flexibly to include not only the numerical values explicitly recited as the limits of a range, but also to include all the individual numerical values or sub-ranges encompassed within that range as if each numerical value and sub-range is explicitly recited. For example, a range of about 1 to about 4.5 should be interpreted to include not only the explicitly recited limits of about 1 to about 4.5, but also include individual numbers such as 2, 3, and 4 and the sub-ranges such as 1-3, 2-4, etc. The same principle applies to ranges reciting only one numerical value, such as "less than about 4.5," which should be interpreted to include all values and ranges above the upper limit of the recited range. Such interpretations are used only when in context appropriate understanding of one or both of the number range limits is required. Further, the interpretation that follows this type of range is applicable regardless of the breadth of the range or the characteristics being described.

[0069] As used herein, an antigenic epitope, also known as an antigenic determinant, refers to a specific chemical group on an antigen molecule that determines antigenic specificity.

[0070] In this application, paired samples of hepatocellular carcinoma (HCC) cancer tissue (tumor tissue) and adjacent normal tissue (paracancerous tissue) were derived from 3 tumors resected successively in the Third Affiliated Hospital of Suzhou University from June 2021 to November 2022. The informed consent of all participants and the approval of 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 3 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 pg / mL insulin. HepG2 cells were grown in DMEM containing 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. All cell culture reagents were from Invitrogen unless otherwise specified. 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); comparison between two groups was performed using paired t test, and P<0.05 was considered as statistically significant difference.

[0075] Example 1

[0076] This example provides screening of T cell receptors (MAGE-A10 TCR) recognizing MAGE-A10 antigen.

[0077] The screening process of MAGE-A10 TCR includes:

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

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

[0080] The scRNA-seq data was analyzed using R software package Seurat, and cells with UMI count <15,000 and mitochondrial gene count <20% were retained, and DoubletFinder was used to remove doublet cells, followed by batch effect elimination by Harmony package; the analysis results showed that a total of 51,734 cells were obtained, which were divided into 8 cell clusters by UMAP dimension 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 sample, 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 tumor microenvironment and their dominant role in immune response;

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

[0082] Since the shared TCR was not present in the tumor samples of 3 patients, the T cells were divided into CD4 + and CD8 + subgroups, and the reactive T cells were identified based on the three conditions of non-viral reactivity, clonal expansion and tumor specificity; the results of the analysis showed that the reactive T cells were mainly enriched in CD4_CD69_Th, CD4_FOXP3_Treg, CD4_CXCL13_T EX and CD8_CXCL13_T EX ; There was a clear distinction between reactive T cells and bystander T cells, and differential gene analysis showed that key genes such as GNG8 and CCR8 in reactive T cells were significantly up-regulated, which were related to enhanced T cell function and tumor immunotherapy; GO and KEGG pathway enrichment analysis further showed that the up-regulated genes of reactive T cells were 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 was a significant difference in TCR diversity between reactive T cells and bystander T cells;

[0083] To determine the possibility of reactive T cells recognizing shared p-MHCs, GLIPH2 was used for clustering analysis of TCR, obtaining 12 catalogs containing 31 CDR3 sequences; The results showed that the sequence with the highest frequency was CDR3b10, followed by CDR3b27; Subsequently, the TCR beta CDR3 sequence was matched with the cell type and verified by phenotype, and due to the immunosuppressive effect of Treg, 10 CDR3 sequences located on Treg were excluded, and finally the candidate TCRs were reduced to 21 sequences, called reactive TCRs;

[0084] Epitope prediction verification was performed on 21 TCRs using the TCRMatch tool to explore their potential to recognize HCC-related antigens; 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 other various tumors (such as melanoma, lung cancer and urothelial carcinoma), and it has become a potential therapeutic target for HCC and other various tumors due to its high immunogenicity.

[0085] Specifically includes:

[0086] (1) Sample collection and processing

[0087] Paired tumor tissue and adjacent normal tissue (paracancerous tissue) samples from 3 HCC patients were collected, 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 standard analysis of scRNA-seq data was performed using the R 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 a 20% parameter identification using the R 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. Dimensionality reduction and clustering were performed based on established marker genes. Uniform manifold approximation and projection (UMAP) was used to visualize the cell clusters and create 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 and myeloid cells, which play a dominant role in the immune response; and non-immune clusters composed of hepatocytes, endothelial cells and 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 tissue mostly gathered 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 the tumor sample and the high level of T cell infiltration in the tumor microenvironment (TME).

[0099] (5) Extract T cells and match them with scTCR-seq data

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

[0101] After ensuring barcode consistency, the scTCR-seq data were matched with the scRNA-seq data of T cells using the combineExpression function of the scRepertoire software package (version 2.0.5) based on default parameters. Cells lacking clonal information were filtered out, resulting in 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 + Subgroup ( 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] It can be learned from Figure 3 that the majority of CD8 + T cells expand in tumors, indicating that they are activated to resist damage from tumor cells and ensure long-term protective immunity. Unfortunately, we were unable to identify shared TCRs in tumor samples from 3 patients; therefore, the inventors employed an alternative approach to divide T cells into two subpopulations and identify reactive T cells in CD4 + T cells and CD8 + T cells, followed by identifying TCRs that can recognize HCC-associated antigens to construct TCR-T cells.

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

[0105] (a) Identification of reactive T cells in CD4 + T cells

[0106] Referring to the established marker genes, 7,094 CD4 + T cells were clustered into 5 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 exhausted (CD4_c4_CXCL13_T EX ) T cells (A and B in Figure 4 ).

[0107] Trajectory analysis of CD4 + T cells: scRNA-seq data was analyzed using R package Monocle2 (version 2.32.0), genes expressed in <10 cells were filtered out, and DDRTree method was selected for dimension reduction, so as to obtain and visualize the differentiation process of two T cell subpopulations, which mainly differentiate into 3 branches: Th, T EM , and Treg (C in Figure 4 ).

[0108] Identification of reactive CD4 + T cells:

[0109] The following 3 conditions were set to identify reactive CD4 + T cells, specifically:

[0110] (i) non-viral reactivity: all beta chain CDR3 amino acid sequences corresponding to viral epitopes were from IEDB (Immune Epitope Database and Analysis Resource) and compared to CD4 + T cell beta chain CDR3, T cells with sequence-inconsistent CDR3 could not recognize known viruses Figure 4 (D); but they have the potential to specifically recognize tumor antigens, thus, these T cells were kept as non-viral reactive cells;

[0111] (ii) expansion: CD4 + T cells with clone size larger than 1 were considered to have completed clonal expansion, thus were selected into expanded cells Figure 4 (E), indicating resistance to tumor antigens;

[0112] (iii) tumor specificity: CD4 + T cells that only infiltrated tumor tissues were sorted into tumor-specific population Figure 4 (F);

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

[0114] In combination with CD4 + T cell atlas findings, reactive T cells in CD4 + T cells were mainly enriched in CD4_CD69_Th, CD4_FOXP3_Treg, and CD4_CXCL13_T EX , of which two types were the major fates of CD4 + T cells.

[0115] (b) Identification of reactive T cells in CD8 + T cells

[0116] In reference to the identification of reactive T cells in CD4 + 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 ), exhaustion (CD8_c2_CXCL13_T EX), cytotoxic (CD8_c3_GNLY_CTL), mucosal-associated invariant (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 phenotypes. Trajectory analysis showed that CD8 + T cells mainly differentiated into T EM , T STR and T EX branches Figure 5 (C). With the same standard to define reactive CD4 + T cells, non-virus reactive, expanded, and tumor-specific CD8 + T cells were considered as reactive cells Figure 5 (D-F).

[0117] Combining the CD8 + T cell atlas, reactive T cells in CD8 + T cells were mainly enriched in CD8_CXCL13_T EX ( Figure 5 G), which is one of the cell fates.

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

[0119] (7) Difference 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) Gene expression difference between reactive T cells and bystander T cells

[0122] To examine the rationality of the reactive T cells defined according to our principle, the differentially expressed genes of reactive T cells relative to bystanders were analyzed using the DESeq2 package (version 1.44.0), and SRplot (http: / / www.bioinformatics.com.cn / SRplot) was used to create an enhanced volcano plot Figure 6Figure 6. Reactivity T cells show a gene signature associated with immune response. A) Reactivity T cells show a gene signature associated with immune response. B) Reactivity T cells show a gene signature associated with immune response. C) Reactivity T cells show a gene signature associated with immune response. D) Reactivity T cells show a gene signature associated with immune response. The Benjamini and Hochberg (BH) method was used to calculate the corrected P value, and a difference was considered statistically significant at <0.05. Among the significantly up-regulated genes, GNG8, which encodes a G protein subunit, has been reported to be involved in the regulation of intracellular signaling pathways and is a positive factor for the immunotherapy of 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. Compared with bystanders, reactive T cells showed enhanced function, as evidenced by the up-regulation of key genes such as GNG8 and CCR8.

[0123] The inventors also performed GO and KEGG pathway enrichment analysis. ClusterProfile (version 4.12.3) was used to perform gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis on the differentially expressed genes, and SRplot was used to create enrichment bar graphs Figure 6 Figure 6. Reactivity T cells show a gene signature associated with immune response. A) Reactivity T cells show a gene signature associated with immune response. B) Reactivity T cells show a gene signature associated with immune response. C) Reactivity T cells show a gene signature associated with immune response. D) Reactivity T cells show a gene signature associated with immune response. The Benjamini and Hochberg (BH) method was used to calculate the corrected P value, and a difference was considered statistically significant at <0.05. Among the significantly up-regulated genes, GNG8, which encodes a G protein subunit, has been reported to be involved in the regulation of intracellular signaling pathways and is a positive factor for the immunotherapy of 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. Compared with bystanders, reactive T cells showed enhanced function, as evidenced by the up-regulation of key genes such as GNG8 and CCR8.

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

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

[0126] A total of 44 different V genes were calculated; however, TRBV16( Figure 6 Figure 6. Reactivity T cells show a gene signature associated with immune response. A) Reactivity T cells show a gene signature associated with immune response. B) Reactivity T cells show a gene signature associated with immune response. C) Reactivity T cells show a gene signature associated with immune response. D) Reactivity T cells show a gene signature associated with immune response. The Benjamini and Hochberg (BH) method was used to calculate the corrected P value, and a difference was considered statistically significant at <0.05. Among the significantly up-regulated genes, GNG8, which encodes a G protein subunit, has been reported to be involved in the regulation of intracellular signaling pathways and is a positive factor for the immunotherapy of 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. Compared with bystanders, reactive T cells showed enhanced function, as evidenced by the up-regulation of key genes such as GNG8 and CCR8.

[0127] Among the J genes, TRBJ2-7 was the most common in bystander T cells. TRBJ2-1 was the most common gene in reactive T cells and the most different between the two groups Figure 6 Figure 6. Reactivity T cells show a gene signature associated with immune response. A) Reactivity T cells show a gene signature associated with immune response. B) Reactivity T cells show a gene signature associated with immune response. C) Reactivity T cells show a gene signature associated with immune response. D) Reactivity T cells show a gene signature associated with immune response. The Benjamini and Hochberg (BH) method was used to calculate the corrected P value, and a difference was considered statistically significant at <0.05. Among the significantly up-regulated genes, GNG8, which encodes a G protein subunit, has been reported to be involved in the regulation of intracellular signaling pathways and is a positive factor for the immunotherapy of 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. Compared with bystanders, reactive T cells showed enhanced function, as evidenced by the up-regulation of key genes such as GNG8 and CCR8.

[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 useful for monitoring the immune response to tumors, which can provide some insights for the development of personalized immunotherapy.

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

[0130] To determine the potential for reactive T cells to recognize shared p-MHCs, we used GLIPH2 to provide significant TCRs based on global similarity and local motifs. The web tool GLIPH2 (Grouping of Lymphocyte Interactions by 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 China A and Figure 5 In Figure A), TCRβ CDR3 sequences were mapped to cell types for phenotypic validation, as shown in Table 2. 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, each of which could theoretically recognize the same p-MHCs. Catalogs 1, 3, and 4 contained the same CDR3, albeit in different patterns, as did catalogs 6 and 11. The most frequent sequence was CDR3b10, followed by CDR3b27. Ten CDR3 sequences located on Tregs were excluded because they suppress immune responses. Thus, the pool of candidate TCRs was narrowed to 21 sequences, designated 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, each TCR was subjected to epitope prediction validation by the analysis tool TCRMatch of IEDB to determine the potential T cell-recognizable epitopes. The web tool TCRMatch (http: / / tools.iedb.org / tcrmatch / ) can score the similarity of selected and beta chain CDR3 in the Immune Epitope Database (IEDB) (https: / / www.iedb.org / home_v3.php) by comprehensive k-mer comparison. The epitopes recognized by CDR3 in IEDB are known, so matched T cell antigen epitopes can be provided based on similarity. Start the analysis after inputting the CDR3 sequence, selecting the conservative C / F+W motif for pruning, and setting the threshold to 0.90.

[0138] After removing viruses and non-human antigens from the result list, it was found that the most common CDR3b10 in Catalog 5 did not predict a matched epitope. However, CDR3b12 shared in Catalog 6 and 11 was predicted to be able 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 also 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 multiple 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 MHC molecules.

[0144] MAGE-A10 protein is mainly located in the nucleoplasm, and nuclear proteins can be directly processed by proteasomes in the nucleoplasm and presented to MHC-I molecules. 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 the MHC molecule, which will have a major impact on antigen presentation.

[0145] The affinity of MAGE-A10 to HLA-A*02:01 was predicted by first inputting 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. Given 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 amino acid full-length sequence of MAGE-A10 was obtained in TANTiGEN 2.0 (http: / / projects.met-hilab.org / tadb / cgi / displayAntigen.pl?ACC=Ag000032):

[0147] MPRAPKRQRCMPEEDLQSQSETQGLEGAQAPLAVEEDASSSTSTSSSFPSSFPSSSSSSSSSCYPLIPSTPEEVSADDETPNPPQSAQIACSSPSVVASLPLDQSDEGSSSQKEESPSTLQVLPDSESLPRSEIDEKVTDLVQFLLFKYQMKEPITKAEILESVIKNYEDHFPLLFSEASECMLLVFGIDVKEVDPTGHSFVLVTSLGLTYDGMLSDVQSMPKTGILILILSIIFIEGYCTPEEVIWEALNMMGLYDGMEHLIYGEPRKLLTQDWVQENYLEYRQVPGSDPARYEFLWGPRAHAEIRKMSLLKFLAKVNGSDPRSFPLWYEEALKDEEERAQDRIATTDDTTAMASASSSATGSFSYPE.

[0148] The polypeptides predicted by NetMHCpan are ranked from low to high by IC 50 Ranking from low to high by IC 50Peptides with a binding affinity <50 nM were considered high, and based on this principle, the top six peptides in the overall list were identified (Table 4). Furthermore, Mp02 was also observed in the TCRMatch results (Table 3), suggesting that this peptide or the entire MAGE-A10 antigen could be a therapeutic target. T2 cell-peptide binding assays in the study "Identification of a new MAGE-A10 antigenic peptide presented by HLA-A*0201 on tumor cells" have confirmed that HLA-A*02:01 molecules have binding affinity for 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, we validated the affinity of TCRs for antigenic epitopes using DLpTCR. DLpTCR (http: / / jianglab.org.cn / DLpTCR / ) is a multimodal integrated deep learning framework with high predictive performance for TCR-epitope interactions. We submitted paired CDR3 and MP02 files and began analysis using DLpTCR in three different ways.

[0155] High affinity between an epitope and an MHC molecule is not sufficient. Therefore, affinity validation between a TCR and an epitope is essential to ensure successful recognition and initiation of an in vivo immune response. CDR3b27 and CDR3b28 can also recognize MAGE-A10, as they belong to the same catalog as CDR3b1211, although they were not predicted to have this property. Therefore, all three CDR3s fall within the scope of affinity validation. DLpTCRs can be used to examine interactions between TCRs and epitopes. Furthermore, the predicted interactions between single- and double-chain TCRs and Mp02 were revealed (Table 5). CDR3b12 binds to Mp02, consistent with the results from TCRMatch; however, its corresponding α-chain CDR3 does not. Fortunately, two other TCR chains bind to Mp02, demonstrating the reliability of the GLIPH2 analysis. Based on this, CDR3b27, along with CDR3a27, is a preferred candidate TCR, as it ranks second in frequency (Table 2).

[0156] Table 5

[0157]

[0158] Example 4

[0159] This example provides the preparation of MAGE-A10 TCR-T cells.

[0160] In this example, the amino acid sequences or nucleotide sequences involved are shown in Table 6, CDR1-a, CDR2-a and CDR3-a are three variable regions of TCRa chain, the amino acid sequences of which are shown in SEQ ID NO. 3, SEQ ID NO. 4 and SEQ ID NO. 1 respectively; CDR1-b, CDR2-b and CDR3-b are three variable regions of TCRb chain, the amino acid sequences of which are shown in SEQ ID NO. 5, SEQ ID NO. 6 and SEQ ID NO. 2 respectively. The amino acid sequences of TCRa chain and TCRb 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 gene and recombinant lentivirus

[0167] The nucleotide sequence of the TCR gene is shown in SEQ ID NO. 11, including the nucleotide sequences encoding TCRa chain and TCRb chain and the nucleotide sequence encoding P2A sequence, in the process of translation, TCRa chain and TCRb chain are obtained by ribosome skipping mechanism. The sequence is synthesized by Shanghai Jikai Gene Technology Co., Ltd. (GENE);

[0168] The TCR gene is cloned into the vector, and the order of elements of the complete recombinant lentivirus vector plasmid is: pRRLSIN-cPPT-SFFV-TCRa-P2A-TCRb-E2A-EGFP-SV40-puromycin; wherein TCRa-P2A-TCRb is the TCR gene;

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

[0170] The supernatant was collected 48 h after transfection, and centrifugation and filtration were sequentially performed to remove cell debris and impurities, followed by ultracentrifugation at 25000 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-A10 TCR-T)

[0172] Peripheral blood mononuclear cells (PBMCs) from an HLA-A*02:01 positive healthy donor were obtained using Ficoll-Paque, and then the PBMCs were activated in the presence of 100 ng / mL OKT-3 antibody and 100 IU / mL IL-2 for 48 h. According to the manufacturer's instructions, CD8 + T cells were separated from the PBMCs using CD8 + T cell isolation kit (11348D, Invitrogen). Subsequently, 1 million CD8 + T cells were transduced with 50 μL of concentrated MAGE-A10-TCR lentivirus, and the transduction lasted for 4 h. The obtained T cells were transduced again after 24 hours. After screening, the T cells were expanded 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-A10 TCR-T cells.

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

[0176] This example verifies the actual affinity between MAGE-A10 TCR-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-A10 TCR-T cells;

[0179] Mock-T cells: T cells transduced with helper plasmids (gag-pol plasmid and VSV-G plasmid) without MAGE-A10-TCR gene;

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

[0181] MAGE-A10 + MCF-7 cells: MCF-7 cells stably expressing MAGE-A10, which are prepared according to the preparation of MAGE-A10 TCR-T cells, specifically: synthesize the MAGE-A10 gene into the lentivirus vector plvx (125839, Addgene); then co-transfect HEK-293T cells with the helper plasmids PxpAx2 (12260, Addgene) and PMD2g (12259, Addgene); transduce the produced lentivirus into MCF-7 cells, obtain a cell strain stably transduced with the MAGE-A10 gene, and then verify the stable expression of the gene MAGE-A10 by PCR and Western blot methods;

[0182] MAGE-A10 + HepG2 cells: HepG2 cells stably expressing MAGE-A10, which are prepared according to the preparation of MAGE-A10 TCR-T cells, specifically: synthesize the MAGE-A10 gene into the lentivirus vector plvx; then co-transfect HEK-293T cells with the helper plasmids PxpAx2 and PMD2g; transduce the produced lentivirus into HepG2 cells, obtain a cell strain stably transduced with the MAGE-A10 gene, and then verify the stable expression of the gene MAGE-A10 by PCR and Western blot methods.

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

[0184] Preparation of peptide solution: MAGE-A10 peptide or MAGE-B6 peptide (control peptide) was dissolved in dimethyl sulfoxide (DMSO) to prepare different concentrations of peptide solution (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 a 6-well plate at a density of 1 x 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 with PBS for 3 times to remove unbound peptides.

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

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

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

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

[0190] Result analysis:

[0191] MCF-7 cells are a kind of breast cancer cells that do not express MAGE-A family proteins, so MCF-7 is loaded with MAGE-A10 and co-cultured with TCR-T cells. As the concentration of MAGE-A10 peptide increases, the secretion of IFN-γ measured by ELISA also increases Figure 7 MCF-7 cells are a kind of breast cancer cells that do not express MAGE-A family proteins, so MCF-7 is loaded with MAGE-A10 and co-cultured with TCR-T cells. As the concentration of MAGE-A10 peptide increases, the secretion of IFN-γ measured by ELISA also increases

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

[0193] Co-culture TCR-T cells or Mock-T cells with MAGE-A10 + MCF-7 cells were co-cultured in a 1:1 ratio in a 96-well plate, and the supernatant was collected after overnight incubation in an incubator; the required ELISA plate was removed, 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 wells were covered with 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 wells were covered with sealing film, and incubated at room temperature for 30 minutes; the plate was washed 3 times, 100 μL of color developing agent TMB solution was added to each well, the reaction wells were covered with sealing film, and incubated at room temperature for 30 minutes; 100 μL of stop solution was added to each well, and the absorbance value was measured at 450 nm using a microplate reader after mixing.

[0194] Result analysis:

[0195] Compared with Mock-T cells, TCR-T cells showed significantly enhanced effects on MAGE-A10 + The effects of MAGE-A10 TCR-T cells on MCF-7 cells were significantly enhanced Figure 7 B).

[0196] (2) Difference in the effects of MAGE-A10 TCR-T cells and ordinary T cells.

[0197] MAGE-A10 TCR-T cells were co-cultured with tumor cells at the specified effector-to-target (E / T) ratio overnight, and lactic dehydrogenase (LDH) was released when the cells were damaged or died. According to the manufacturer's instructions, the activity of LDH was detected to reflect the lysis of tumor cells. At the same time, dead tumor cells were stained with propidium iodide (PI). Specifically, it includes:

[0198] (a) Lactic dehydrogenase (LDH) release test

[0199] According to the instructions of the lactic dehydrogenase cytotoxicity detection kit (C0016, Beyotime), the cytotoxicity was detected:

[0200] The 96-well plate was divided into the following groups: cell-free culture solution wells (background blank control wells), untreated control cell wells (sample control wells), untreated cells for subsequent lysis wells (sample maximum enzyme activity control wells), and treated cell wells (sample wells), and were labeled;

[0201] Except for "background blank control well", 1 x 10 4 MAGE-A10 - MCF-7 cells, MAGE-A10 + MCF-7 cells or MAGE-A10 + HepG2 cells, and then an equal amount of culture solution was added to the "background blank control well";

[0202] The TCR-T cells were added to the "sample well" at a specified E / T ratio (0.125:1, 0.25:1, 0.5:1, 1:1, and 2:1, respectively) with tumor cells, and an equal amount of culture solution was added to the remaining labeled wells, and then incubated in an incubator overnight.

[0203] One hour before the scheduled detection time, the LDH release reagent provided in the kit was added to the "sample maximum enzyme activity control well" at a volume of 10% of the original culture solution. After mixing by repeated pipetting several times, the incubation was continued in the incubator.

[0204] After reaching the scheduled time, the cell culture plate was centrifuged at 400 x g for 5 min using a multi-well plate centrifuge.

[0205] The supernatant of each well was taken and added to the corresponding well of a new 96-well plate. The LDH detection working solution was added, and after mixing, the plate was incubated at room temperature for 30 min in the dark.

[0206] The absorbance value was measured at 490 nm using an enzyme-labeled instrument.

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

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

[0209] (b) Hoechst 33342 / PI staining

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

[0211] The TCR-T cells were co-cultured with MAGE-A10 - MCF-7 cells, MAGE-A10 + MCF-7 cells or MAGE-A10 + HepG2 cells at a specified E / T ratio (1:1), and then incubated in an incubator overnight.

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

[0213] After staining, the cells were carefully washed once with PBS and observed 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. Figure 7 C and D). Compared with MCF-7, TCR-T cells showed a significant difference in the 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 Middle C), resulting in the death of about half of the tumor cells, which is higher than MAGE-A10 + MCF-7( Figure 7 (D) This may be due to the additive effect of TCR-T cell recognition of AFP expressed by HepG2.

[0216] In summary, TCR-T cells are able to specifically recognize and eliminate MAGE-A10-positive tumor cells. Furthermore, when targeting HCC cells, they can generate even stronger lethality by recognizing other HCC-associated antigens. This potent and precise cytotoxicity exhibited by MAGE-A10 TCR-T cells against HCC cells is expected to be incorporated into new therapeutic 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; 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; The amino acid sequence of the CDR1-β is shown in SEQ ID NO.5; the amino acid sequence of the CDR2-β is shown in SEQ ID NO.

6.

2. The MAGE-A10-specific T cell receptor MAGE-A10 TCR according to claim 1, characterized in that The amino acid sequence of the TCR α chain is shown in SEQ ID NO.7; the amino acid sequence of the TCR β chain is shown in SEQ ID NO.

8.

3. A nucleic acid, characterized in that The nucleic acid encodes the MAGE-A10-specific T cell receptor MAGE-A10 TCR according to claim 1 or 2.

4. The nucleic acid according to claim 3, 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.

5. The nucleic acid according to claim 4, characterized in that The nucleotide sequence of the nucleic acid is shown in SEQ ID NO.

11.

6. A recombinant expression vector, characterized in that: The recombinant expression vector comprises the nucleic acid according to any one of claims 3 to 5.

7. A host cell, characterized in that The host cell comprises the nucleic acid according to any one of claims 3 to 5, or the recombinant expression vector according to claim 6.

8. 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 claim 1 or 2.

9. Use of the MAGE-A10-specific T cell receptor (MAGE-A10 TCR) of claim 1 or 2, the nucleic acid of any one of claims 3 to 5, the recombinant expression vector of claim 6, the host cell of claim 7, and / or the MAGE-A10 TCR-T cell of claim 8 in the preparation of a medicament for treating cancer; the cancer being selected from hepatocellular carcinoma, melanoma, lung cancer, and / or urothelial carcinoma.

10. A pharmaceutical composition comprising the MAGE-A10-specific T cell receptor (MAGE-A10 TCR) of claim 1 or 2, the nucleic acid of any one of claims 3 to 5, the recombinant expression vector of claim 6, the host cell of claim 7 and / or the MAGE-A10 TCR-T cell of claim 8, and a pharmaceutically acceptable carrier.

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