Immunogenicity-based antigen peptide screening method and immunogenicity-based antigen peptide evaluation method

Abstract

The invention provides an antigen peptide screening method and evaluation method based on immunogenicity, and belongs to the technical field of immunology. The invention provides an evaluation method for candidate antigen peptides screened by a conventional machine algorithm, and the candidate antigen peptides are subjected to polypeptide MHC binding affinity difference and immunogenicity calculation, the probability that candidate antigen peptide-MHC compounds are identified by TCR, the transport efficiency of antigen transporter (TAP), proteasome splitting decomposition and expression abundance and the like, so that the candidate antigen peptides are evaluated. And evaluating the candidate antigen peptide. The invention further provides a screening method of the antigen peptide, and the screening method comprises the steps of machine algorithm prediction, evaluation, ELISpot verification, IFN gamma flow sorting, co-culture with an organoid model, embedding section and Ki-67 immunohistochemical staining, and screening to obtain the immunogenic antigen peptide. After the screening, the new antigen peptide fragment with strong immunogenicity can be more accurately screened.

Description

Technical Field

[0001] The present invention relates to the technical field of immunology, and in particular to an antigen peptide screening method and an evaluation method based on immunogenicity. Background Art

[0002] At present, the screening of new antigen peptides is mainly carried out through machine algorithms and enzyme-linked immunospot (ELISpot) technology. The machine algorithm mainly includes the prediction of peptide HLA binding ability and peptide immunogenicity. In terms of HLA binding ability prediction, there are currently more recognized algorithms; after the peptide binds to the HLA molecule, the ability to activate T cells to further kill tumors is called immunogenicity. However, there is currently no recognized algorithm for immunogenicity, an important peptide performance. The evaluation and optimization of candidate new antigen peptides is an important research direction.

[0003] ELISpot technology is almost the only in vitro validation method for the immunogenicity of new antigen peptides. It validates the ability of new antigen peptides to activate T cells in vitro at the cellular level. However, this technology ignores the effect of tumor immune microenvironment on immunogenicity, and the immune microenvironment of glioblastoma has a profound effect on the efficacy of immunotherapy. Therefore, it is necessary to propose a more accurate in vitro validation model. Summary of the invention

[0004] The present invention provides an antigen peptide screening method and an evaluation method based on immunogenicity. After the evaluation and screening, immunogenic new antigen peptide segments that can be used in clinical treatment can be obtained.

[0005] The present invention provides a method for evaluating candidate antigen peptides, comprising performing the following operations on the screened candidate antigen peptides respectively: (1) detecting the binding affinity between the candidate antigen peptides and MHC;

[0006] (2) detecting the immunogenicity of the candidate antigenic peptide;

[0007] (3) detecting the probability of candidate antigen peptide-MHC complex being recognized by TCR;

[0008] (4) a comprehensive prediction score that tests MHC presentation, transport efficiency of antigen transporters, and proteasome cleavage rate;

[0009] (5) Detecting the expression abundance of the candidate antigen peptide.

[0010] In a specific embodiment of the present invention, NetMHCpan is used to calculate the difference score of the binding affinity between the wild-type peptide and the mutant peptide of the candidate antigen peptide and MHC.

[0011] In a specific embodiment of the present invention, Vaxijen and Virus-immu are used to calculate the immunogenicity of the candidate antigen peptide.

[0012] In a specific embodiment of the present invention, Neo Immune is used to calculate the probability of a candidate antigen peptide-MHC complex being recognized by TCR.

[0013] In a specific embodiment of the present invention, NetCTLpan is used to calculate the comprehensive prediction score of MHC presentation, antigen transporter transport efficiency and proteasome cleavage rate.

[0014] The present invention also provides a method for screening antigenic peptides, comprising the following steps: using the above-mentioned evaluation method to evaluate candidate antigenic peptides predicted by a machine algorithm, and ranking them based on the antigenic activity scoring function obtained by the evaluation;

[0015] The sorted candidate antigen peptides were validated by ELISpot in turn to obtain several peptides that can activate T cell responses, and the antigen-reactive T cells were sorted by IFNγ flow cytometry. After the corresponding antigen peptides that were positive for IFNγ flow cytometry were co-cultured with the organoid model for a period of time, the organoid samples were collected, embedded and sectioned, and Ki-67 immunohistochemically stained to screen for immunogenic antigen peptides.

[0016] In a specific embodiment of the present invention, the organoid model includes a patient-derived organoid model.

[0017] The present invention also provides antigenic peptides obtained by screening using the above screening method.

[0018] The present invention also provides a group of antigenic peptides having immunogenicity to glioblastoma obtained by screening using the above screening method.

[0019] In a specific embodiment of the present invention, the antigen peptide comprises an amino acid sequence as shown in any one of SEQ ID No.1 to SEQ ID No.14.

[0020] Beneficial effects: The present invention provides an evaluation method for candidate antigen peptides screened by conventional machine algorithms, which evaluates the candidate antigen peptides by performing peptide MHC binding affinity differences, immunogenicity calculations, the probability of candidate antigen peptide-MHC complexes being recognized by TCR, as well as antigen transporter protein (TAP) transport efficiency, proteasome cleavage and expression abundance, and selects verification based on the screening threshold of the verified data set.

[0021] The present invention also provides a method for screening antigenic peptides, including first performing machine algorithm prediction, evaluation of the above method, ELISpot verification, IFNγ flow sorting, and co-culturing with organoid models to further verify the immunogenicity of the new antigenic peptides, and finally embedding sections and Ki-67 immunohistochemical staining to screen for immunogenic antigenic peptides. After the above screening, the present invention screened 14 immunogenic new antigenic peptides from 21 glioblastoma patients. The screening method of the present invention will help to more accurately screen for new antigenic peptides with strong immunogenicity, improve the accuracy of screening of immunogenic new antigenic peptides, and will help improve the therapeutic efficacy of new antigens in glioblastoma patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is the result diagram of ELISpot validation;

[0023] Figure 2 This is a diagram of the validation results of the glioblastoma organoid model;

[0024] Figure 3 This is a graph showing the efficacy of PD-1 monoclonal antibody sensitization with new antigens;

[0025] Figure 4 In order to sort the T cells of the patient's PBMC after stimulation with the neoantigen peptide, IFN-γ and CD107a were selected for flow cytometric analysis of neoantigen-reactive T cells (Neo-T cells), in which Mutant: mutant peptide; Wildtype: wild-type peptide; Negative control: negative control group; Positive control: positive control group;

[0026] Figure 5 This is a flow cytometry result diagram of the changes in neoantigen-reactive T cells after the combination of neoantigen peptide and PD-1 monoclonal antibody;

[0027] Figure 6 The ELISpot results of candidate neoantigen peptides P06 (A), P19 (B), P20 (C) and P15 (D) patients; Mutant: Mutant peptide, mutant peptide; Wildtype: Wildtype peptide, wild-type peptide; Negative Control: negative control group, PBMC cell culture medium without any treatment; Positive Control: positive control group;

[0028] Figure 7Figure 1 shows the similarity of the organoid model to the parental tumor tissue and the retention of new antigen mutations. A: Correlation coefficient analysis shows that the new antigen-related mutations of each organoid are positively correlated with its matching tissue, but not with other tissues. The correlation is distributed from dark to light colors; B: The distribution of new antigen-related mutations predicted by WES analysis in tumors and matching organoids. Those that only exist in the parental tumor are represented by dark blue; those that only exist in the organoid are represented by light blue; and those that exist in both the patient's tumor and the organoid are represented by orange.

[0029] Figure 8 Figure 1 shows the results of the organoid model retaining the TMB characteristics of the parental tumor tissue. Figure A: TMB analysis of organoids and matched tumor tissues using WES data (n=21 pairs), TMB: Tumor Mutation Burden; B: TMB value distribution in patient-matched organoids and tumor tissues;

[0030] Fig. 9 The results of the organoid model retaining the characteristics of the parental tumor tissue. In the figure, A: The organoid model Ki-67 staining shows strong activity; B: On the basis of activity, HE staining shows that it retains characteristics such as parental tissue-specific nuclear morphology; C, D: Multi-color fluorescence, SOX2 OLIG2 DCX GFAP are all glioblastoma-specific markers, and the organoid models retain such characteristic markers. DETAILED DESCRIPTION

[0031] The present invention provides a method for evaluating candidate antigen peptides, comprising performing the following operations on the screened candidate antigen peptides respectively: (1) detecting the binding affinity between the candidate antigen peptides and MHC;

[0032] (2) detecting the immunogenicity of the candidate antigenic peptide;

[0033] (3) detecting the probability of candidate antigen peptide-MHC complex being recognized by TCR;

[0034] (4) a comprehensive prediction score that tests MHC presentation, transport efficiency of antigen transporters, and proteasome cleavage rate;

[0035] (5) Detecting the expression abundance of the candidate antigen peptide.

[0036] In a specific embodiment of the present invention, NetMHCpan is used to calculate the binding affinity of the wild-type peptide and mutant peptide of the candidate antigen peptide to MHC, and the differential affinity score is calculated. During the operation:

[0037]

[0038] In a specific embodiment of the present invention, the immunogenicity of the candidate antigen peptide is calculated using Vaxijen and Virus-immu. It is divided into two parts: the candidate antigen immunogenicity value (Vaxijen; VirusImmu) and the number of antigen support tools Toolsnum (0.5 and 0.4 are used as the immunogenicity thresholds, respectively, and those above the threshold are defined as new antigens, and the calculation results are 0, 1 and 2).

[0039] In a specific embodiment of the present invention, Neo Immune is used to calculate the probability of a candidate antigen peptide-MHC complex being recognized by TCR. For the candidate antigen peptide-MHC combination, a TCR sequence with a recognition score higher than 0.99 is selected, and the ratio to the total TCR is calculated as the recognition probability (TCRratio).

[0040] In a specific embodiment of the present invention, NetCTLpan is used to calculate the combined prediction score (Combined Prediction score; Comb) of MHC presentation, antigen transporter transport efficiency and proteasome cleavage rate.

[0041] In a specific embodiment of the present invention, the transcriptome analysis of tissue RNA is performed, the RNA sequencing data is aligned to the human reference genome, and the obtained counts are converted into expression abundance (TPM), and the TPM is required to be not less than 0.1.

[0042]

[0043] Where n is the total number of genes, L i is the length of gene i, N i is the count number of gene i.

[0044] After the above operation, the present invention further includes ranking the candidate antigen peptides based on the new antigen activity scoring function:

[0045] score=Comb*BAdai*TCRratio*(Toolsnum+e Vaxijen / 4+VirusImmu )

[0046] The present invention also provides a method for screening antigenic peptides, comprising the following steps: using the above-mentioned evaluation method to evaluate candidate antigenic peptides predicted by a machine algorithm, and ranking them based on the antigenic activity scoring function obtained by the evaluation;

[0047] The sorted candidate antigen peptides were validated by ELISpot in turn to obtain several peptides that can activate T cell responses, and the antigen-reactive T cells were sorted by IFNγ flow cytometry. After the corresponding antigen peptides that were positive for IFNγ flow cytometry were co-cultured with the organoid model for a period of time, the organoid samples were collected, embedded and sectioned, and Ki-67 immunohistochemically stained to screen for immunogenic antigen peptides.

[0048] In a specific embodiment of the present invention, candidate antigen peptides are predicted by machine algorithms, which may be WESdata-Somatic Mutation Calling, WESdata-HLAtyping, WESdata-Neoantigenprediction, TCGA&CGGA cohort, RNA expression level, and HLA frequency, etc.

[0049] In the specific operation of WESdata-Somatic Mutation Calling, FastQC (v.0.12.1) used default parameters to perform quality control analysis on preprocessed data, Trimmomatic (v.0.39) was used to filter low-quality reads and remove adapters, MINLEN was set to 75, and the rest of the parameters were default. The quality-controlled sequencing data were aligned with the human reference genome (hg19) by BWA, and the Blood sequencing data was used as a reference. MuTect2, Varscan, Muse, Somaticsniper, and Strelka were used to detect somatic mutations, filtered, and annovar was used for annotation. In order to obtain high-quality somatic mutations, the screening criteria were as follows: 1) the number of mutated reads in the tumor sample was greater than 1 and the allele frequency of the variant was not less than 0.05 (VAF ≥ 0.05); 2) the sequencing depth of the tumor tissue was greater than or equal to 15 (DP ≥ 15); 3) at least two tools supported the mutation. The filtered somatic annotation was used for subsequent analysis.

[0050] When performing WESdata-HLA typing, in one embodiment, Polysolver is used to predict HLA haplotypes based on blood WES sequencing data.

[0051] When performing WESdata-Neoantigenprediction, in one embodiment, peptides were constructed by sliding windows on the affected mutation positions based on the annotated VCF files, and all 9 polymerized peptides were generated for nonsynonymous somatic mutations (nonsynonymous SNVs). The binding affinity of polymerized peptides related to the patient's HLA-I alleles was predicted by netMHCpan-4.0, and high affinity binders were defined as IC 50Binders with affinity equal to or less than 500nM. Peptides with affinity of mutant peptides <500nM and affinity of corresponding wildtype peptides greater than 500nM were used as candidate antigens (CandidatedAntigen) for subsequent analysis. The immunogenicity scores of 9-mer peptides were calculated. The main tools included VaxiJen, Virus-immu, and Neo Immune, a deep learning-based T cell activation-related antigen identification method. Vaxijen scores greater than 0.5 were defined as "Antigen", and Virus-immu scores greater than 0.4 were defined as "Antigen". Neo Immune calculated the score of peptide-HLA pairs recognized by TCR sequences. The higher the score, the greater the probability of being recognized by TCR. TCR sequences with scores higher than 0.99 were defined, and their proportion of total TCR sequences was calculated as TCRratio. NetCTLpan calculated the comprehensive prediction score of MHC presentation, antigen transporter transport efficiency, and proteasome cleavage rate. Finally, a scoring model was constructed to score and screen candidate antigen-HLA.

[0052] score=Comb*BAdai*TCRratio*(Toolsnum+e Vaxijen / 4+VirusImmu ))

[0053] TCRratio: TCR binding frequency calculated by Neo Immune, Toolsnum: the number of tools with immune scores above the threshold (0, 1, 2), Vaxijen and VirusImmu represent the calculated immunogenicity values, respectively; BAdai: the difference in affinity scores between mutant peptides / wild peptides and corresponding HLA; Comb: the comprehensive prediction score of candidate antigens and HLA calculated by NetCTLpan.

[0054] Screening criteria: 1) Mutation-related antigens appear in both sample tumor tissue and organoids; 2) The expression level of the corresponding mutant gene in the organoid sample is not less than 0.1.

[0055] When conducting the TCGA&CGGA cohort, in a specific embodiment of the present invention, TCGA-GBM mutation-related data were downloaded from GDC in maf format, and primary tumor samples (01A) and IDH wildtype samples were screened, with a total of 296 samples for subsequent analysis. IDHwildtype and primary GBM samples were screened in the CGGA database for subsequent mutation calling, with a total of 42 samples for subsequent analysis. The mutation calling method is the same as above: MuTect2, Varscan, Muse, Somaticsniper, Strelka are used to detect somatic mutations, filtered and annotated using annovar. The screening criteria are as above. R language maftools is used for visualization of sample mutations and calculation of TMB.

[0056] When RNA expression is performed, in a specific embodiment of the present invention, FastQC is used to perform quality control analysis on the preprocessed data, and Trimmomatic is used to filter low-quality reads and remove adapters. The quality-controlled sequencing data is aligned with the human reference genome (hg19) by hista2, and the htseq-count software obtains the number of reads aligned to protein-coding genes in each sample, and the MINAQUAL parameter a is set to 8, stranded is set to no, and the remaining parameters are default.

[0057] When verifying the HLA frequency, in a specific embodiment of the present invention, the HLA allele distribution (HLA-1, HLA-B, HLA-C) from the AFND (http: / / allelefrequencies.net) database and the TCGA healthy samples are used, and the distribution frequency of HLA in the CGGA database is calculated. HLA alleles that meet AF>1% are defined as commonHLA for subsequent analysis.

[0058] The present invention evaluates and optimizes the candidate antigen peptides obtained based on the above machine screening. The evaluation method is the same as above and will not be repeated here.

[0059] The present invention performs ELISpot verification on the candidate antigen peptides obtained by evaluation and obtains peptides that can activate T cell responses; then the new antigen-reactive T cells are sorted by IFNγ flow cytometry, and then further co-cultured with the organoid model. After 3 days, the organoid samples are collected, embedded and sliced, and Ki-67 immunohistochemically stained to evaluate the killing efficacy. Finally, an ideal immunogenic new antigen peptide that can be used for clinical treatment is obtained. In a specific embodiment of the present invention, the organoid model includes a patient-derived organoid model, and a glioblastoma patient-derived organoid model is constructed in the embodiment, thereby screening antigenic peptides that are immunogenic to toe blastoma.

[0060] The present invention also provides antigenic peptides obtained by screening using the above screening method.

[0061] The present invention also provides a group of antigenic peptides having immunogenicity to glioblastoma obtained by screening using the above screening method.

[0062] In a specific embodiment of the present invention, the antigen peptide comprises an amino acid sequence as shown in any one of SEQ ID No.1 to SEQ ID No.14.

[0063] In order to further illustrate the present invention, the antigen peptide screening method and evaluation method based on immunogenicity provided by the present invention are described in detail below in conjunction with the examples, but they should not be construed as limiting the scope of protection of the present invention.

[0064] Example 1

[0065] After obtaining tumor tissue and blood samples from patients in the operating room, whole exome and transcriptome sequencing was performed;

[0066] After obtaining the sequencing data, algorithm analysis begins to predict new antigen peptides.

[0067] 1. Algorithm for preliminary prediction and screening of new antigen peptides

[0068] (1)WESdata-Somatic Mutation Calling

[0069] FastQC was used to perform quality control analysis on the preprocessed data, and Trimmomatic was used to filter low-quality reads and remove adapters. The quality-controlled sequencing data was aligned with the human reference genome (hg19) by BWA, and the Blood sequencing data was used as a reference. MuTect2, Varscan, Muse, Somaticsniper, and Strelka were used to detect somatic mutations, filtered, and annotated using annovar. In order to obtain high-quality somatic mutations, the screening criteria were as follows: 1) the number of mutated reads in the tumor sample was greater than 1 and the allele frequency of the variant was not less than 0.05 (VAF≥0.05); 2) the sequencing depth of the tumor tissue was greater than or equal to 15 (DP≥15); 3) at least two tools supported the mutation. The filtered somatic annotation was used for subsequent analysis.

[0070] (2)WESdata-HLAtyping

[0071] Polysolver was used to predict HLA haplotypes based on blood WES sequencing data.

[0072] (3)WESdata-Neoantigenprediction

[0073] Based on the annotated VCF files, peptides were constructed by sliding windows on the affected mutation positions, and all 9 polymerized peptides were generated for nonsynonymous somatic mutations (nonsynonymous SNVs). The binding affinity of polymerized peptides related to the patient's HLA-I alleles was predicted by netMHCpan-4.0. Peptides with a mutant peptide affinity <500nM and a corresponding wildtype peptide affinity greater than 500nM were used as candidate antigens for subsequent analysis.

[0074] Calculate the immunogenicity score of the 9-mer peptide. The main tools include VaxiJen, Virus-immu and Neo Immune, a deep learning-based T cell activation-related antigen identification method. Vaxijen scores greater than 0.5 are defined as "Antigen", and Virus-immu scores greater than 0.4 are defined as "Antigen". Neo Immune calculates the score of peptide-HLA pairs recognized by TCR sequences. The higher the score, the greater the probability of being recognized by TCR. Define TCR sequences with scores higher than 0.99, and calculate their proportion of total TCR sequences as TCRratio. NetCTLpan calculates the comprehensive prediction score of MHC presentation, antigen transporter transport efficiency and proteasome cleavage rate. Finally, a scoring model is constructed to score and screen candidate antigen-HLA.

[0075] score=Comb*BAdai*TCRratio*(Toolsnum+e Vaxijen / 4+VirusImmu ))

[0076] TCRratio: TCR binding frequency calculated by NeoImmune, Toolsnum: the number of tools with immune scores above the threshold (0, 1, 2), Vaxijen and VirusImmu represent the calculated immunogenicity values, respectively; BAdai: the difference in affinity scores between mutant peptides / wild peptides and corresponding HLA; Comb: the comprehensive prediction score of candidate antigens and HLA calculated by NetCTLpan.

[0077] Screening criteria: 1) Mutation-related antigens appear in both sample tumor tissue and organoids; 2) The expression level of the corresponding mutant gene in the organoid sample is not less than 0.1.

[0078] (4)TCGA&CGGA cohort

[0079] TCGA-GBM mutation-related data were downloaded from GDC in maf format, and primary tumor samples (01A) and IDHwildtype samples were screened, with a total of 296 samples used for subsequent analysis.

[0080] IDHwildtype and primary GBM samples were screened in the CGGA database for subsequent mutation calling, and a total of 42 samples were used for subsequent analysis. The mutation calling method was the same as above: MuTect2, Varscan, Muse, Somaticsniper, Strelka were used to detect somatic mutations, filtered and annotated using annovar. The screening criteria were the same as above.

[0081] R language maftools was used to visualize sample mutations and calculate TMB.

[0082] (5) RNA expression

[0083] FastQC was used to perform quality control analysis on the preprocessed data, and Trimmomatic was used to filter low-quality reads and remove adapters. The quality-controlled sequencing data was aligned with the human reference genome (hg19) using hista2, and the htseq-count software was used to obtain the number of reads aligned to protein-coding genes in each sample.

[0084] (6) HLA frequency

[0085] The HLA allele distribution (HLA-1, HLA-B, HLA-C) from the AFND (http: / / allelefrequencies.net) database and TCGA healthy samples was used to calculate the HLA distribution frequency in the CGGA database. HLA alleles that met AF>1% were defined as commonHLA for subsequent analysis.

[0086] 2. Peptide evaluation and optimization

[0087] (1) Peptide MHC binding affinity difference: The binding affinity difference score between the wild-type peptide and the mutant peptide. The affinity score was calculated by NetMHCpan;

[0088] (2) The probability of peptide MHC being recognized by TCR: Vaxijen and Virus-immu calculate the immunogenicity of the peptide, and NeoImmune calculates the probability of the peptide-MHC complex being recognized by TCR;

[0089] (3) NetCTLpan calculates a comprehensive prediction score for MHC presentation, antigen transporter transport efficiency, and proteasome cleavage rate;

[0090] (4) Expression abundance: TPM not less than 0.1.

[0091] Neoantigen ranking based on neoantigen activity scoring function: Neoantigens are ranked by neoantigen activity scoring function. And validation is selected based on the threshold of the validated dataset.

[0092] 3. Isolation and extraction of peripheral blood mononuclear cells (PBMC) from patients

[0093] (1) Material preparation: heparin anticoagulant tube, peripheral blood (20 ml for example), PBS, lymphocyte separation solution, buffer Easysep buffer (STEMCELL), red blood cell lysis solution (Solarbio), 50 ml centrifuge tube, funnel centrifuge tube, 3 10 ml pipettes, pipette gun, PBMC culture medium (RPMI-1640 culture medium, 100 U / ml IL-2, 10% FBS, 1% double antibody), PBMC freezing solution (90% RPMI-1640 culture medium, 10% FBS), 6-well plate. The above consumables should be placed in the clean bench for ultraviolet irradiation half an hour in advance;

[0094] (2) Transfer 20 ml of PBS to a 50 ml sterile centrifuge tube;

[0095] (3) Use a pipette to transfer the peripheral blood to a sterile 50ml centrifuge tube, rinse the blood collection tube with 10ml PBS, and transfer the rinse solution to a 50ml centrifuge tube. Perform the same operation for the second tube of blood. Centrifuge tube system: 20ml blood + 20ml PBS rinse solution;

[0096] (4) Use a sterile syringe to draw 20 ml of lymphocyte separation fluid and inject it into a 50 ml lymphocyte separation tube until the liquid level is flush with the funnel (15 ml);

[0097] (5) Use a pipette (set the pipette to the lowest setting) to transfer the blood diluent into the lymphocyte separation tube. Keep the pipette tip about 1 cm above the liquid surface and slowly add the blood diluent along the tube wall. Add 20 ml of blood diluent to each lymphocyte separation tube. A clear dividing line can be seen between the blood and lymphocyte separation fluid.

[0098] (6) Place the lymphocyte separation tube in a centrifuge at 2500r / min for 20min and take the supernatant. Slowly pour the supernatant into a blank 50ml centrifuge tube and stop when the sediment (red blood cells) at the bottom of the funnel centrifuge tube tends to pour out. Dilute the supernatant with PBS 1:1;

[0099] (7) Centrifugation at 1500 r / min for 15 min, discard the supernatant, and collect the cell pellet to obtain peripheral blood mononuclear cells;

[0100] (8) Resuspend each tube of cell pellet with 2 ml of red blood cell lysis buffer, gently blow evenly, and let stand for 3 minutes. After 3 minutes, mix the two tubes of cell suspension into one tube and add PBS to 45 ml. Incubate at 1500 r / min for 15 minutes, discard the supernatant, and obtain a relatively pure peripheral blood mononuclear cell pellet;

[0101] (9) Resuspend in 5 ml PBMC medium and count the cells. Add medium according to the number of cells until the cell concentration reaches 1×10 6 cells / ml, 2-3 ml of cell suspension per well, and culture in an incubator;

[0102] (10) After culturing for about 6 h, observe the morphology of PBMCs under a light microscope, place the cells in a centrifuge tube in a clean bench, and centrifuge at 1500 r / min for 10 min.

[0103] (11) Discard the supernatant, take 1 ml of PBMC freezing solution, resuspend the cells thoroughly, place them in a cell freezing tube, label them, and place them in liquid nitrogen for long-term freezing.

[0104] 4. PBMC-peptide co-culture

[0105] (1) After the patient's PBMCs were revived, the cryopreserved tubes were placed in a 37°C constant temperature water bath for 5 min;

[0106] (2) Rewarm the PBMC culture medium, transfer the cell suspension to a 50 ml centrifuge tube with a pipette, add 15 ml of PBMC culture medium, and centrifuge at 1200 r / min for 5 min;

[0107] (3) Discard the supernatant, add 1 ml of culture medium, resuspend thoroughly, and count the cells;

[0108] (4) Take 5×10 5 PBMCs were placed in a 24-well plate, and 500 μL of culture medium was added to resuspend and culture the cells;

[0109] (5) The peptide solution was thawed at room temperature, and 5 μL (20 μg / ml) of wild-type and mutant peptide solution were added to the culture system. The well plate surface was labeled and the 24-well plate was placed at 37°C and 5% CO. 2 and 90% humidity in a sterile incubator;

[0110] (6) Every 3 days, 5 μL of peptide solution was repeatedly administered for stimulation, and the culture medium was supplemented appropriately. After 2 to 3 cycles of stimulation, cell samples were collected for ELISpot experiments.

[0111] 5. ELISpot experiment

[0112] (1) Material preparation: RPMI-1640 medium, PBMC medium (as described above), PHA, IFN-γ ELISpot kit (Mabtech), ELISpot PVDF 96-well plate (Mabtech), AEC color development solution (Dakoway), 35% alcohol, PBS, fetal bovine serum (FBS, Gibco), sample gun, sample tank;

[0113] (2) ELISpot plate coating: 35% alcohol is placed in the sample tank, 20 μL / well is taken out with a pipette, added to the PVDF 96-well plate, incubated for 50 seconds, and the alcohol is removed; PBS is taken out with a pipette, 200 μL / well, and the plate is washed, and repeated 5 times; diluted capture mAb (15 μg / ml) is taken, 100 μL / well, tightly covered, and incubated at 4°C overnight;

[0114] (3) Cell incubation: remove the liquid in the well plate, add 200 μL / well of PBS, wash the plate, repeat 5 times; add 200 μL / well of culture medium, block, and incubate at room temperature for at least 30 min; fully resuspend the PBMC cells stimulated by the above-mentioned peptides, take 100 μL / well and place them in a PVDF 96-well plate, with 3 replicates for each group; set up the control group, add 5 μL / well of PHA to the culture system of the positive control group, and only add the cell suspension to the negative control group, with 3 replicates for each group; the PVDF 96-well plate was kept at 37°C and 5% CO 2 Incubate in a sterile incubator with 90% humidity for 24 to 48 hours;

[0115] (4) ELISA: 200 μL / well of PBS, wash the plate, repeat 5 times; add detection antibody (7-B6-1 biotin, 1 μg / ml, dissolved in PBS containing 0.5% FBS), incubate at room temperature for 2 h; 200 μL / well of PBS, wash the plate 5 times; add 100 μL / well of streptavidin-HRP (1:100 dilution), incubate at room temperature for 1 h; 200 μL / well of PSB, wash the plate 5 times; 200 μL / well of PBS, wash the plate 5 times; add 100 μL of AEC colorimetric solution, protect from light, incubate at 37°C incubator, observe every 5 min, discard the colorimetric solution after spots appear, add 200 μL / well of PBS, wash the plate, and stop color development;

[0116] (5) The PVDF plate was placed in a dry, light-proof environment and imaged in an AID multi-mode microplate imaging analyzer (enzyme-linked spot analyzer) after 24 hours.

[0117] 6. Construction, culture and passage of patient-derived organoid models

[0118] (1) Preparation of materials: sterile culture dishes, sterile microscissors, forceps, PBS, Normocin, organoid culture system (DMEM:F12, 50% neurobasal, 1% GlutaMax, 1% NEAAs, 1% PenStrep, 1% N2, 1X B27, all reagents purchased from Gibco and Thermo Fisher Scientific), six-well plates, 50 ml centrifuge tubes. The above materials were placed in a clean bench and exposed to ultraviolet light for 30 min.

[0119] (2) Fresh tumor tissue was obtained from the operating room, placed in PBS, and transported and stored on ice;

[0120] (3) Take 15 ml PBS + 150 μL Normocin in a sterile culture dish and thoroughly rinse the tumor tissue;

[0121] (4) Take 15 ml of PBS in a sterile culture dish and rinse the tumor tissue again. Repeat three times;

[0122] (5) Place 5-10 ml of PBS in a sterile culture dish so that the tumor tissue can be immersed in it, and use sterile microscissors to cut the tumor tissue into pieces with a diameter of about 1 mm;

[0123] (6) Place the minced tumor tissue in a centrifuge tube, add PBS to 15 ml, and centrifuge at 800 rpm for 5 min;

[0124] (7) Discard the supernatant, resuspend the minced tissue in culture medium, place in a six-well plate, and incubate at 37°C, 5% CO 2 The cells were cultured in a shaker (120 rpm) in a sterile incubator with 90% humidity, observed and the medium was changed daily. Within 2 to 3 weeks, the tumor mass generally formed a round organoid;

[0125] (8) When the organoids grow to 3 mm or larger under light microscopy and naked eye, take the organoids out of the clean bench and use sterile microscissors to cut them into pieces with a diameter less than 1 mm. Repeat steps (6)-(7) to achieve organoid subculture.

[0126] 7. T cell flow cytometry

[0127] (1) Extract PBMC according to step 1.2, add candidate neoantigen peptides, and repeat stimulation for 2 to 3 cycles;

[0128] (2) Collect and wash the cell pellet, about 1×10 7 40 μL of buffer (Miltenyi) was added and thoroughly blown to prepare a single-cell suspension;

[0129] (3) Add 10 μL of biotin-labeled CD8+ (Miltenyi) T cell combination antibody to every 40 μL of single cell suspension, mix thoroughly and incubate at 4°C for 5 min;

[0130] (4) Add 30 μL of buffer and mix well, add 20 μL of CD8+ T cell combination magnet (Miltenyi), mix well and incubate at 4°C for 10 min;

[0131] (5) Add 500 μL buffer and mix well before magnetic bead screening. Wash the Miltenyi column with 3 ml buffer and place it in the MACS separation magnetic field.

[0132] (6) Place the cell suspension in a sorting column to collect unlabeled passing cells, which represent CD8+ T cells;

[0133] (7) Rinse the column with 3 ml of buffer, collect the unlabeled cells that pass through, representing CD8+ T cells, and mix them with the cells in step (5) for subsequent experiments.

[0134] 8. Immunofluorescence staining

[0135] (1) Fix the organoids with 1 ml of 4% paraformaldehyde (PFA) at room temperature for 20 minutes, and fix with PFA overnight; dehydrate with 10%, 20%, and 30% sucrose gradient until the organoids sink to the bottom. Each step of dehydrating the organoids takes about 4 hours; embed with OCT, and for organoids, place 5 organoids in each mold; for intraoperative tumor tissue, place it on the sample tray, cover it with OCT, and place the mold flat in a -80℃ refrigerator for 30 minutes; slice with an ice cutter, trim the slices to 20μm thickness, and slice 5μm; before staining, fully dry at room temperature for 1 hour, and store the remaining slices at -20℃;

[0136] (2) Draw circles with a hydrophobic pen, block with PFA for 10 min, wash with PBS, repair with ice-cut antigen repair solution at room temperature for 5 min, and wash with PBS;

[0137] (3) Use QuickBlock immunostaining blocking solution (Beyotime) for 15-20 min and wash with PBS;

[0138] (4) Autofluorescence quencher (biotium), 5 min, washed 3 times with PBS;

[0139] (5) Antibody dilution solution 1:200-1:400 with primary antibody, 4°C overnight;

[0140] (6) Wash with PBS three times, dilute with fluorescent secondary antibody at 1:400, and incubate at room temperature for 1 h;

[0141] (7) Wash with PBS three times, wipe the PBS on the slide with absorbent paper, and wipe the cover glass with dust-free paper;

[0142] (8) Add a drop of sealing medium (containing DPAI) to each slide (no bubbles), seal tightly with a coverslip, and remove bubbles;

[0143] (9) Take photos under a fluorescence microscope with a 20x objective lens.

[0144] 9. Flow cytometry analysis

[0145] (1) Collect and wash the cell precipitate, which should be about 1 x 105 cells or visible to the naked eye in the EP tube.

[0146] (2) Incubate with direct labeled flow cytometry antibodies at 4°C for 30 minutes and wash with PBS. The flow cytometry antibodies used in this experiment are CD107a / LAMP-1 Antibody (Elabscience) APC, IFN-γ Antibody (Elabscience) FITC.

[0147] (3) Resuspend the pellet in 400ul PBS and load onto the machine. Repeat each sample 3 times with 10,000 cells for each data point. The main in vitro test results should be repeated 2-3 times.

[0148] The present invention screened 14 immunogenic new antigenic peptides from 21 patients, and the ELISpot positive results and organoid validation results were as follows: Figures 1-2 As shown, in order to further verify the immunogenicity of the peptide, the present invention continued to sort out the neoantigen-reactive T cells and perform flow cytometry detection. CD107a is a surrogate marker of cytotoxic activity expressed by T cells, which can more accurately reflect the functional status of T cells; and IFN-γ, as a cytokine for T cells to kill tumors, has been widely verified to be an important factor. The present invention found that after stimulation with immunogenic neoantigen peptides, T cells expressing both CD107a and IFN-γ showed an upward trend ( Figure 4 At the same time, it can be observed that for peptide No. 15, which showed a strong positive result in ELISpot, the proportion of active T cells in its flow cytometry results was also relatively higher, which further supplemented the ELISpot results and proved the immunogenicity of the new antigen peptide. Similarly, the present invention continued to verify this part of the results again by flow cytometry, and the results were as follows Figure 5 As shown in the figure, after combined use of PD-1 monoclonal antibody, the number of T cells expressing both CD107a and IFN-γ increased significantly compared with the group stimulated with single neoantigen peptide ( Figure 5 AB), further demonstrating the synergistic effect of immune checkpoint inhibitors on neoantigen therapy.

[0149] The ELISpot results of other peptides are shown below: Figure 6 As shown, the present invention conducted a correlation analysis of new antigen-related mutations in organoids and tumor tissues, and the results are as follows Figure 7 As shown, most of the 21 pairs of matched samples show strong correlation ( Figure 7 The overall distribution of neoantigens in organoids and their parental tumor tissues is also relatively ideal. Most neoantigen-related mutations can be present in both organoids and parental tumors, with the highest rate reaching more than 75% ( Figure 7 Middle B). The role of tumor mutation burden (TMB) in GBM has been widely studied. TMB is believed to be closely related to the generation of new antigens. Tumors with high TMB may produce more new antigens and are related to the enhancement of the body's immune response and the efficacy of immune checkpoint inhibitors.

[0150] As an important feature related to the expression of tumor neoantigens, the present invention continues to analyze the similarity between organoids and parental tumor tissues in terms of TMB. Figure 8 As shown in Figure A, there is no significant difference in TMB between organoids and matched tumor tissues. At the same time, the present invention also plotted the distribution of TMB in each pair of samples, and finally found that the change trend of TMB was relatively consistent ( Figure 8 (middle B).

[0151] Histological results Fig. 9 As shown in Table 1, the histological level proves that the organoid model can retain the characteristics of the parental tumor tissue, proving that it can be used for further efficacy verification. The peptide structure and the HLA molecules that can be bound are shown in Table 1.

[0152] Table 1 Immunogenic peptides

[0153]

[0154]

[0155] In addition, it is worth noting that in the process of using the organoid model for verification, it was found that not all peptides verified by ELISpot can stimulate T cells to achieve strong immune killing efficacy. Figure 3As shown in the figure, the T cells stimulated by peptides 79 and 60 can kill organoids to a certain extent, but the killing efficiency achieved is not very strong. On this basis, the present invention adopts a method of combining PD-1 monoclonal antibody, that is, co-culturing the organoid model with neoantigen-reactive T cells and PD-1 monoclonal antibody, and collecting the organoids after 3 days; fixing and embedding the slices, and staining with Ki67. It was found that a more significant killing efficiency was finally achieved. Therefore, the application of organoid models that can reflect the microenvironment of glioblastoma has certain positive significance and can bring direct and effective guiding significance to clinical applications.

[0156] The patient's PBMC stimulated with the neoantigen peptide was sorted by T cell flow cytometry, and IFN-γ and CD107a were selected for flow cytometric analysis of neoantigen-reactive T cells (Neo-T cells). The results are as follows: Figure 4 As shown in the figure, after stimulation with the immunogenic peptide, the number of T cells expressing IFN-γ and CD107a increased significantly, and the results were statistically significant. After the combination of the new antigen peptide and PD-1 monoclonal antibody, the changes in the new antigen-reactive T cells were detected by flow cytometry, and the results were as follows: Figure 5 shown.

[0157] The results of candidate new antigen peptide ELISpot are as follows Figure 6 As shown, the unpaired sample T test was used, and the p value was tested using the log-rank method. In this result, there was a statistical difference in the number of IFNγ positive points in the wild-type peptide group and the mutant peptide group. The similarity of the organoid model to the parental tumor tissue and the retention of the new antigen mutation are shown in Figure 7 As shown in Figure 2, the organoid model retains the TMB characteristics of the parental tumor tissue. Figure 8 As shown in Figure 2, the organoid model retains the characteristics of the parental tumor tissue. Fig. 9 As shown, it was demonstrated at the histological level that the organoid model can retain the characteristics of the parental tumor tissue, proving that it can be used for further efficacy verification.

[0158] Although the above embodiment describes the present invention in detail, it is only a part of the embodiments of the present invention, not all of the embodiments. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A method for evaluating candidate antigen peptides, characterized in that: The method comprises performing the following operations on the screened candidate antigen peptides respectively: (1) detecting the binding affinity between the candidate antigen peptides and MHC; (2) detecting the immunogenicity of the candidate antigenic peptide; (3) detecting the probability of candidate antigen peptide-MHC complex being recognized by TCR; (4) a comprehensive prediction score that tests MHC presentation, transport efficiency of antigen transporters, and proteasome cleavage rate; (5) Detecting the expression abundance of the candidate antigen peptide.

2. The evaluation method according to claim 1, characterized in that: This includes using NetMHCpan to calculate the difference scores in binding affinity between the wild-type peptide and mutant peptide of the candidate antigen peptide and MHC.

3. The evaluation method according to claim 1, characterized in that: The immunogenicity of the candidate antigen peptide is calculated using Vaxijen and Virus-immu.

4. The evaluation method according to claim 1, characterized in that: This includes using Neo Immune to calculate the probability that a candidate antigen peptide-MHC complex will be recognized by TCR.

5. The evaluation method according to claim 1, characterized in that: This includes the use of NetCTLpan to calculate a comprehensive prediction score for MHC presentation, antigen transporter transport efficiency, and proteasome cleavage rate.

6. A method for screening antigenic peptides, characterized in that: The following steps are involved: Using the evaluation method described in any one of claims 1 to 5 to evaluate the candidate antigen peptides predicted by the machine algorithm, and ranking them based on the antigen activity scoring function obtained by the evaluation; The sorted candidate antigen peptides were validated by ELISpot in turn to obtain several peptides that can activate T cell responses, and the antigen-reactive T cells were sorted by IFNγ flow cytometry. After the corresponding antigen peptides that were positive for IFNγ flow cytometry were co-cultured with the organoid model for a period of time, the organoid samples were collected, embedded and sectioned, and Ki-67 immunohistochemically stained to screen for immunogenic antigen peptides.

7. The screening method according to claim 6, characterized in that: The organoid models include patient-derived organoid models.

8. An antigenic peptide obtained by screening using the screening method according to claim 6 or 7.

9. A group of antigenic peptides having immunogenicity to glioblastoma obtained by screening using the screening method of claim 6 or 7.

10. The antigen peptide according to claim 9, characterized in that: The antigen peptide comprises any one of the amino acid sequences shown in SEQ ID No. 1 to SEQ ID No. 14.

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