Immunogenicity-based antigen peptide screening and evaluation methods

Abstract

The application provides an immunogenicity-based antigen peptide screening method and evaluation method, and belongs to the technical field of immunology.The application provides an evaluation method for candidate antigen peptides screened by a conventional machine algorithm, wherein the candidate antigen peptides are evaluated by polypeptide MHC binding affinity difference, immunogenicity calculation, probability of the candidate antigen peptide-MHC complex being recognized by a TCR, and antigen transporter (TAP) transport efficiency, proteasome cleavage and expression abundance.The application also provides an antigen peptide screening method, which comprises machine algorithm prediction, the above evaluation, ELISpot verification, IFN gamma flow sorting and co-culture with an organoid model, and finally embedding sectioning and Ki-67 immunohistochemical staining to screen immunogenic antigen peptides.The above screening will help to more accurately screen strong immunogenic neoantigen peptides.

Description

Technical Field

[0001] This invention relates to the field of immunology technology, specifically to immunogenicity-based antigen peptide screening and evaluation methods. Background Technology

[0002] Currently, the screening of neoantigen peptides mainly relies on machine learning algorithms and enzyme-linked immunospot (ELISpot) technology. Machine learning algorithms primarily include HLA binding affinity prediction and immunogenicity prediction. For HLA binding affinity prediction, there are relatively well-established algorithms. Immunogenicity refers to the ability of a peptide to activate T cells and further kill tumors after binding to HLA molecules. However, there is currently no universally accepted algorithm for this important peptide property; therefore, the evaluation and optimization of candidate neoantigen peptides is an important research direction.

[0003] ELISpot technology is currently almost the only in vitro method for validating the immunogenicity of neoantigen peptides. It verifies the activation ability of neoantigen peptides on T cells at the cellular level in vitro. However, this technology neglects the influence of the tumor immune microenvironment on immunogenicity, and the immune microenvironment of glioblastoma has a profound impact on the efficacy of immunotherapy. Therefore, it is necessary to propose a more accurate in vitro validation model. Summary of the Invention

[0004] This invention provides an immunogenic antigen peptide screening and evaluation method, which, after evaluation and screening, can yield immunogenic neoantigen peptides that can be applied to clinical treatment.

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

[0006] (2) Detect the immunogenicity of the candidate antigen peptide;

[0007] (3) Detect the probability that the candidate antigen peptide-MHC complex is recognized by the TCR;

[0008] (4) Detect the comprehensive predictive score of MHC presentation, antigen transport efficiency and proteasome cleavage rate.

[0009] (5) Detect the expression abundance of the candidate antigen peptides.

[0010] In one specific embodiment of the present invention, the method includes using NetMHCpan to calculate the difference score of binding affinity between wild-type peptides and mutant peptides of candidate antigen peptides and MHC.

[0011] In one specific embodiment of the present invention, the immunogenicity of the candidate antigen peptide is calculated using Vaxijen and Virus-immu.

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

[0013] In one specific embodiment of the present invention, a comprehensive predictive score is calculated using NetCTLpan to assess MHC presentation, antigen transporter efficiency, and proteasome cleavage rate.

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

[0015] The candidate antigen peptides were sequentially validated by ELISpot to obtain several peptides that could activate T cell responses. Antigen-reactive T cells were then sorted by IFNγ flow cytometry. The corresponding antigen peptides that were positive by IFNγ flow cytometry were co-cultured with organoid models for a period of time. The organoid samples were then collected, embedded, sectioned, and stained with Ki-67 immunohistochemistry to screen for immunogenic antigen peptides.

[0016] In one 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-described screening method.

[0018] The present invention also provides a group of antigenic peptides that are immunogenic to glioblastoma, obtained by screening using the above-described screening method.

[0019] In one specific embodiment of the present invention, the antigenic peptide comprises any one of the amino acid sequences shown in SEQ ID No. 1 to SEQ ID No. 14.

[0020] Beneficial effects: This invention provides an evaluation method for candidate antigen peptides obtained by conventional machine algorithms. The method evaluates candidate antigen peptides by assessing differences in peptide MHC binding affinity, immunogenicity calculation, the probability of TCR recognition of candidate antigen peptide-MHC complex, antigen transporter (TAP) transport efficiency, proteasome cleavage, and expression abundance. Validation is then selected based on a validation dataset screening threshold.

[0021] This invention also provides a method for screening antigenic peptides, including first performing machine algorithm prediction, evaluation of the above methods, ELISpot validation, IFNγ flow cytometry sorting, and co-culturing with organoid models to further verify the immunogenicity of neoantigen peptides. Finally, embedding sections and Ki-67 immunohistochemical staining are performed to screen for immunogenic antigenic peptides. After the above screening, this invention screened 14 immunogenic neoantigen peptides from 21 glioblastoma patients. The screening method described in this invention will help to more accurately screen for highly immunogenic neoantigen peptides, improve the accuracy of immunogenic neoantigen peptide screening, and help improve the efficacy of neoantigen therapy for glioblastoma patients. Attached Figure Description

[0022] Figure 1 Figure showing the validation results for ELISpot;

[0023] Figure 2 Figure showing the validation results of the glioblastoma organoid model;

[0024] Figure 3 The graph shows the therapeutic effects of PD-1 monoclonal antibody sensitization to neoantigen.

[0025] Figure 4 To perform T cell flow cytometry sorting on PBMCs of patients stimulated with neoantigen peptides, the following graph shows the results of flow cytometry analysis of neoantigen-reactive T cells (Neo-T cells) using IFN-γ and CD107a. In the graph, Mutant represents mutant peptides; Wildtype represents wild-type peptides; Negative control represents negative control group; and Positive control represents positive control group.

[0026] Figure 5 This is a flow cytometry result showing changes in neoantigen-responsive T cells after combined use of neoantigen peptide and PD-1 monoclonal antibody.

[0027] Figure 6 The ELISpot results for patients with candidate neoantigen peptides P06(A), P19(B), P20(C), and P15(D) are shown in the figure. Mutant: Mutant peptide, mutant peptide; Wildtype: Wildtype peptide, wild-type peptide; Negative Control: Negative control group, PBMC cell culture medium was not treated in any way; Positive Control: Positive control group.

[0028] Figure 7The figure shows the similarity of organoid models to parental tumor tissues and the retention of neoantigen mutations. In the figure, A: Correlation coefficient analysis shows that the neoantigen-related mutations of each organoid are positively correlated with their matched tissues, but not with other tissues. The distribution of correlation from dark to light indicates that it is high to low. B: Distribution of neoantigen-related mutations predicted by WES analysis in tumors and matched organoids. Those present only in parental tumors are represented by dark blue; those present only in organoids are represented by light blue; and those present in both patient tumors and organoids are represented by orange.

[0029] Figure 8 The image shows the retention results of TMB features of parental tumor tissue in organoid models. In the image, A: TMB analysis of organoids and matched tumor tissues using WES data (n=21 pairs), TMB: Tumor Mutation Burden; B: Distribution of TMB values ​​in patient-matched organoids and tumor tissues.

[0030] Figure 9 The figure shows the retention of parental tumor tissue characteristics by organoid models. In the figure, A: the organoid model is stained with Ki-67 and has strong activity; B: on the basis of activity, HE staining shows that it retains the specific nuclear morphology and other characteristics of the parental tissue; C, D: multicolor fluorescence, SOX2, OLIG2, DCX, and GFAP are all glioblastoma-specific markers, and the organoid models have retained these characteristic markers. Detailed Implementation

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

[0032] (2) Detect the immunogenicity of the candidate antigen peptide;

[0033] (3) Detect the probability that the candidate antigen peptide-MHC complex is recognized by the TCR;

[0034] (4) Detect the comprehensive predictive score of MHC presentation, antigen transport efficiency and proteasome cleavage rate.

[0035] (5) Detect the expression abundance of the candidate antigen peptides.

[0036] In one specific embodiment of the present invention, the binding affinity of wild-type and mutant peptides of candidate antigen peptides to MHC is calculated using NetMHCpan, and differential affinity scores are calculated. During operation:

[0037]

[0038] In one specific embodiment of the present invention, the immunogenicity of the candidate antigen peptide is calculated using Vaxijen and VirusImmu. The result consists of two parts: the candidate antigen immunogenicity value (Vaxijen; VirusImmu) and the number of antigen support tools (Toolsnum, with immunogenicity thresholds of 0.5 and 0.4 respectively; values ​​above the threshold are defined as neoantigens, and the calculated results are 0, 1, and 2).

[0039] In one specific embodiment of the present invention, the method includes using Neo Immune to calculate the probability that a candidate antigen peptide-MHC complex is recognized by a TCR, selecting TCR sequences with a recognition score higher than 0.99 for the candidate antigen peptide-MHC combination, and calculating the ratio of the TCR sequence to the total TCR as the recognition probability (TCRratio).

[0040] In one specific embodiment of the present invention, a combined prediction score (Comb) is calculated using NetCTLpan to assess MHC presentation, antigen transporter efficiency, and proteasome cleavage rate.

[0041] In one specific embodiment of the present invention, the method includes performing transcriptomic analysis on tissue RNA, aligning RNA sequencing data to a human reference genome, and converting the obtained counts values ​​into expression abundance (TPM), requiring that the TPM be not less than 0.1.

[0042]

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

[0044] Following the above operations, the present invention further includes ranking the candidate antigen peptides based on a neoantigen activity scoring function:

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

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

[0047] The candidate antigen peptides were sequentially validated by ELISpot to obtain several peptides that could activate T cell responses. Antigen-reactive T cells were then sorted by IFNγ flow cytometry. The corresponding antigen peptides that were positive by IFNγ flow cytometry were co-cultured with organoid models for a period of time. The organoid samples were then collected, embedded, sectioned, and stained with Ki-67 immunohistochemistry to screen for immunogenic antigen peptides.

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

[0049] In the WESdata-Somatic Mutation Calling process, FastQC (v.0.12.1) with default parameters was used for quality control analysis of the preprocessed data. Trimmomatic (v.0.39) was used to filter low-quality reads and remove adapters, with MINLEN set to 75 and other parameters left as default. The quality-controlled sequencing data was aligned to the human reference genome (hg19) using BWA, with Blood sequencing data as a reference. MuTect2, Varscan, Muse, Somaticsniper, and Strelka were used to detect somatic mutations, which were then filtered and annotated using Annovar. To obtain high-quality somatic mutations, the following screening criteria were used: 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 annotations were used for subsequent analysis.

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

[0051] In performing WESdata-Neoantigen prediction, one embodiment constructs peptides from the annotated VCF file by sliding a window at the affected mutation sites, generating all 9 polymeric peptides from nonsynonymous somatic mutations (SNVs). The binding affinity of the polymeric peptides to the patient's HLA-I allele is predicted using netMHCpan-4.0; high-affinity binders are defined as IC50. 50Binders with a binding affinity of 500 nM or less were selected. Peptides with a mutant peptide affinity <500 nM and a corresponding wildtype peptide affinity >500 nM were used as candidate antigens for subsequent analysis. Immunogenicity scores for the 9-peptides were calculated using tools including VaxiJen, Virus-immu, and the deep learning-based T-cell activation-related antigen identification method Neo Immune. VaxiJen scores greater than 0.5 were defined as "Antigens," and Virus-immu scores greater than 0.4 were also defined as "Antigens." Neo Immune calculated the peptide-HLA score for recognition by TCR sequences; higher scores indicated a greater probability of TCR recognition. TCR sequences with scores higher than 0.99 were defined, and their proportion of total TCR sequences was calculated as the TCR ratio. NetCTLpan calculated a comprehensive predictive score for MHC presentation, antigen transporter 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: number of tools with immune scores above the threshold (0, 1, 2); Vaxijen and VirusImmu represent the calculated immunogenicity values, respectively; BAdai: difference in affinity scores between mutant / wild-type peptides and their corresponding HLA; Comb: comprehensive prediction score of candidate antigens and HLA calculated by NetCTLpan.

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

[0055] In a specific embodiment of this invention, during the TCGA & CGGA cohort analysis, TCGA-GBM mutation-related data were downloaded from GDC in MAF format. Primary tumor samples (01A) and IDH wildtype samples were screened, totaling 296 samples for subsequent analysis. IDH wildtype and primary GBM samples were screened from the CGGA database for subsequent mutation invocation, totaling 42 samples for subsequent analysis. The mutation invocation method was the same as above: MuTect2, Varscan, Muse, Somaticsniper, and Strelka were used to detect somatic mutations, and annovar was used for annotation after filtering. The screening criteria were as described above. The R language maftools was used for visualization of sample mutations and calculation of TMB.

[0056] In one specific embodiment of this invention, when performing RNA expression quantification, FastQC is used for quality control analysis of preprocessed data, employing Trimmomatic to filter low-quality reads and remove adapters. The quality-controlled sequencing data is aligned to the human reference genome (hg19) using Hista2, and the number of reads aligned to protein-coding genes in each sample is obtained using htseq-count software. The MINAQUAL parameter 'a' is set to 8, 'stranded' is set to 'no', and the remaining parameters are left as default.

[0057] In validating HLA frequencies, in one specific embodiment of this invention, the distribution of HLA alleles (HLA-1, HLA-B, HLA-C) from the AFND (http: / / allelefrequencies.net) database and TCGA healthy samples was used, while the distribution frequencies of HLA in the CGGA database were also calculated. HLA alleles satisfying AF > 1% were defined as commonHLA for subsequent analysis.

[0058] The present invention evaluates and selects candidate antigen peptides obtained by the above-mentioned machine screening, and the evaluation method is the same as above, which will not be repeated here.

[0059] This invention validates the candidate antigen peptides obtained through evaluation and selection using ELISpot to identify peptides capable of activating T cell responses. Neoantigen-reactive T cells are then sorted by IFNγ flow cytometry and further co-cultured with organoid models. After 3 days, organoid samples are collected for embedding, sectioning, and Ki-67 immunohistochemical staining to evaluate cytotoxic efficacy. Ultimately, ideal immunogenic neoantigen peptides suitable for clinical treatment are obtained. In one specific embodiment of this invention, the organoid model includes a patient-derived organoid model. In this example, a glioblastoma patient-derived organoid model was constructed to screen for immunogenic antigen peptides against digitioblastoma.

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

[0061] The present invention also provides a group of antigenic peptides that are immunogenic to glioblastoma, obtained by screening using the above-described screening method.

[0062] In one specific embodiment of the present invention, the antigenic peptide comprises any one of the amino acid sequences shown in SEQ ID No. 1 to SEQ ID No. 14.

[0063] To further illustrate the present invention, the immunogenicity-based antigen peptide screening and evaluation methods provided by the present invention are described in detail below with reference to embodiments, but these 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 the patient in the operating room, whole exome and transcriptome sequencing were performed.

[0066] Once the sequencing data is obtained, algorithm analysis is performed to predict neoantigen peptides.

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

[0068] (1)WESdata-Somatic Mutation Calling

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

[0070] (2) WESdata-HLAtyping

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

[0072] (3)WESdata-Neoantigenprediction

[0073] Based on the annotated VCF file, peptides were constructed by sliding a window at the affected mutation sites, and all 9 polymeric peptides were generated from nonsynonymous somatic mutations (SNVs). The binding affinity of the polymeric peptides to the patient's HLA-I allele was predicted using netMHCpan-4.0. Peptides with mutated peptide affinity <500 nM and corresponding wildtype peptide affinity >500 nM were used as candidate antigens for subsequent analysis.

[0074] Immunogenicity scores for the 9-peptide were calculated using tools including VaxiJen, Virus-immu, and Neo Immune, a deep learning-based method for identifying T-cell activation-related antigens. VaxiJen scores greater than 0.5 were defined as "Antigens," and Virus-immu scores greater than 0.4 were also defined as "Antigens." Neo Immune calculated the score for peptide-HLA recognition by TCR sequences; higher scores indicated a greater probability of TCR recognition. TCR sequences with scores higher than 0.99 were defined, and their proportion of total TCR sequences was calculated as TCRratio. NetCTLpan calculated a comprehensive predictive score for MHC presentation, antigen transporter efficiency, and proteasome cleavage rate. Finally, a scoring model was constructed to score and screen candidate antigen-HLA pairs.

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

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

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

[0078] (4)TCGA&CGGAcohort

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

[0080] Forty-two samples were selected from the CGGA database, including those with IDHwildtype and primary GBM mutations, for subsequent mutation retrieval. The mutation retrieval methods were the same as above: MuTect2, Varscan, Muse, Somaticsniper, and Strelka were used to detect somatic mutations, and annovar was used for annotation after filtering. The selection criteria were as described above.

[0081] The R language maftools is used for visualizing sample mutations and calculating TMB.

[0082] (5) RNA expression level

[0083] FastQC was used for quality control analysis of preprocessed data, using Trimmomatic to filter low-quality reads and remove adapters. The quality-controlled sequencing data were aligned to the human reference genome (hg19) using Hista2, and the number of reads aligned to protein-coding genes in each sample was obtained using the htseq-count software.

[0084] (6) HLA frequency

[0085] The distribution of HLA alleles (HLA-1, HLA-B, HLA-C) was obtained from the AFND (http: / / allelefrequencies.net) database and healthy samples from TCGA, and the distribution frequency of HLA in the CGGA database was also calculated. HLA alleles satisfying AF > 1% were defined as commonHLA for subsequent analysis.

[0086] 2. Peptide evaluation optimization

[0087] (1) Difference in MHC binding affinity of peptides: The binding affinity difference score between wild-type peptides and mutant peptides, and the affinity score is calculated by NetMHCpan;

[0088] (2) Probability of peptide MHC being recognized by TCR: Vaxijen and Virus-immu calculated the immunogenicity of peptides, and NeoImmune calculated the probability of peptide-MHC complex being recognized by TCR.

[0089] (3) NetCTLpan calculates the combined predictive score of MHC presentation, antigen transporter 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 using a neoantigen activity scoring function. Validation is then performed based on a selection threshold from the validated dataset.

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

[0093] (1) Preparation of materials: Heparin anticoagulant tubes, peripheral blood (20ml as an example), PBS, lymphocyte separation medium, buffer Easysep buffer (STEMCELL), red blood cell lysis buffer (Solarbio), 50ml centrifuge tubes, funnel centrifuge tubes, 3 x 10ml pipettes, pipettes, PBMC medium (RPMI-1640 medium, 100U / ml IL-2, 10% FBS, 1% penicillin antibody), PBMC cryopreservation solution (90% RPMI-1640 medium, 10% FBS), 6-well plates. All the above consumables should be placed in a clean bench for ultraviolet irradiation half an hour in advance.

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

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

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

[0097] (5) Transfer the blood diluent to the lymphocyte separation tube using a pipette (set to the lowest setting). 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 boundary will be visible between the blood and the lymphocyte separation fluid.

[0098] (6) Transfer the lymphocyte separation tube to a centrifuge and centrifuge at 2500 rpm for 20 minutes. Collect the supernatant. Slowly pour the supernatant into a blank 50 ml centrifuge tube. Stop when the sediment (red blood cells) at the bottom of the funnel centrifuge tube tends to spill out. Dilute the supernatant with PBS 1:1.

[0099] (7) Centrifuge 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 in 2 ml of erythrocyte lysis buffer, gently mix, and let stand for 3 min. After 3 min, combine the cell suspensions from both tubes into one tube and add PBS to 45 ml. Blend at 1500 rpm for 15 min, discard the supernatant, and obtain a relatively pure peripheral blood mononuclear cell pellet.

[0101] (9) Resuspend cells in 5 ml of PBMC medium and count them. Add medium to a cell concentration of 1×10⁻⁶ cells based on the cell count. 6 Cells / ml, 2-3 ml of cell suspension per well, incubate statically in an incubator;

[0102] (10) After culturing for about 6 hours, 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 cryopreservation solution, resuspend it thoroughly, place it in a cell cryopreservation tube, label it, and store it in liquid nitrogen for long-term cryopreservation.

[0104] 4. Co-culture of PBMC-peptide fragments

[0105] (1) After the patient’s PBMCs were resuscitated, the cryopreservation tubes were placed in a 37°C constant temperature water bath for 5 minutes;

[0106] (2) PBMC medium was warmed, cell suspension was transferred to 50ml centrifuge tube by pipette, 15ml PBMC medium was added, and centrifuged at 1200r / min for 5min;

[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 resuspended in 500 μL of culture medium in 24-well plates and cultured.

[0109] (5) Thaw the polypeptide solution at room temperature, and add 5 μL (20 μg / ml) of wild-type and mutant polypeptide solutions to the culture system respectively. Label the surface of the well plate and place the 24-well plate in a sterile incubator at 37°C, 5% CO2 and 90% humidity.

[0110] (6) Stimulate with 5 μL of polypeptide lysate every 3 days, and supplement with an appropriate amount of culture medium. Stimulate for a total of 2 to 3 cycles, and collect cell samples for ELISpot experiment.

[0111] 5. ELISpot Experiment

[0112] (1) Preparation of materials: RPMI-1640 medium, PBMC medium (as above), PHA, IFN-γ ELISpot kit (Mabtech), ELISpot PVDF 96-well plate (Mabtech), AEC chromogenic solution (Dakewei), 35% alcohol, PBS, fetal bovine serum (FBS, Gibco), multi-channel pipette, sample loading tray;

[0113] (2) ELISpot plate preparation: Place 35% alcohol in the sample loading well, take 20 μL / well with a multi-channel pipette, add it to the PVDF 96-well plate, incubate for 50 s, and remove the alcohol; take 200 μL / well with PBS with a multi-channel pipette, wash the plate, and repeat 5 times; take 100 μL / well with diluted capture mAb (15 μg / ml), cover tightly, and incubate overnight at 4℃;

[0114] (3) Cell incubation: Remove the liquid from the well plate, add 200 μL of PBS / well, wash the plate, and repeat 5 times; add 200 μL of culture medium / well, block, and incubate at room temperature for at least 30 min; fully resuspend the PBMC cells stimulated by the above peptides, take 100 μL / well into PVDF 96-well plates, and set up 3 replicates for each group; set up control groups, add 5 μL of PHA / well to the culture system of the positive control group, and add only cell suspension to the negative control group, and set up 3 replicates for each group; place the PVDF 96-well plates in a sterile incubator at 37℃, 5% CO2 and 90% humidity for 24-48 h;

[0115] (4) Enzyme-linked immunospot assay: Take 200 μL of PBS / well, 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; take 200 μL of PBS / well, wash the plate 5 times; add streptavidin-HRP (1:100 dilution), 100 μL / well, incubate at room temperature for 1 h; take 200 μL of PSB / well, wash the plate 5 times; take 200 μL of PBS / well, wash the plate 5 times; add 100 μL of LAEC chromogenic solution, protect from light, incubate at 37℃, observe every 5 min, after the spots appear, discard the chromogenic solution, add 200 μL of PBS / well, wash the plate, and stop the chromogenic process;

[0116] (5) The PVDF plate was placed in a dry and dark 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 and forceps, PBS, Normocin, organoid culture system (DMEM: F12, 50% neurobasal, 1% GlutaMax, 1% NEAAs, 1% PenStrep, 1% N2, 1X B27, the above reagents were purchased from Gibco and Thermo Fisher Scientific), six-well plates, 50ml centrifuge tubes. The above items were placed in a clean bench and irradiated with ultraviolet light for 30 minutes.

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

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

[0121] (4) Take 15ml of PBS into a sterile culture dish and rinse the tumor tissue again. Repeat 3 times.

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

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

[0124] (7) Discard the supernatant, resuspend the tissue fragments in the culture medium, place them in a six-well plate, and incubate in a sterile incubator at 37°C, 5% CO2, and 90% humidity on a shaker (120 r / min). Observe daily and change the medium. Within 2–3 weeks, the tumor mass will generally form a round organoid.

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

[0126] 7. T cell flow cytometry sorting

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

[0128] (2) Collect approximately 1×10⁻⁶ cells from the washed cell pellet. 7 Add 40 μL of buffer (Miltenyi) to each cell and mix thoroughly to prepare a single-cell suspension;

[0129] (3) Add 10 μL of biotin-labeled CD8+(Miltenyi) T cell combination antibody to each 40 μL 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 magnetic (Miltenyi), mix well and incubate at 4°C for 10 min.

[0131] (5) Add 500 μL of buffer solution and mix well before screening with magnetic beads. After washing the separation column (Miltenyi) with 3 ml of buffer solution, place it in the MACS separation magnetic field.

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

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

[0134] 8. Immunofluorescence staining

[0135] (1) Fix organoids with 1 ml of 4% paraformaldehyde (PFA) at room temperature for 20 min, and fix with PFA overnight; dehydrate with 10%, 20%, and 30% sucrose gradients until the organoids settle to the bottom. Each dehydrated organoid takes about 4 h; embed with OCT, placing 5 organoids in each mold for organoids; for intraoperative tumor tissue, place it on a sample tray, cover with OCT, and place the mold flat in a -80℃ freezer for 30 min; slice with an ice cutter, trim to 20 μm thickness, and slice to 5 μm thickness; before staining, air dry at room temperature for 1 h, 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-cure antigen retrieval solution at room temperature for 5 min, and wash with PBS.

[0137] (3) Use QuickBlock immunostaining blocking solution (Beyotime) to block for 15-20 minutes, then wash with PBS;

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

[0139] (5) Prepare primary antibody with antibody dilution buffer at a ratio of 1:200 to 1:400 and incubate overnight at 4°C;

[0140] (6) Wash 3 times with PBS, prepare fluorescent secondary antibody with antibody dilution 1:400, and let stand at room temperature for 1 hour;

[0141] (7) Wash with PBS 3 times, wipe the PBS off the slide with absorbent paper, and wipe the coverslip clean with lint-free paper.

[0142] (8) Place a drop of mounting medium (containing DPAI) on each slide (avoid air bubbles), cover the slide tightly, and remove air bubbles;

[0143] (9) Take a picture with a fluorescence microscope, 20x objective.

[0144] 9. Flow cytometry analysis

[0145] (1) Collect and wash the cell precipitate, about 1*105 cells, or until the precipitate is visible to the naked eye in the EP tube.

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

[0147] (3) Resuspend the precipitate in 400ul PBS, run it on the instrument, repeat 3 times for each sample, with 10,000 cells per data point. Repeat the main in vitro test results 2-3 times.

[0148] This invention screened 14 immunogenic neoantigen peptides from 21 patients. The ELISpot positive results and organoid validation results are as follows: Figures 1-2 As shown, to further verify the immunogenicity of the peptide, this invention further sorted neoantigen-reactive T cells and performed flow cytometry analysis. CD107a is a surrogate marker for the cytotoxic activity of T cells and can accurately reflect the functional status of T cells; while IFN-γ, as a cytokine in which T cells exert tumor-killing effects, has had its important role widely verified. This invention found that after stimulation with the immunogenic neoantigen peptide, the number of T cells expressing both CD107a and IFN-γ showed an increasing trend. Figure 4 (AD). Simultaneously, it can be observed that for peptides like Peptide No. 15, which showed a strong positive result on ELISpot, the proportion of active T cells in the flow cytometry results was also relatively slightly higher. This further supplements the ELISpot results, demonstrating the immunogenicity of the neoantigen peptide. Similarly, this invention further validated these results using flow cytometry, as shown in the results... Figure 5 As shown, the combination of PD-1 monoclonal antibody and T cells expressing both CD107a and IFN-γ showed a significant increasing trend compared to the single neoantigen peptide stimulation group. Figure 5 The results (AB) further demonstrate the synergistic effect of immune checkpoint inhibitors on neoantigen therapy.

[0149] ELISpot results for other peptides are shown below. Figure 6 As shown, this invention performed a correlation analysis of neoantigen-related mutations in organoids and tumor tissues, and the results are as follows. Figure 7 As shown, most of the 21 matched samples exhibited a strong correlation. Figure 7 (A). The overall distribution of neoantigens in organoids and their parental tumor tissues is also relatively ideal, with most neoantigen-related mutations coexisting in both organoids and parental tumors, reaching as high as 75% or more. Figure 7 (B) The role of tumor mutational burden (TMB) in GBM has been extensively studied. TMB is considered to be closely related to the production of neoantigens. Tumors with high TMB may produce more neoantigens and are associated with enhanced immune responses and the efficacy of immune checkpoint inhibitors.

[0150] As an important feature associated with tumor neoantigen expression, this invention further analyzed the similarity of organoids and parental tumor tissues in terms of TMB. The overall analysis results of 21 pairs of data are as follows: Figure 8 As shown in Figure A, there was no significant difference in TMB between organoids and matched tumor tissues. Furthermore, this invention also plotted the distribution of TMB in each pair of samples, ultimately finding that the trends in TMB variation were relatively consistent. Figure 8 (B)

[0151] Histological results such as Figure 9 As shown, histological evidence demonstrates that organoid models can retain parental tumor tissue characteristics, proving their potential for further efficacy validation. Peptide structures and HLA-binding molecules are shown in Table 1.

[0152] Table 1 Immunogenic peptides

[0153]

[0154]

[0155] Furthermore, it is worth noting that during the validation process using organoid models, it was discovered that not all peptides obtained through ELISpot validation can stimulate T cells to achieve potent immune killing efficacy. For example... Figure 3As shown, T cells stimulated by peptides 79 and 60 can kill organoids to some extent, but the killing efficacy is not very strong. Based on this, this invention employs a combined PD-1 monoclonal antibody method, co-culturing the organoid model with neoantigen-reactive T cells and PD-1 monoclonal antibody. After 3 days, the organoids are harvested; fixed, embedded, and sectioned, and stained with Ki67. A more significant killing efficacy was ultimately achieved. Therefore, the application of organoid models that reflect the glioblastoma microenvironment has certain positive significance and can provide direct and effective guidance for clinical applications.

[0156] PBMCs from patients stimulated with neoantigen peptides were subjected to flow cytometry sorting for T cells. IFN-γ and CD107a were selected for flow cytometry analysis of neoantigen-reactive T cells (Neo-T cells). Results are as follows: Figure 4 As shown, stimulation with the immunogenic peptide significantly increased the number of T cells expressing IFN-γ and CD107a, with statistically significant differences. After combining the neoantigen peptide with PD-1 monoclonal antibody, changes in neoantigen-responsive T cells were detected by flow cytometry, and the results are as follows. Figure 5 As shown.

[0157] The results of the candidate neoantigen peptide ELISpot are as follows: Figure 6 As shown, an unpaired samples t-test was used, and the p-value was tested using the log-rank method. In this result, the number of IFNγ positive points in the wild-type peptide group and the mutant peptide group showed statistically significant differences. The similarity of the organoid model to parental tumor tissue and the preservation of neoantigen mutations are shown in the following figures. Figure 7 As shown, the organoid model preserves the TMB features of the parental tumor tissue as follows: Figure 8 As shown, the organoid model preserves the characteristics of parental tumor tissue as follows: Figure 9 As shown, this demonstrates at the histological level that organoid models can preserve the characteristics of parental tumor tissue, proving their potential for further efficacy validation.

[0158] Although the above embodiments have provided a detailed description of the present invention, they are only some embodiments of the present invention, and not all embodiments. People can obtain other embodiments based on these embodiments without creative effort, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A method for screening glioblastoma antigen peptides, characterized in that, The process includes the following steps: evaluating the candidate antigen peptides predicted by the machine algorithm using an evaluation method, and ranking them based on the antigen activity scoring function obtained from the evaluation; the evaluation method includes performing the following operations on the screened candidate antigen peptides respectively: (1) detecting the binding affinity of the candidate antigen peptides to MHC; calculating the difference score of the binding affinity between wild-type peptides and mutant peptides of the candidate antigen peptides to MHC using NetMHCpan. Where BAdai is the binding affinity difference score; (2) Detecting the immunogenicity of the candidate antigen peptide; including calculating the immunogenicity of the candidate antigen peptide using Vaxijen and Virus-immu; And the number of antigen support tools (Toolsnum); with 0.5 and 0.4 as immunogenicity thresholds respectively, those above the threshold are defined as neoantigens; (3) Detect the probability of candidate antigen peptide-MHC complex being recognized by TCR, select TCR sequences with recognition scores higher than 0.99; calculate the probability of candidate antigen peptide-MHC complex being recognized by TCR using Neo Immune; (4) Detect the comprehensive prediction score of MHC presentation, antigen transport efficiency and proteasome cleavage rate; use NetCTLpan to calculate the comprehensive prediction score of MHC presentation, antigen transport efficiency and proteasome cleavage rate. (5) Detect the expression abundance of the candidate antigen peptide; the detection of expression abundance includes transcriptomic analysis of tissue RNA, aligning RNA sequencing data to the human reference genome, and converting the obtained counts value into expression abundance, requiring the expression abundance to be not less than 0.1; Where TPM represents expression abundance and n represents the total number of genes. L i The length of gene i, N i for The count of gene i; It also includes ranking candidate antigen peptides based on a neoantigen activity scoring function: Where score is the neoantigen activity scoring function, TCRratio is the TCR binding frequency calculated by Neo Immune, Toolsnum is the number of tools with an immune score higher than the threshold, Vaxijen and VirusImmu represent the calculated immunogenicity values, respectively; BAdai is the difference in affinity scores between mutant peptides / wild-type peptides and their corresponding HLA; Comb is the comprehensive prediction score of candidate antigens and HLA calculated by NetCTLpan. The candidate antigen peptides were sequentially validated by ELISpot to obtain several peptides that could activate T cell responses. Antigen-reactive T cells were then sorted by IFNγ flow cytometry. The corresponding antigen peptides that were positive by IFNγ flow cytometry were co-cultured with organoid models for a period of time. The organoid samples were then collected, embedded, sectioned, and stained with Ki-67 immunohistochemistry to screen for immunogenic antigen peptides. The screening criteria are: 1) mutation-related antigens appear simultaneously in both the tumor tissue and organoids of the sample; 2) the expression level of the corresponding mutated gene in the organoid sample is not less than 0.

1.

2. The screening method according to claim 1, characterized in that, The organoid models include patient-derived organoid models.

3. A group of antigenic peptides that are immunogenic to glioblastoma, obtained by screening using the screening method described in claim 1 or 2, characterized in that, The antigenic peptide includes any of the amino acid sequences shown in SEQ ID No. 1 to SEQ ID No. 12.

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