Public value evaluation method and device for tumor neoantigens, equipment and storage medium

Abstract

The invention belongs to the technical field of bioinformatics, and discloses a common value evaluation method and device for tumor neoantigens, equipment and a storage medium, missense mutation analysis is performed by acquiring mutation data of a pan cancer sample, a plurality of missense mutation sites are obtained and compared with a human genome to obtain candidate mutation sites, and the candidate mutation sites are used for evaluating the common value of the tumor neoantigens. Extracting the newborn peptide fragment corresponding to each candidate mutation site, inputting the newborn peptide fragment into the prediction model, predicting the binding score of the newborn peptide fragment and the human leukocyte antigen, and calculating the common value score of each newborn peptide fragment according to the number of the newborn peptide fragments generated at the candidate mutation sites and the corresponding binding score of the newborn peptide fragment and the human leukocyte antigen; according to the method, the new peptide fragments are sorted according to the descending order of the scores, and the new antigen sequence is screened out from the sorting result, so that the new antigen value can be effectively evaluated based on the number of the new peptide fragments generated by the site and the binding scores of the corresponding new peptide fragments and the human leukocyte antigen, and the new antigen with higher adaptability is screened out.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics technology, specifically relating to a method, apparatus, equipment, and storage medium for assessing the public value of a neotumor antigen. Background Technology

[0002] Cancer is caused by the accumulation of mutated genes, some of which may lead to the production of new proteins called neoantigens. Neoantigens possess high immunogenicity and have long been considered ideal targets for tumor immunotherapy. However, due to limitations in drug development cycles and the existence of heterogeneous clones within cancer tissue, most neoantigens derived from random "passenger gene" mutations often show poor therapeutic efficacy. Conversely, neoantigens generated from driver gene mutations typically maintain high clonal conservatism, exhibiting more stable therapeutic potential. If a neoantigen generated from a hotspot mutation in a driver gene successfully binds to a common HLA allele, this type of cancer-specific epitope will be shared among cancer patients, forming a "public" neoantigen. Compared to individual patient-specific neoantigens, public neoantigens have greater clinical translational value due to their tumor specificity, clonal conservation, and widespread presence in specific patient populations, enabling more patients to benefit from this therapy. To date, only a few public neoantigens have been publicly disclosed, reflecting that current assessment methods based on the frequency of missense mutations in large-scale disease genes and class I HLA binding ability are not yet effective in evaluating neoantigen value, resulting in poor suitability of the screened neoantigens. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, device, and storage medium for assessing the public value of tumor neoantigens, which can effectively assess the value of neoantigens and screen out more suitable neoantigens.

[0004] The first aspect of this invention discloses a method for assessing the public value of a tumor neoantigen, comprising:

[0005] Obtain pan-cancer sample mutation data, and perform missense mutation analysis on the pan-cancer sample mutation data to obtain multiple missense mutation sites;

[0006] Multiple missense mutation sites were compared with the human genome to obtain multiple candidate mutation sites;

[0007] Extract the nascent peptide corresponding to each of the candidate mutation sites;

[0008] The newly formed peptide is input into the prediction model to predict the binding score between the newly formed peptide and human leukocyte antigen.

[0009] Based on the number of newly generated peptides produced by the candidate mutation sites and the binding scores of the corresponding newly generated peptides to human leukocyte antigens, a common value score is calculated for each newly generated peptide.

[0010] The newly synthesized peptides are sorted in descending order of public value score to obtain a sorting result, and new antigen sequences are screened from the sorting result.

[0011] In some embodiments, missense mutation analysis is performed on pan-cancer sample mutation data to obtain multiple missense mutation sites, including:

[0012] Calculate the missense mutation frequency at each mutation site in the pan-cancer sample mutation data;

[0013] Mutation sites whose missense mutation frequency reaches a first specified proportion are identified as missense mutation sites.

[0014] In some embodiments, the public value score is the Cauchy-Schwarz score; screening for novel antigen sequences from the ranking results includes:

[0015] Based on the number of all the newly synthesized peptides, the sequence with the highest Cauchy-Schwarz score in descending order is selected as the second-highest specified proportion and identified as the neoantigen sequence.

[0016] In some embodiments, the binding score includes the predicted scores of the nascent peptide and each HLA-I and HLA-II; calculating the common value score for each nascent peptide includes:

[0017] The public value score for each of the newly formed peptides is calculated using the following formula:

[0018] Cauchy-Schwarz rating = [∑ i＝1…n (HLAI1+HLAI2+…+HLAI n )+

[0019] ∑ i＝1…n (HLAII1+HLAII2+…+HLAII n )]×VAF;

[0020] Where n is the number of newly generated peptides produced by the candidate mutation site, HLAI n For the corresponding newly formed peptide and the predicted score of the nth HLAII, HLAII n The predicted score is the corresponding newly generated peptide and the nth HLAII, and VAF is the missense mutation frequency.

[0021] In some embodiments, the prediction model is selected from any of the following: NetMHCpan4.1, NetMHCIIpan4.1EL, Consensus, ANN 4.0, SMMPMBEC, SMM, netMHCcons.

[0022] A second aspect of this invention discloses a public value assessment device for tumor neoantigens, comprising:

[0023] Acquisition unit, used to acquire mutation data from pan-cancer samples;

[0024] The analysis unit is used to perform missense mutation analysis on pan-cancer sample mutation data to obtain multiple missense mutation sites.

[0025] The alignment unit is used to align multiple missense mutation sites with the human genome to obtain multiple candidate mutation sites;

[0026] An extraction unit is used to extract the nascent peptide corresponding to each of the candidate mutation sites;

[0027] The prediction unit is used to input the newly formed peptide into the prediction model and predict the binding score of the newly formed peptide to human leukocyte antigen.

[0028] The calculation unit is used to calculate the common value score of each newly generated peptide based on the number of newly generated peptides generated by the candidate mutation sites and the binding score of the corresponding newly generated peptides to human leukocyte antigens.

[0029] A sorting unit is used to sort multiple newly formed peptide segments in descending order of public value score to obtain a sorting result;

[0030] The screening unit is used to select new antigen sequences from the sorting results.

[0031] In some embodiments, the analysis unit is specifically used to calculate the missense mutation frequency of each mutation site in the pan-cancer sample mutation data; and to screen mutation sites whose missense mutation frequency reaches a first specified proportion to determine them as missense mutation sites.

[0032] In some embodiments, the public value score is a Cauchy-Schwarz score; the screening unit is specifically used to select the second specified proportion of sequences with the highest Cauchy-Schwarz scores in descending order, based on the number of all the newly formed peptides, and determine them as neoantigen sequences.

[0033] A third aspect of the present invention discloses an electronic device, including a memory storing executable program code and a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the public value assessment method for tumor neoantigens disclosed in the first aspect.

[0034] A fourth aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to perform the public value assessment method for tumor neoantigens disclosed in the first aspect.

[0035] The beneficial effects of this invention are as follows: by acquiring pan-cancer sample mutation data, performing missense mutation analysis on the pan-cancer sample mutation data, obtaining multiple missense mutation sites, comparing them with the human genome, obtaining multiple candidate mutation sites, extracting the nascent peptide corresponding to each candidate mutation site and inputting it into a prediction model, predicting the binding score of the nascent peptide with human leukocyte antigen, calculating the public value score of each nascent peptide based on the number of nascent peptides generated by the candidate mutation sites and the corresponding binding score of the nascent peptides with human leukocyte antigen, sorting the multiple nascent peptides in descending order of public value score, and screening out neoantigen sequences from the sorting results, the value of neoantigens can be effectively evaluated based on the number of nascent peptides generated by the candidate mutation sites and the corresponding binding score of the nascent peptides with human leukocyte antigens, and more suitable neoantigens can be screened out. Attached Figure Description

[0036] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0037] Unless otherwise specified or defined, the same reference numerals in different figures represent the same or similar technical features, and different reference numerals may be used to represent the same or similar technical features.

[0038] Figure 1 This is a flowchart of a public value assessment method for a tumor neoantigen disclosed in this invention;

[0039] Figure 2 This is an overall flowchart of a specific embodiment of the present invention;

[0040] Figure 3 This is the sorting result of newly formed peptides according to the Cauchy-Schwarz score from largest to smallest, as disclosed in the embodiments of the present invention;

[0041] Figure 4 This is the verification result of the screened new antigen sequence disclosed in the embodiments of the present invention;

[0042] Figure 5 This is a schematic diagram of the structure of a public value assessment device for tumor neoantigens disclosed in an embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention.

[0044] Explanation of reference numerals in the attached figures:

[0045] 501. Acquisition unit; 502. Analysis unit; 503. Comparison unit; 504. Extraction unit; 505. Prediction unit; 506. Calculation unit; 507. Sorting unit; 508. Filtering unit; 601. Memory; 602. Processor. Detailed Implementation

[0046] Unless otherwise specified or defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. When combined with the technical solutions of the invention in a real-world scenario, all technical and scientific terms used herein may also have meanings corresponding to the purpose of achieving the technical solutions of the invention. The terms "first," "second," etc., used herein are merely for distinguishing names and do not represent a specific number or order. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0047] It should be noted that when a component is considered "fixed" to another component, it can be directly fixed to the other component or there can be an intervening component; when a component is considered "connected" to another component, it can be directly connected to the other component or there can be an intervening component; when a component is considered "mounted" on another component, it can be directly mounted on the other component or there can be an intervening component; when a component is considered "placed" on another component, it can be directly placed on the other component or there can be an intervening component.

[0048] Unless otherwise specified or defined, the terms "described" or "the" as used herein refer to the technical features or technical content mentioned or described prior to the relevant section, which may be the same as or similar to the technical features or technical content mentioned herein. Furthermore, the terms "comprising" and "having," and any variations thereof, as used herein, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0049] This invention discloses a method for assessing the public value of tumor neoantigens, which can be implemented through computer programming. The method can be executed by electronic devices such as computers, laptops, and tablets, or by a public value assessment device for tumor neoantigens embedded in an electronic device; this invention does not limit this to any particular type. To facilitate understanding of this invention, specific embodiments will be described in more detail below with reference to the accompanying drawings.

[0050] like Figure 1 As shown, the method includes the following steps 110-160:

[0051] 110. Obtain pan-cancer sample mutation data, and perform missense mutation analysis on the pan-cancer sample mutation data to obtain multiple missense mutation sites.

[0052] Missense mutation analysis includes: calculating the missense mutation frequency of each mutation site in the pan-cancer sample mutation data, and screening mutation sites whose missense mutation frequencies reach a first specified proportion to be identified as missense mutation sites. The first specified proportion is a preset value, such as 0.2% or 0.3%.

[0053] 120. Multiple missense mutation sites were compared with the human genome to obtain multiple candidate mutation sites.

[0054] The pan-cancer sample mutation data can be downloaded and statistically analyzed from a database, which can be selected from any of the following: cBioPortal, TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus), COSMIC (Catalogue of Somatic Mutations in Cancer), Kaplan-Meier Plotter, or GENIE (Genomics Evidence Neoplasia Information Exchange). The human genome can be selected from any of the following versions: GRCh38 / hg38, T2T CHM13v2.0 / hs1, GRCh37 / hg19, NCBI36 / hg18, NCBI35 / hg17, or NCBI34 / hg16.

[0055] 130. Extract the nascent peptide corresponding to each candidate mutation site.

[0056] Among them, the nascent peptide is a peptide that can bind to two types of molecules of human leukocyte antigen (HLA) and induce an immune response. These two types of molecules include HLA-I molecules and HLA-II molecules.

[0057] 140. Input the newly formed peptide into the prediction model to predict the binding score between the newly formed peptide and human leukocyte antigen.

[0058] The prediction model can be a pre-built machine learning model, for example, selected from any of the following: NetMHCpan4.1, NetMHCIIpan4.1EL, Consensus, ANN 4.0, SMMPMBEC, SMM, netMHCcons. The binding score of the nascent peptide to human leukocyte antigen can be calculated based on the affinity of the nascent peptide to HLA-I and HLA-II.

[0059] 150. Calculate the common value score of each new peptide based on the number of newly generated peptides generated by the candidate mutation sites and the binding score of the corresponding newly generated peptides to human leukocyte antigens.

[0060] The public value score can be specifically the Cauchy-Schwarz score, which is calculated by taking into account the number of newly generated peptides produced by candidate mutation sites and the affinity of the newly generated peptides to HLA-I and HLA-II, based on the gene mutation frequency of different disease patient groups.

[0061] The Cauchy-Schwarz score can be calculated using the following formula:

[0062] Cauchy-Schwarz rating = [∑ i＝1…n (HLAI1+HLAI2+…+HLAI n )+

[0063] ∑ i＝1…n (HLAII1+HLAII2+…+HLAII n )]×VAF;

[0064] Where n represents the number of newly generated peptides from candidate mutation sites, i.e., the number of effective peptides. Effective peptides are sub-peptides that contain missense mutation sites and have a binding score with HLA. n The predicted score of the newly formed peptide and the nth HLAII is given by HLAII. n The predicted score for the newly formed peptide and the nth HLAII is given, and VAF represents the missense mutation frequency.

[0065] 160. Sort multiple newly generated peptides in descending order of public value score to obtain the sorting results, and screen out the new antigen sequences from the sorting results.

[0066] The process of sorting multiple newly formed peptides includes listing both the number and sequence of each peptide. The screening criteria for selecting neoantigen sequences from the sorting results are as follows: sequences are ordered from highest to lowest Cauchy-Schwarz score, and based on the total number of newly formed peptides, the second-highest percentage of sequences with the highest Cauchy-Schwarz scores are selected as valid neoantigen sequences. This second-highest percentage is a preset value, such as 1% or 2%.

[0067] The final selected neoantigen sequences are used to translate into clinical drugs. Comparing the neoantigen sequences with in vitro experimental validation results and clinical sequencing data allows for the validation of the Cauchy-Schwarz score. Specifically, sample data from at least one cancer patient group can be selected as the research sample, and the Cauchy-Schwarz score can be used to score and rank the research samples. The cancer types in the cancer patient group can be selected from at least one of the following: lung cancer, breast cancer, and prostate cancer.

[0068] Example 1

[0069] The overall flowchart of this embodiment is as follows: Figure 2 As shown, the process includes preliminary preparation, Cauchy-Schwarz scoring, ranking of neoantigen value scores, and verification of the screened neoantigen sequences.

[0070] (1) Preliminary preparations

[0071] Specifically, 98,478 cancer patients were selected as the study subjects, containing a total of 3,392,826 missense mutation sites. The sample population was predominantly Caucasian, followed by Asian and African populations; it covered 33 different cancer types, with lung cancer, breast cancer, and prostate cancer being the main types. A missense mutation frequency of ≥0.2% was used as the screening criterion, and 1200 missense mutation sites were obtained for subsequent research. The NetMHCpan4.1 model was used to predict the affinity of the nascent peptide with HLA-I, and the NetMHCIIpan4.1EL model was used to predict the affinity of the nascent peptide with HLA-II. The binding scores of the nascent peptide with HLA-I and HLA-II were calculated. The specific cancer population statistics are shown in Table 1 below:

[0072] Table 1. Statistics on the number of cancer cases

[0073]

[0074]

[0075] (2) Cauchy-Schwarz score

[0076] Following the prior evaluation method for common neoantigens, a total of 7 common neoantigens were screened. Using the Cauchy-Schwarz scoring method, 15 neoantigens were obtained. Besides 6 identical neoantigens, EGFR-L858R, KRAS-G13*, and TP53-G245 were found to have been validated in Phase 1 / 2 clinical trials. Four completely previously unreported novel sequences, including IDH1 and DNMT3A, were also identified.

[0077] (3) Ranking of neoantigen value scores

[0078] The newly formed peptides were sorted in descending order of their Cauchy-Schwarz scores. The sorting results are as follows: Figure 3 As shown.

[0079] (4) Result comparison

[0080] Table 2 Comparison of Cauchy-Schwarz Index Assessment Results with In Vitro Experimental Results and Clinical Test Results

[0081]

[0082]

[0083] (5) Validation of the screened new antigen sequences

[0084] like Figure 4 As shown, sequencing data from 8065 lung cancer patients, 3374 prostate cancer patients, 6439 breast cancer patients, and 494 lung adenocarcinoma patients were tested, with "checkmarks" indicating validated peptides. This finding not only validates the effectiveness of the Cauchy-Schwarz index assessment model in identifying neoantigens with broad potential but also demonstrates the universality and reliability of this assessment method across disease domains. Therefore, the results indicate that the Cauchy-Schwarz score can be used to develop common neoantigens into routine drugs for rapid cancer treatment, demonstrating the significant effectiveness of this assessment method in advancing neoantigen research and clinical application.

[0085] In summary, this invention broadly covers various cancer populations and spans multiple cancer lineages. Through a qualitative and sophisticated neoantigen value assessment mechanism, it successfully transforms highly adaptable public neoantigens into standardized, potentially clinically applicable routine drugs. This transformation process not only marks a significant leap from theoretical research to clinical practice but also brings innovative treatment strategies and methods to the field of cancer therapy.

[0086] like Figure 5As shown, this embodiment of the invention discloses a public value assessment device for tumor neoantigens, including an acquisition unit 501, an analysis unit 502, a comparison unit 503, an extraction unit 504, a prediction unit 505, a calculation unit 506, a sorting unit 507, and a screening unit 508, wherein...

[0087] Acquisition unit 501 is used to acquire pan-cancer sample mutation data;

[0088] Analysis unit 502 is used to perform missense mutation analysis on pan-cancer sample mutation data to obtain multiple missense mutation sites;

[0089] The alignment unit 503 is used to align multiple missense mutation sites with the human genome to obtain multiple candidate mutation sites;

[0090] Extraction unit 504 is used to extract the nascent peptide corresponding to each candidate mutation site;

[0091] Prediction unit 505 is used to input the newly formed peptide into the prediction model and predict the binding score of the newly formed peptide to human leukocyte antigen.

[0092] The calculation unit 506 is used to calculate the common value score of each new peptide based on the number of new peptides generated by the candidate mutation site and the binding score of the corresponding new peptides to human leukocyte antigen.

[0093] Sorting unit 507 is used to sort multiple newly formed peptides in descending order of public value score to obtain sorting results;

[0094] The screening unit 508 is used to screen out new antigen sequences from the sorting results.

[0095] As an optional implementation, the analysis unit 502 is specifically used to calculate the missense mutation frequency of each mutation site in the pan-cancer sample mutation data; and to screen mutation sites whose missense mutation frequency reaches a first specified proportion and determine them as missense mutation sites.

[0096] As an optional implementation, the public value score is the Cauchy-Schwarz score; the screening unit 508 is specifically used to select the second-highest specified proportion of sequences with the highest Cauchy-Schwarz scores in descending order, based on the number of all newly generated peptides, and determine them as new antigen sequences.

[0097] like Figure 6 As shown, an embodiment of the present invention discloses an electronic device, including a memory 601 storing executable program code and a processor 602 coupled to the memory 601;

[0098] The processor 602 calls the executable program code stored in the memory 601 to execute the public value assessment method for tumor neoantigens described in the above embodiments.

[0099] This invention also discloses a computer-readable storage medium storing a computer program that causes a computer to execute the public value assessment method for tumor neoantigens described in the above embodiments.

[0100] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

[0101] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A method for assessing the public value of a tumor neoantigen, characterized in that, include: Obtain pan-cancer sample mutation data, and perform missense mutation analysis on the pan-cancer sample mutation data to obtain multiple missense mutation sites; Multiple missense mutation sites were compared with the human genome to obtain multiple candidate mutation sites; Extract the nascent peptide corresponding to each of the candidate mutation sites; The newly formed peptide is input into the prediction model to predict the binding score between the newly formed peptide and human leukocyte antigen. Based on the number of newly generated peptides produced by the candidate mutation sites and the binding scores of the corresponding newly generated peptides to human leukocyte antigens, a common value score is calculated for each newly generated peptide. The newly synthesized peptides are sorted in descending order of public value score to obtain a sorting result, and new antigen sequences are screened from the sorting result.

2. The method for assessing the public value of tumor neoantigens as described in claim 1, characterized in that, Missense mutation analysis of pan-cancer sample mutation data yielded multiple missense mutation sites, including: Calculate the missense mutation frequency at each mutation site in the pan-cancer sample mutation data; Mutation sites whose missense mutation frequency reaches a first specified proportion are identified as missense mutation sites.

3. The method for assessing the public value of tumor neoantigens as described in claim 2, characterized in that, The public value score is the Cauchy-Schwarz score; new antigen sequences are selected from the ranking results, including: Based on the number of all the newly synthesized peptides, the sequence with the highest Cauchy-Schwarz score in descending order is selected as the second-highest specified proportion and identified as the neoantigen sequence.

4. The method for assessing the public value of tumor neoantigens as described in claim 3, characterized in that, The binding score includes the predicted scores of the newly formed peptide and each HLA-I and HLA-II; the common value score for each newly formed peptide is calculated, including: The public value score for each of the newly formed peptides is calculated using the following formula: Cauchy-Schwarz rating = [∑ i＝1…n (HLAI1+HLAI2+…+HLAI n )+∑ i＝1…n (HLAII1+HLAII2+…+HLAII n )]×VAF; Where n is the number of newly generated peptides produced by the candidate mutation site, HLAI n For the corresponding newly formed peptide and the predicted score of the nth HLAII, HLAII n The predicted score is the corresponding newly generated peptide segment and the nth HLAII, and VAF is the missense mutation frequency.

5. The method for assessing the public value of tumor neoantigens as described in any one of claims 1 to 4, characterized in that, The prediction model is selected from any of the following: NetMHCpan4.1, NetMHCIIpan4.1EL, Consensus, ANN 4.0, SMMPMBEC, SMM, netMHCcons.

6. A public value assessment device for a tumor neoantigen, characterized in that, include: Acquisition unit, used to acquire mutation data from pan-cancer samples; The analysis unit is used to perform missense mutation analysis on pan-cancer sample mutation data to obtain multiple missense mutation sites. The alignment unit is used to align multiple missense mutation sites with the human genome to obtain multiple candidate mutation sites; An extraction unit is used to extract the nascent peptide corresponding to each of the candidate mutation sites; The prediction unit is used to input the newly formed peptide into the prediction model and predict the binding score of the newly formed peptide to human leukocyte antigen. The calculation unit is used to calculate the common value score of each newly generated peptide based on the number of newly generated peptides generated by the candidate mutation sites and the binding score of the corresponding newly generated peptides to human leukocyte antigens. A sorting unit is used to sort multiple newly formed peptide segments in descending order of public value score to obtain a sorting result; The screening unit is used to select new antigen sequences from the sorting results.

7. The public value assessment device for tumor neoantigens as described in claim 6, characterized in that, The analysis unit is specifically used to calculate the missense mutation frequency of each mutation site in the pan-cancer sample mutation data; and to screen mutation sites whose missense mutation frequency reaches a first specified proportion to determine them as missense mutation sites.

8. The public value assessment device for tumor neoantigens as described in claim 6, characterized in that, The public value score is the Cauchy-Schwarz score; the screening unit is specifically used to select the second-highest specified proportion of sequences with the highest Cauchy-Schwarz scores, based on the number of all the newly formed peptides, in descending order of Cauchy-Schwarz scores, to determine the new antigen sequences.

9. An electronic device, characterized in that, It includes a memory storing executable program code and a processor coupled to the memory; the processor invokes the executable program code stored in the memory to execute the public value assessment method for tumor neoantigens according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program causes a computer to perform the public value assessment method for tumor neoantigens as described in any one of claims 1 to 5.

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