A method and system for identifying new tumor antigens based on second-generation sequencing data

Abstract

The present invention proposes a method and system for identifying tumor neoantigens based on second-generation sequencing data, the method comprising: obtaining tumor samples and peripheral blood samples of patients, constructing sequencing libraries and performing targeted capture of RNA and DNA; performing quality control on second-generation sequencing data; performing sequence comparison on DNA data and RNA data after quality control, detecting peripheral blood SNV, InDel mutations, SNV and InDel mutations of tumor tissues, annotating and calculating the SNV and InDel mutation frequencies of blood tissues and tumor tissues, and calculating gene expression abundance at the same time, thereby screening out mutations that can produce variant proteins; detecting gene fusion and abnormal splicing events of tumor tissues; typing HLA-I and HLA-II alleles to predict the genotype of the patient's HLA; confirming candidate neoantigen polypeptide sequences; predicting the binding affinity and stability index of the neoantigen-MHC complex, and simultaneously evaluating and optimizing the neoantigen. The method and system for predicting neoantigens of the present invention optimize the detection time of neoantigens and reduce operating costs.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular relates to a method and system for identifying new tumor antigens based on second-generation sequencing data. Background Art

[0002] Recent studies on tumor immunotherapy have shown that using the patient's own immune system is an advanced strategy to eliminate cancer cells. During tumorigenesis, the genetic material of somatic cells undergoes many mutations (somatic mutations), some of which lead to the appearance of abnormal proteins, thereby promoting the progression of cancer and affecting diagnosis and treatment. Some mutant peptides produced by the mutation of the genetic material of tumor cells can be presented to the cell surface through human leukocyte antigens (HLA), which are the major histocompatibility complex (MHC) of humans. Mutant peptides can bind to HLA to form pMHC complexes on the cell surface, some of which can be recognized by T cell receptors (TCRs), thereby causing an immune response to kill cancer cells. These mutant peptides that can be presented by HLAs and further recognized by TCRs to cause an immune response are named neoantigens. A large number of studies have shown that neoantigens can be used to develop personalized cancer vaccines, and neoantigen-based immunotherapy is a promising cancer treatment method.

[0003] Somatic genetic variation in different cancers and different patients is largely heterogeneous. Therefore, it is necessary to identify and evaluate neoantigens at a personalized level. In general, neoantigen prediction based on next-generation sequencing (NGS) data includes three steps: (1) obtaining genetic variation sites that can produce variant proteins from genomic sequencing data and converting them into neoantigens containing mutations of appropriate length; (2) predicting the binding affinity between MHC molecules and neoantigens; (3) evaluating and optimizing candidate neoantigens. There are four main types of genetic mutations that can produce variant proteins: single nucleotide variation (SNV), insertion and deletion mutations (InDel), gene fusion, and abnormal splicing. Most of the current neoantigen prediction processes are still based on single nucleotide variation and insertion and deletion. Many recent studies have also emphasized the key role of gene fusion and abnormal splicing in neoantigen prediction. Therefore, it is necessary to increase the types of variation sites examined to discover neoantigens more comprehensively. Factors such as the binding affinity of neoantigens to MHC molecules, the stability of binding, and the hydrophobicity of neoantigens are important references for determining whether neoantigens are immunogenic. How to optimize candidate neoantigens based on these factors is a very important step.

[0004] The prediction of tumor neoantigens based on high-throughput sequencing data is a very complex process, which mainly consists of three parts: (1) detecting mutations that can produce variant proteins (SNV, InDel, gene fusion, abnormal splicing), and converting them into neoantigens containing mutations; (2) predicting neoantigen-MHC binding; (3) sorting candidate neoantigens. The current technology has areas that need improvement in each part, as follows:

[0005] There are two problems with current technologies for genetic variation detection:

[0006] 1. Only the detection of SNV and InDel is considered; 2. A single detection tool is used, which narrows the screening scope of new antigens.

[0007] The prediction of HLA genotypes is incomplete. Most current technologies only predict HLA genotypes of type I, which reduces the number of effective neoantigens. The current neoantigen-MHC binding prediction only considers binding affinity, without considering other indicators such as binding stability, which will result in high false positives. The neoantigen prediction process has many analysis modules, slow running speed, and long time consumption. Summary of the invention

[0008] The purpose of the present invention is to propose a method and system for identifying new tumor antigens based on second-generation sequencing data, which can effectively solve the above-mentioned technical problems existing in the prior art.

[0009] In order to achieve the above object, an embodiment of the present invention provides a method for identifying tumor neoantigens based on second-generation sequencing data, the method comprising the steps of:

[0010] S1. Obtain tumor samples and peripheral blood samples from patients, extract DNA and RNA from tumor samples and DNA from peripheral blood samples, construct sequencing libraries and perform targeted capture of RNA and DNA, obtain DNA exon sequencing data of tumor tissues and blood tissues, and RNA transcriptome sequencing data of tumor tissues;

[0011] S2, quality control of next-generation sequencing data;

[0012] S3. Use the reference genome as a control group to perform sequence comparison with the quality-controlled DNA and RNA data, detect SNV and InDel mutations in peripheral blood, SNV and InDel mutations in tumor tissues, annotate and calculate the SNV and InDel mutation frequencies in blood tissues and tumor tissues, and calculate gene expression abundance, thereby screening out mutations that can produce variant proteins;

[0013] S4, detecting gene fusion and abnormal splicing events of tumor tissues based on the sequence alignment results and gene expression abundance of the tumor tissue RNA sequencing data obtained in S3;

[0014] S5, typing the HLA-I and HLA-II alleles according to the quality-controlled blood sample DNA data obtained in S2, and predicting the HLA genotype of the patient;

[0015] S6. Confirm the candidate neoantigen peptide sequence;

[0016] S7. Predict the binding affinity and stability index of the neoantigen-MHC complex, and evaluate and optimize the neoantigen, including:

[0017] S7.1. Estimate the binding affinity between each peptide and each HLA class I and each HLA class II allele, and estimate the binding stability between each peptide and MHC;

[0018] S7.2. Design conditions for screening new antigens, including:

[0019] A. Binding affinity is less than 500uM, and Rank_bf is obtained;

[0020] B. Compare the predicted binding score of the neoantigen with the predicted binding scores of a group of random natural peptides to obtain the percentage ranking of the predicted binding score of the neoantigen in the background, and screen out the neoantigens with a percentage ranking less than 2% to obtain Rank_pr;

[0021] C. The new antigen with binding stability greater than 1 h is obtained by Rank_bs;

[0022] S7.3. The conditions for screening new antigens are added together to obtain a scoring formula, and the score Score_neo of the new antigen is calculated, which is expressed as follows: Score_neo = Rank_bf*0.5+Rank_pr*0.3+Rank_bs*0.2.

[0023] Preferably, the standard for the amount of sequencing data is 30G bases for DNA and 10G bases for RNA.

[0024] Preferably, the step S2 specifically includes:

[0025] Flexbar software was used to remove linker sequences, sequences with a base mass score less than 15 accounting for more than 40%, and sequences containing 5 or more N bases from the DNA data of blood tissue, the DNA data of tumor tissue, and the RNA transcriptome data of tumor tissue. The GC content of the bases was between 45% and 55%, and the proportion of base mass values ​​greater than 30 was more than 85%.

[0026] Preferably, in S3, the reference genome is used as a control group to perform sequence comparison with the quality-controlled DNA data and RNA data, specifically: the quality-controlled DNA sequence is aligned to the human reference genome using the MEN algorithm of the BWA software, the alignment results are obtained and sorted, and an Index file is created, and then the Sambamba software is used to mark or remove duplicate sequences; the quality-controlled RNA sequence is aligned to the human reference genome using STAR;

[0027] The detection of peripheral blood SNV, InDel mutations, tumor tissue SNV, InDel mutations includes:

[0028] Use Strelka and Mutect2 to align the sequence comparison results of the blood samples to the human reference genome to obtain the gene variation results of the blood samples;

[0029] The basic mutation results were used as the control group, and Strelka and Mutect2 software were used to detect mutations in tumor tissues based on the sequence comparison results of tumor tissues;

[0030] The screening conditions for screening out mutations that can produce variant proteins include:

[0031] (1) The gene expression abundance of the transcript where the SNV / InDel is located is greater than 1;

[0032] (2) the variant allele frequency of the tumor sample was greater than or equal to 0.1;

[0033] (3) the variant allele frequency of the normal sample is less than or equal to 0.05;

[0034] (4) The number of sequencing reads covering the variant site in the tumor sample is greater than or equal to 5.

[0035] Preferably, the DNA variation results of the detected tumor tissues are annotated using VEP software and default parameters.

[0036] Preferably, the step S4 specifically includes:

[0037] S4.1. Gene fusions were detected using STAR-Fusion software, considering only fusion events that merged the coding regions of the parental genes; the conditions used to test gene fusions included:

[0038] a. The TPM of the fusion event is greater than or equal to 0.1;

[0039] b. The gene expression abundance of the fusion transcript is greater than 1 TPM;

[0040] S4.2. Use SUPPA2 software and set default parameters to detect RNA variable splicing events in tumor tissues and isoenzyme isomers with abundance greater than 1 TPM.

[0041] Preferably, the criteria for the candidate neoantigen polypeptide sequence are:

[0042] For SNV and InDel mutations, the mutation information is directly applied to the proteome reference to obtain 31 amino acid mutant peptides and normal peptides, and the peptides containing mutation points are cut into short peptides matching the length of the new antigen, 8-11 amino acids for HLA-I type and 15-25 amino acids for HLA-II type. If the mutation is at the beginning or end of the data transcript, the following and preceding 15-25 amino acid sequences are intercepted respectively. For fusion genes and abnormal shearing, the amino acid sequence corresponding to the transcript is directly used.

[0043] Another embodiment of the present invention provides a tumor neoantigen identification system based on second-generation sequencing data, including: an operation unit, a status viewing unit, an error finding unit, a re-execution analysis unit, a pause unit, a display task unit and an initialization unit; wherein the status viewing unit, the error finding unit, the re-execution analysis unit, the pause unit, the display task unit and the initialization unit are respectively connected to the operation unit.

[0044] Yet another embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method for identifying tumor neoantigens based on second-generation sequencing data as described in any of the above embodiments.

[0045] Yet another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method for identifying tumor neoantigens based on second-generation sequencing data as described in any of the above embodiments.

[0046] Compared with the prior art, the method and system for identifying tumor neoantigens based on second-generation sequencing data provided by the embodiment of the present invention have the following technical effects: The present invention provides a new variation detection scheme: 1. By integrating the sequencing data of the genome and transcriptome, four major genetic variations that produce neoantigens are detected simultaneously: SNV, InDel, gene fusion, and abnormal editing. 2. In order to reduce the false positive of genetic variation, the present invention uses two tools to detect each genetic variation at the same time, and then comprehensively analyzes the detection results of the two tools to expand the scope of variation screening, and finally uses the abundance change of gene expression to further verify the accuracy of the variation. In order to solve the problem of incomplete prediction of the types of HLA genotypes at present, the present invention proposes to use two new tools to predict the HLA-I genotype and HLA-Ⅱ genotype of patients to increase the number of candidate neoantigens. The present invention proposes to use neoantigen-MHC-I type binding affinity, neoantigen-MHC-Ⅱ type binding affinity, neoantigen-MHC binding stability, mutation frequency, mutant gene expression, etc. as indicators to screen out candidate neoantigens, and then sort the priority of candidate neoantigens by the proportion of indicator importance to obtain the final list of candidate neoantigens. In order to solve the problems of numerous analysis modules, slow operation speed and long time consumption in the new antigen prediction process, the present invention designs a new antigen prediction system, which can optimize the detection time of new antigens and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the implementation mode will be briefly introduced below. Obviously, the drawings described below are only some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A schematic flow chart of a method for identifying neoantigens in tumors based on next-generation sequencing data provided in an embodiment of the present invention.

[0049] Figure 2 The present invention provides patient data information schematically.

[0050] Figure 3 The paired sample information provided in the embodiment of the present invention is schematically shown.

[0051] Figure 4 A schematic diagram of the results of screening new antigen polypeptides provided in an embodiment of the present invention.

[0052] Figure 5 A schematic diagram of task states executed by a system provided in an embodiment of the present invention.

[0053] Figure 6A schematic diagram of a partial analysis process provided by an embodiment of the present invention.

[0054] Figure 7 Schematic diagram of a framework of a tumor neoantigen recognition system based on second-generation sequencing data provided by an embodiment of the present invention

[0055] Figure 8 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Figure 1 A flowchart of a method for identifying tumor neoantigens based on second-generation sequencing data provided in an embodiment of the present application, the method comprising steps S1 to S7:

[0058] S1. Obtain the patient's tumor sample and peripheral blood sample, extract the DNA and RNA of the tumor sample and the DNA of the peripheral blood sample respectively, construct a sequencing library and perform targeted capture of RNA and DNA, obtain DNA exome sequencing data of tumor tissue and blood tissue, and RNA transcriptome sequencing data of tumor tissue.

[0059] As a preferred solution, the standard for the amount of sequencing data is 30G bases for DNA and 10G bases for RNA.

[0060] S2. Quality control of next-generation sequencing data, including:

[0061] The DNA data of blood tissue, the DNA data of tumor tissue, and the RNA transcriptome data of tumor tissue were removed using Flexbar (v3.5.0) software to remove the adapter sequences, the sequences with a base mass score less than 15 accounting for more than 40%, and the sequences containing 5 or more N bases. The GC content of the bases was between 45% and 55%, and the proportion of bases with a quality value greater than 30 was more than 85%.

[0062] S3, using the reference genome as a control group to perform sequence comparison with the DNA data and RNA data after quality control, detect SNV and InDel mutations in peripheral blood, SNV and InDel mutations in tumor tissues, annotate and calculate the SNV and InDel mutation frequencies in blood tissues and tumor tissues, and calculate the gene expression abundance, thereby screening out mutations that can produce variant proteins. The following is a further explanation of step S3.

[0063] As a preferred embodiment of the present invention, the human reference genome is used as the reference genome.

[0064] Specifically, S3.1, first perform sequence comparison, specifically comparing DNA data with a reference genome and comparing RNA data with a reference gene.

[0065] Specifically, the S2 quality-controlled NDA sequence was aligned to the human reference genome using the BWA (v0.7.17) software MEN algorithm to obtain the alignment results. The alignment results were then sorted and an Index file was created. Sambamba (v1.0.0) software was used to mark / remove duplicate sequences. The S2 quality-controlled RNA sequence was then aligned to the human reference genome using STAR (v2.7.1). All of the above software used default parameters.

[0066] S3.2. Then, the detection of SNV and InDel mutations in peripheral blood and SNV and InDel mutations in tumor tissue mainly includes the following steps:

[0067] Detect SNV and InDel mutations in blood tissues: Use Strelka (v2.9.10) and Mutect2 (GATK, v.3.6) to compare the results of blood samples in S2 to the human reference genome to obtain the genetic variation results of blood samples;

[0068] Detection of SNV and InDel mutations in tumor tissues: The results of SNV and InDel mutations detected in blood tissues were used as normal controls, and Strelka (v2.9.10) and Mutect2 (GATK, v.3.6) software were used to detect mutations in tumor tissues after sequence comparison.

[0069] As a preferred solution, all the above software use default parameters.

[0070] S3.3. Perform SNV and InDel annotation and mutation frequency calculation. This process includes the following two steps:

[0071] Based on the detected results, VEP (v110.1) software was used with default parameters to annotate the tumor DNA variation results detected in s3.2;

[0072] Based on the results of detection of SNV and InDel mutations in peripheral blood and SNV and InDel mutations in tumor tissues, the SNV and InDel mutation frequencies in blood tissues and tumor tissues were calculated.

[0073] S3.4, and then calculate the gene expression abundance, specifically including the following steps:

[0074] According to the quality-controlled RNA sequencing data in S2, Salmon (v1.10.2) software was used to set the default parameters and quantify the transcript abundance in the RNA-seq data in TPM.

[0075] As a preferred solution, for polypeptides derived from SNV, InDel and abnormal splicing, the expression level is quantified as the expression level of the transcript where the SNV, InDel, gene fusion and abnormal splicing variation are located. For fusion-derived peptides, the expression level is quantified as the average of the two parental transcripts. Wherein, TPM: transcripts per million.

[0076] S3.5. Based on the detection results of SNV and InDel mutations in tumor tissues and steps S3.3-S3.4, further screening for mutations that can produce mutant proteins, specifically including the following steps:

[0077] Screening mutations that can produce protein variation: For SNV, only variants annotated as "missense_variant" are considered. For InDel, only variants annotated as "inframe_insertion", "inframe_deletion" or "frameshift_variant" are considered.

[0078] Further screening of variants that meet the following criteria:

[0079] (1) The abundance (TPM) of the transcript where the SNV / InDel is located is greater than 1;

[0080] (2) the variant allele frequency of the tumor sample was greater than or equal to 0.1;

[0081] (3) the variant allele frequency of the normal sample is less than or equal to 0.05;

[0082] (4) The number of sequencing reads covering the variant site in the tumor sample is greater than or equal to 5.

[0083] S4. Based on the sequence alignment results and gene expression abundance of the tumor tissue RNA sequencing data obtained in S3, gene fusion and abnormal splicing events of the tumor tissue are detected.

[0084] Specifically, based on the comparison results of RNA data with reference genes, this process includes two steps:

[0085] S4.1. Detection of gene fusion events: Gene fusions were detected using STAR-Fusion (v1.11.0) software, and only fusion events that merged the coding regions of the parental genes were considered, i.e., fusion events annotated as "INFRAME" and "FRAMESHIFT".

[0086] Among them, the supporting evidence used to weigh the detected fusion events has the following two conditions:

[0087] a. The TPM of the fusion event is greater than or equal to 0.1;

[0088] b. The gene expression abundance of the fusion transcript is greater than 1 TPM;

[0089] S4.2. Detection of variable splicing events: Use SUPPA2 (v2.3) software, set default parameters, detect variable splicing events of RNA in tumor tissues, and isoenzyme isomers with abundance greater than 1 TPM.

[0090] S5. Based on the quality-controlled blood sample DNA data obtained in S2, the HLA-I and HLA-II alleles are typed to predict the patient's HLA genotype.

[0091] It can be understood that in step S5, the HLA-I and HLA-II alleles are typed to predict the patient's HLA genotype. The Optitype (v1.3.5) software is used to set the default parameters to predict the patient's HLA-I genotype with a typing resolution of four bits; the HLA-HD (v1.0) software is used to set the default parameters to predict the patient's HLA-II genotype.

[0092] S6. Confirm the candidate neoantigen peptide sequence.

[0093] It can be understood that step S6 is to extract candidate polypeptide sequences based on the screening results of mutations that produce variant proteins, and its criteria include the following conditions:

[0094] For SNV and InDel mutations, the mutation information is directly applied to the proteome reference to obtain 31 amino acid mutant peptides and normal peptides, and the peptides containing mutation points are cut into short peptides that match the length of the new antigen, with a length of 8-11 amino acids for HLA-I type and 15-25 amino acids for HLA-II type. If the mutation is at the beginning or end of the data transcript, the following and preceding 15-25 amino acid sequences are intercepted respectively. For fusion genes and abnormal shearing, the amino acid sequence corresponding to the transcript is used directly.

[0095] S7. Predict the binding affinity and stability index of the neoantigen-MHC complex, and evaluate and optimize the neoantigen, including:

[0096] S7.1. Estimate the binding affinity between each peptide and each HLA class I and each HLA class II allele, and estimate the binding stability between each peptide and MHC;

[0097] It can be understood that the estimation method of this step S7.1 is as follows:

[0098] Prediction of neoantigen-MHC-I complexes: NetMHCpan (v4.1) software was used with default parameters to estimate the binding affinity between each peptide and each HLA class I allele;

[0099] Prediction of neoantigen-MHC-II complexes: NetMHCIIpan (v4.1) software was used with default parameters to estimate the binding affinity between each peptide and each HLA type II allele;

[0100] NetMHCstabpan (v1.0) software was used to set default parameters and estimate the binding stability between each peptide and MHC.

[0101] S7.2. Design conditions for screening new antigens, including:

[0102] A. Binding affinity is less than 500uM, and Rank_bf is obtained;

[0103] B. Compare the predicted binding score of the neoantigen with the predicted binding scores of a group of random natural peptides to obtain the percentage ranking of the predicted binding score of the neoantigen in the background, and screen out the neoantigens with a percentage ranking less than 2% to obtain Rank_pr;

[0104] Thus, the identification of the sequence of the new antigen is completed.

[0105] like Figure 2 As shown, an embodiment of the present invention provides a tumor neoantigen recognition system based on second-generation sequencing data, comprising:

[0106] An operation unit 51, a status checking unit 52, an error finding unit 53, a re-execution analysis unit 54, a pause unit 55, a display task unit 56 and an initialization unit 57; wherein the status checking unit 52, the error finding unit 53, the re-execution analysis unit 54, the pause unit 55, the display task unit 56 and the initialization unit 57 are respectively connected to the operation unit 51.

[0107] The operation unit 51 is used to start the analysis. Set the task name of this analysis, configure the patient ID list and the sample list to be sequenced. According to the input content, the system automatically allocates computing resources; according to the above functional modules, the analysis process is generated, and the running status of each process is monitored: submit, finish, none, error, check the results generated by each process, and synchronously record all analysis processes.

[0108] The status viewing unit 52 is used to view the tasks executed by the system and the progress of each task: the analysis modules that need to be executed, the modules being analyzed, the modules that have completed the analysis, and the modules with errors.

[0109] The error search unit 53 is used to find out the location and cause of the error in time through this command when an error occurs in the analysis process.

[0110] The re-execution analysis unit 54 is used to allow the task to continue executing from the error point after the error in a task flow is resolved.

[0111] The pause unit 55 is used to stop a certain analysis item being analyzed.

[0112] The task display unit 56 is used to display the detailed analysis process that needs to be executed for a task, and the ID, input, output, and parameters of each analysis module in the process.

[0113] The initialization unit 57 is used to initialize a task, execute the task from the beginning, and delete the existing record of the task.

[0114] The specific implementation of a tumor neoantigen identification device based on second-generation sequencing data in this embodiment can refer to the description of a tumor neoantigen identification method based on second-generation sequencing data in the above embodiment, which will not be repeated here.

[0115] The beneficial effects of the present invention will be further described below in conjunction with embodiments.

[0116] Example 1

[0117] Tumor tissue and peripheral blood samples were obtained from a lung cancer patient. DNA and RNA from the tumor tissue and DNA from the peripheral blood were extracted to construct a sequencing library. Whole exome sequencing was performed to obtain 30G of DNA data and 10G of RNA data.

[0118] The data analysis is as follows:

[0119] Step 1: Data preparation:

[0120] (1) Patient data information table to be analyzed: sample information table, paired sample information table. Figure 2 shown.

[0121] The sample name is the unique name of the sample. The library name is the name of the sample library. Different batches of library construction must have different library names. The lane name is the name of the same library construction, different sequencing, or the same sequencing different sequencing chips, which is used to match the target data. The same sample can have multiple libraries and multiple lanes. The classification indicates whether the sample is a DNA sequencing or RNA sequencing sample.

[0122] like Figure 3 As shown in the figure, the paired samples of the patient need to provide a pairing information table to describe the relationship between the clinical patient and the sequencing data. The case number is the unique number of each patient, and the control sample number is the sample number of the peripheral blood sample or adjacent tissue. The tumor sample number is the sample number of the tumor tissue. The transcriptome sample number is the sample number of the transcriptome sequencing of the tumor tissue. Gender is used to mark the gender information of the case.

[0123] Configuration file: parameters used by each analysis software in the functional module, thresholds for data screening, etc.

[0124] Step 2: The run command starts the functional analysis module to screen patients for new antigens.

[0125] (1) Quality control: Flexbar (v3.5.0) software was used to perform quality control on the sequencing data in the sample information table, including removing the sequencing adapters of each read, removing sequences with a base quality score less than 15 that accounted for more than 40% and reads containing 5 or more N bases, the total GC base content of the reads was between 45% and 55%, the proportion of reads with a base quality value greater than 30 was greater than 85%, and the average sequencing depth was greater than 200X. If any of the above indicators was not met, the analysis was terminated.

[0126] (2) NDA alignment: The NDA sequences after quality control in (1) were aligned to the human reference genome using the BWA (v0.7.17) software, using the parameters in the configuration file and the MEN algorithm to obtain the alignment results. The alignment results were then sorted and an Index file was created. The Sambamba (v1.0.0) software was then used to mark and remove duplicate sequences.

[0127] (3) RNA alignment: The RNA sequences after quality control in (1) were aligned to the human reference genome using STAR (v2.7.1) using the parameters in the configuration file.

[0128] (4) Tumor SNV and InDel mutation detection: Based on the results of (2), Strelka (v2.9.10) and Mutect2 (GATK, v.3.6) were used to align the results of the blood samples in (2) to the human reference genome to obtain the blood SNV and InDel mutation results. Blood SNV and InDel were then used as normal controls, and Strelka and Mutect2 software were used to detect tumor SNV and InDel mutations in (2) and output the mutation detection results. Strelka (v2.9.10) and Mutect2 (GATK, v.3.6) used the parameters in the configuration file.

[0129] (5) SNV, InDel annotation and mutation frequency calculation: Based on the results in (4), the biological functions of tumor SNV and InDel mutations were annotated using VEP (v110.1) software. The mutation frequencies of SNV mutations in blood and tumor tissues were then calculated respectively. VEP (v110.1) used the parameters in the configuration file.

[0130] (6) Calculation of gene expression abundance: Based on the quality-controlled RNA results of (2), Salmon (v1.10.2) was used to quantify the transcript abundance of tumor tissues in TPM. For peptides derived from SNVs, InDels, and abnormal splicing, the expression level was quantified as the expression level of the transcript where the SNV, InDel, and abnormal splicing were located. For fusion-derived peptides, the expression level was quantified as the average of the two parental transcripts. TPM: transcripts per million. Salmon (v1.10.2) uses the parameters in the configuration file.

[0131] (7) Detection of gene fusion and abnormal splicing: Based on the results of (3), STAR-Fusion (v1.11.0) software was used to detect gene fusion events in tumors. Only fusion events that merged the coding regions of the parental genes and those annotated as "INFRAME" and "FRAMESHIFT" were considered. The detected fusion events must meet the following two conditions: (1) the FFPM of the fusion event is greater than or equal to 0.1 (at least one fusion supporting fragment per million reads); (2) the abundance of the fusion transcript is greater than 1 TPM (the quantification method of transcripts will be introduced below). SUPPA2 (v2.3) software was used to detect abnormal splicing events of RNA in tumor tissues in S2-3, and isoenzyme isomers with an abundance greater than 1 TPM. STAR-Fusion (v1.11.0) and SUPPA2 (v2.3) use the parameters in the configuration file.

[0132] (8) Screening of effective variants: Based on the results in (4), (5), (6), and (7), mutations that can produce variant proteins are screened. For SNV, only variants annotated as "missense_variant" are considered. For InDel, only variants annotated as "inframe_insertion", "inframe_deletion", or "frameshift_variant" are considered. At the same time, the SNV and InDel mutations screened must meet the following conditions: (1) The abundance (TPM) of the transcript where the SNV and InDel are located is greater than 1; (2) The variant allele frequency of the tumor sample is greater than or equal to 0.1; (3) The variant allele frequency of the normal sample is less than or equal to 0.05; (4) The number of sequencing reads covering the variant site in the tumor sample is greater than or equal to 5.

[0133] (9) Predicting the patient's HLA genotype: Based on the blood sample DNA data after quality control in (2), the Optitype (v1.3.5) software was used to predict the patient's HLA-I genotype, with a typing resolution of four digits; the HLA-HD (v1.0) software was used to predict the patient's HLA-II genotype. The Optitype (v1.3.5) software and the HLA-HD (v1.0) software used the parameters in the configuration file.

[0134] (10) Determine the candidate new antigen peptide sequence. For SNV and InDel, the mutation information is directly applied to the proteome reference to obtain 31 amino acid mutant peptides and normal peptides, and the peptides containing the mutation points are cut into short peptides that match the length of the new antigen: 8-11 amino acids for HLA-I type and 15-25 amino acids for HLA-II type. If the mutation is at the start or end of the transcript, the following and preceding 15 to 25 amino acid sequences are truncated respectively. For fusion genes and abnormal shearing, the amino acid sequence corresponding to the transcript is used directly.

[0135] (11) Calculate the binding affinity and stability index of the new antigen-MHC complex. Based on the results of (9) and (10), use NetMHCpan (v4.1) software to estimate the binding affinity between each peptide and each MHC-I molecule; use NetMHCIIpan (v4.1) software to estimate the binding affinity between each peptide and each MHC-II molecule; use NetMHCstabpan (v1.0) software to estimate the binding stability between each peptide and MHC. NetMHCpan (v4.1) NetMHCIIpan (v4.1) and NetMHCstabpan (v1.0) use the parameters in the configuration file.

[0136] (12) Evaluation and optimization of neoantigens. Based on the results in (11), eligible candidate neoantigens are screened according to the following conditions: (1) binding affinity less than (bf) 500uM, (2) the predicted binding score of the neoantigen is compared with the predicted binding score of a group of random natural peptides to obtain the percentage ranking (pr) of the predicted binding score of the neoantigen in the background, and the neoantigens with a percentage ranking less than 2% are screened out, and (3) the neoantigens with a binding stability (bs) greater than 1h are screened out. Then obtain the ranking results of the neoantigens under each condition: Rank_bf, Rank_pr, Rank_bs, and then add the three ranking results together to obtain the final score of each neoantigen according to the following formula.

[0137] Formula: Score_neo=Rank_bf*0.5+Rank_pr*0.3+Rank_bs*0.2

[0138] The results of the screened new antigen peptides are as follows Figure 4 shown.

[0139] Example 2

[0140] Operation of the dispatch system

[0141] Status viewing unit: View the status of tasks executed by the system, such as Figure 5 As shown:

[0142] Project_Name: project name, Submit_Time: project submission time, Finished_Time: project completion time, State: project execution status (running, done, error), Total: tasks that need to be analyzed in the project, Queued: waiting tasks, Running: running tasks, Done: completed tasks, Error: tasks with errors.

[0143] Display task unit: Display the detailed analysis process that needs to be performed for a task, including the ID, input, output, and parameters of each analysis module in the process. Figure 6 As shown, part of the analysis process of the pipeline_R21010 project is shown:

[0144] ID: subtask number, name: subtask name, program: task name, input: input data, output output data, sample: sample number, param: parameters used by task module software, info: format of input data, state: subtask execution status (submit, finish, error).

[0145] Error finding unit: When an error occurs in the system, the cause of the error can be detected through daemon+task name.

[0146] Re-execute the analysis unit: After clearing the error, resume+task name to restart the analysis process from the error point.

[0147] Pause Unit: Stops the analysis project being analyzed.

[0148] Initialization unit: ini+task name, initialize the task and execute the task from the beginning. If the task has been executed, the existing analysis results of the task will be deleted.

[0149] like Figure 7As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310 and a processor 320, wherein the memory 310 is used to store one or more computer instructions, and the processor 320 is used to call and execute the one or more computer instructions, so as to implement any of the above-mentioned methods for identifying tumor neoantigens based on second-generation sequencing data.

[0150] That is, the electronic device 300 includes: a processor 320 and a memory 310, in which computer program instructions are stored, wherein when the computer program instructions are executed by the processor, the processor 320 executes any of the above-mentioned methods for identifying tumor neoantigens based on second-generation sequencing data.

[0151] Furthermore, if Figure 8 As shown, the electronic device 300 further includes a network interface 330 , an input device 340 , a hard disk 350 , and a display device 360 ​​.

[0152] The above-mentioned interfaces and devices can be interconnected through a bus architecture. The bus architecture can be a bus and bridge that can include any number of interconnected buses. Specifically, one or more central processing units (CPUs) represented by processor 320 and various circuits of one or more memories represented by memory 310 are connected together. The bus architecture can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits together. It can be understood that the bus architecture is used to achieve connection and communication between these components. In addition to the data bus, the bus architecture also includes a power bus, a control bus, and a status signal bus, which are all well known in the art, so they will not be described in detail herein.

[0153] The network interface 330 can be connected to a network (such as the Internet, a local area network, etc.), obtain relevant data from the network, and save it in the hard disk 350.

[0154] The input device 340 can receive various instructions input by the operator and send them to the processor 320 for execution. The input device 340 can include a keyboard or a pointing device (for example, a mouse, a trackball, a touch pad or a touch screen, etc.).

[0155] The display device 360 ​​can display the result obtained by the processor 320 executing the instruction.

[0156] The memory 310 is used to store programs and data necessary for the operation of the operating system, as well as data such as intermediate results during the calculation process of the processor 320.

[0157] It is understood that the memory 310 in the embodiment of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. The memory 310 of the apparatus and method described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0158] In some implementations, the memory 310 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system 311 and application programs 312 .

[0159] The operating system 311 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 312 includes various application programs, such as a browser, etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present invention can be included in the application 312.

[0160] The processor 320, when calling and executing the application and data stored in the memory 310, specifically, the program or instruction stored in the application 312, takes the patient's tumor sample and peripheral blood sample, extracts DNA and RNA of the tumor sample and DNA of the peripheral blood sample, constructs a sequencing library and performs targeted capture of RNA and DNA, obtains DNA data of tumor tissue and blood tissue, and RNA transcriptome data of tumor tissue; performs quality control on the second-generation sequencing data; uses the reference genome as a control group to perform sequence comparison with the DNA data and RNA data after quality control, and detects peripheral blood SNV, InDel mutation, tumor The SNV and InDel variations of tissues are annotated and the SNV and InDel mutation frequencies of blood tissues and tumor tissues are calculated, and the gene expression abundance is calculated to screen out mutations that can produce mutant proteins; based on the sequence alignment results and gene expression abundance of the tumor tissue RNA sequencing data obtained, gene fusion and abnormal splicing events of tumor tissues are detected; based on the quality-controlled blood sample DNA data obtained, HLA-I and HLA-II alleles are typed to predict the patient's HLA genotype; candidate neoantigen polypeptide sequences are confirmed; the binding affinity and stability indicators of the neoantigen-MHC complex are predicted, and the evaluation and optimization of the neoantigen is performed at the same time.

[0161] The method for identifying tumor neoantigens based on second-generation sequencing data disclosed in the above embodiment of the present invention can be applied to the processor 320, or implemented by the processor 320. The processor 320 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 320. The above-mentioned processor 320 can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiment of the present invention can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 310, and the processor 320 reads the information in the memory 310 and completes the steps of the above method in combination with its hardware.

[0162] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application or a combination thereof.

[0163] For software implementation, the techniques described herein can be implemented by modules (e.g., procedures, functions, etc.) that perform the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0164] Specifically, the processor 320 is also used to read the computer program and execute any of the above-mentioned methods for identifying tumor neoantigens based on second-generation sequencing data.

[0165] The present application also provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the above method, such as executing the method executed by the above electronic device, which is not repeated here.

[0166] Optionally, the storage medium involved in the present application, such as computer-readable storage medium, may be non-volatile or volatile.

[0167] Optionally, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of blockchain nodes, etc. Among them, the blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods, each of which contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. The blockchain may include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0168] It should be noted that, for the above-mentioned various method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0169] In the several embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0170] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0171] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform some steps of the sending and receiving method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (RandomAccess Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0172] The above disclosures are only some preferred embodiments of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiments and equivalent changes made according to the claims of the present invention are still within the scope of the invention.

Claims

1. A method for identifying new tumor antigens based on second-generation sequencing data, characterized in that: The method comprises the following steps: S1. Obtain tumor samples and peripheral blood samples from patients, extract DNA and RNA from tumor samples and DNA from peripheral blood samples, construct sequencing libraries and perform targeted capture of RNA and DNA, obtain DNA exon sequencing data of tumor tissues and blood tissues, and RNA transcriptome sequencing data of tumor tissues; S2. Quality control of the next-generation sequencing data, including: For DNA data from blood tissue, DNA data from tumor tissue, and RNA transcriptome data from tumor tissue, Flexbar software was used to remove adapter sequences, sequences with a base mass score less than 15 that accounted for more than 40%, and sequences containing 5 or more N bases. The GC content of the bases was between 45% and 55%, and the proportion of bases with a quality value greater than 30 was greater than 85%. S3. Use the reference genome as the control group to perform sequence comparison with the quality-controlled DNA data and RNA data, detect SNV and InDel mutations in peripheral blood, SNV and InDel mutations in tumor tissues, annotate and calculate the SNV and InDel mutation frequencies in blood tissues and tumor tissues, and calculate the gene expression abundance, thereby screening out mutations that can produce variant proteins; The screening conditions for screening out mutations that can produce variant proteins include: (1) The gene expression abundance of the transcript where the SNV / InDel is located is greater than 1; (2) the variant allele frequency of the tumor sample was greater than or equal to 0.1; (3) the variant allele frequency of the normal sample is less than or equal to 0.05; (4) the number of sequencing reads covering the variant site in the tumor sample was greater than or equal to 5; S4. Based on the sequence alignment results and gene expression abundance of the tumor tissue RNA sequencing data obtained in S3, gene fusion and abnormal splicing events of the tumor tissue are detected, including: S4.

1. Gene fusions were detected using STAR-Fusion software, considering only fusion events that merged the coding regions of the parental genes; the conditions used to test gene fusions included: a. The TPM of the fusion event is greater than or equal to 0.1; b. The gene expression abundance of the fusion transcript is greater than 1 TPM; S4.

2. Use SUPPA2 software with default parameters to detect RNA alternative splicing events in tumor tissues and isoenzyme isomers with abundance greater than 1 TPM; S5, typing the HLA-I and HLA-II alleles according to the quality-controlled blood sample DNA data obtained in S2, and predicting the HLA genotype of the patient; S6. Confirm the candidate neoantigen peptide sequence; S7. Predict the binding affinity and stability index of the neoantigen-MHC complex, and evaluate and optimize the neoantigen, including: S7.

1. Estimate the binding affinity between each polypeptide and each HLA type I and each HLA type II allele, and estimate the binding stability between each polypeptide and MHC; S7.

2. Design conditions for screening new antigens, including: A. Binding affinity is less than 500uM, and Rank_bf is obtained; B. Compare the predicted binding score of the neoantigen with the predicted binding scores of a group of random natural peptides to obtain the percentage ranking of the predicted binding score of the neoantigen in the background, and screen out the neoantigens with a percentage ranking less than 2% to obtain Rank_pr; C. The new antigen with binding stability greater than 1 h is obtained by Rank_bs; S7.

3. The conditions for screening new antigens are added together to obtain a scoring formula, and the score Score_neo of the new antigen is calculated, which is expressed as follows: Score_neo = Rank_bf*0.5+Rank_pr*0.3+Rank_bs*0.

2.

2. A method for identifying tumor neoantigens based on second-generation sequencing data according to claim 1, characterized in that: The standard for sequencing data volume is 30G bases for DNA and 10G bases for RNA.

3. The method for identifying tumor neoantigens based on second-generation sequencing data according to claim 1, characterized in that: In S3, the reference genome is used as a control group to perform sequence comparison with the quality-controlled DNA data and RNA data, specifically: the quality-controlled DNA sequence is aligned to the human reference genome using the MEN algorithm of the BWA software, the alignment results are obtained and sorted, and an Index file is created, and then the Sambamba software is used to mark or remove duplicate sequences; the quality-controlled RNA sequence is aligned to the human reference genome using the STAR software; The detection of peripheral blood SNV, InDel mutations, tumor tissue SNV, InDel mutations includes: Use Strelka and Mutect2 to align the sequence comparison results of the blood samples to the human reference genome to obtain the gene variation results of the blood samples; The basic mutation results were used as the control group, and Strelka and Mutect2 software were used to detect mutations in tumor tissues based on the sequence comparison results of tumor tissues.

4. A method for identifying tumor neoantigens based on second-generation sequencing data according to claim 3, characterized in that: VEP software and default parameters were used to annotate the DNA variation results detected in tumor tissues.

5. The method for identifying tumor neoantigens based on second-generation sequencing data according to claim 1, characterized in that: The criteria for the candidate neoantigen polypeptide sequences are: For SNV and InDel, the mutation information is directly applied to the proteomic reference to obtain mutant peptides and normal peptides of 31 amino acids, and the peptides containing the mutation sites are cut into short peptides matching the length of the new antigen, with a length of 8-11 amino acids for HLA-I type and 15-25 amino acids for HLA-II type. If the mutation is at the start or end of the data transcript, the following and preceding 15 to 25 amino acid sequences are truncated respectively. For fusion genes and abnormal shearing, the amino acid sequence corresponding to the transcript is used directly.

6. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the tumor neoantigen identification method based on second-generation sequencing data as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the method for identifying tumor neoantigens based on second-generation sequencing data as described in any one of claims 1-5.

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