MRNA (messenger ribonucleic acid) vaccine for targeting breast cancer cell line as well as preparation method and application of mRNA vaccine

Abstract

The invention relates to an mRNA (messenger Ribonucleic Acid) vaccine targeting breast cancer cell line neoantigen epitopes as well as a preparation method and application thereof, and belongs to the technical field of tumor mRNA vaccines. According to the invention, new autoantigen epitopes are obtained by mutation screening in tumor cells through biosignal analysis, and the new autoantigen epitopes show better immunocompetence when being applied to mRNA vaccines.

Description

Technical Field

[0001] The present invention relates to an mRNA vaccine targeting breast cancer cell lines and a preparation method and application thereof, belonging to the technical field of tumor mRNA vaccines. Background Art

[0002] Tumor immunotherapy can control and kill tumor cells by stimulating or mobilizing the body's immune system and enhancing the anti-tumor immunity of the tumor microenvironment. Under normal circumstances, mutations that cause tumors can produce new self-antigen epitopes, also known as neoantigens. Neoantigen epitopes released by tumor cells can be captured by antigen-presenting cells, which can start and activate effector T cells in the body through antigen presentation. When effector T cells migrate and infiltrate tumor tissues, they can specifically recognize tumor cells with neoantigen epitopes, thereby achieving tumor cell clearance.

[0003] Since neoantigen epitopes are only expressed by tumor cells, neoantigen epitope-based tumor immunotherapy can trigger true tumor-specific T cell responses, thereby preventing "off-target" damage to non-tumor tissues. In addition, specific T cell responses activated by neoantigen epitopes persist and provide the potential for immune memory after treatment, which provides the possibility of long-term prevention of disease recurrence. However, tumor cells normally release fewer antigen epitopes and cannot efficiently activate the tumor-immune cycle.

[0004] In recent years, mRNA technology has been booming. The widespread use of the new crown mRNA vaccine has proved the safety, efficiency and low-cost rapid development of mRNA technology. Based on the central law that mRNA can encode proteins, the construction of mRNA vaccines encoding tumor neoantigen epitopes can controllably and continuously express tumor neoantigens, activate the tumor-immune cycle in the body, and achieve efficient clearance of tumor cells. At the same time, with the rapid development of high-throughput gene sequencing and bioinformatics analysis technology, the screening and identification of neoantigen epitopes have been greatly promoted, which is of great significance for the development of personalized cancer vaccines.

[0005] However, tumor cells have many mutations, and the tumor information of different tumor patients is also quite different. Only effective new antigen epitopes can activate the patient's tumor-immune cycle and achieve clinical benefits for the patient. How to efficiently and accurately screen the patient's personalized tumor new antigen epitopes is the key to new antigen mRNA therapy and is also a hot research and development hotspot at this stage. Summary of the invention

[0006] The first aspect of the present invention provides a bioinformatics process for screening personalized neoantigen epitopes for tumors, and establishes a scoring mechanism for a large number of screened neoantigen epitopes, which can accurately screen out effective tumor neoantigen epitopes from a large number of neoantigen epitopes for the development of mRNA vaccines.

[0007] In the second aspect of the present invention, new autoantigen epitopes were screened from mutations occurring in tumor cells through bioinformatics analysis, which showed good immune activity when applied to mRNA vaccines, thereby obtaining mRNA vaccines that can target and treat melanoma, breast cancer, and pancreatic cancer, respectively.

[0008] An isolated DNA molecule comprising a coding region, wherein the coding region comprises a nucleotide sequence for encoding a peptide having an amino acid sequence as shown below;

[0009] The first type: any one or several of SEQ ID NO.1-11 in series;

[0010] The second type: any one or several of SEQ ID NO.34-44 are connected in series.

[0011] The coding region also has a 5' untranslated region and a 3' untranslated region upstream and downstream, respectively.

[0012] In the DNA molecule, the coding region contains a sequence for encoding a peptide obtained by sequentially connecting amino acid sequences such as SEQ ID NO.1-11, or a sequence for encoding a peptide obtained by sequentially connecting amino acid sequences such as SEQ ID NO.34-44.

[0013] When amino acid sequences are connected in series, they are connected through a Linker sequence.

[0014] The amino acid sequence also contains a MITD domain and a signal peptide.

[0015] The DNA molecule contains the sequence shown in SEQ ID NO.32 or SEQ ID NO.57.

[0016] A protein having a sequence of any one or several peptides in SEQ ID NOs. 1-11, or having a sequence of any one or several peptides in SEQ ID NOs. 34-44.

[0017] The protein has an amino acid sequence as shown in SEQ ID NO.26, or an amino acid sequence as shown in SEQ ID NO.56.

[0018] An antigen expression vector comprises the isolated DNA molecule mentioned above.

[0019] The carrier is a liposome.

[0020] An mRNA vaccine comprises the above-mentioned antigen expression vector.

[0021] The use of the above-mentioned isolated DNA molecules, proteins, antigen expression vectors or mRNA vaccines in the preparation of melanoma and breast cancer vaccines.

[0022] The mRNA vaccine also includes a pharmaceutically acceptable carrier, liposome nanoparticles, wherein the cationic liposomes are composed of four lipid components, including cationic lipids, neutral auxiliary lipids, cholesterol and PEGylated lipids. The cationic lipids include at least one of SM102, ALC-0315, and DLin-MC3-DMA, and the neutral lipids include at least one of DSPC, DOPE, DOPC, DOPG, and DOPS.

[0023] A method for screening antigen sequences of mRNA vaccines, comprising the following steps:

[0024] Step 1: After RNA and DNA are extracted from the tumor cell lines, transcriptome sequencing and WES sequencing are performed respectively;

[0025] Step 2: Perform quality control on the data from WES sequencing and compare it to the reference genome to obtain somatic SNV and indel mutation sites; perform quality control on the data from transcriptome sequencing and compare it to the reference genome to obtain expression level data;

[0026] Step 3, for each variant site obtained in step 2, a window size range of different amino acid lengths is set, and the fragments where the variant site is located are traversed in sequence within the range. In each traversal, the sequence region covered by the window containing the variant site is traversed and slid in sequence, and a set of amino acid fragments that have been traversed and slid for the variant site is obtained in sequence;

[0027] Step 4: for each variant site, calculate the affinity between the fragments in the set obtained in step 3 and the MHC protein, then calculate the median or average value of the affinity, and select the mutation site corresponding to the median or average value lower than the threshold;

[0028] Step 5: Obtain the epitope score for each mutation site according to the following formula:

[0029] TotalScore=Binding Score×Expressionscore

[0030] BindingScore is the affinity score, which is calculated by summing the affinities of a set of amino acid sequences obtained through sliding and traversing steps of the mutation sites selected in step 4 and each MHC allele, and then normalizing them;

[0031] ExpressionScore is the expression level of the location selected in step 4 in the transcript;

[0032] The mutation sites with higher scores are selected, and the peptides where they are located are used as antigens alone or in series.

[0033] In step 4, it is also necessary to filter out peptides with a wild-type to mutant affinity ratio lower than 2; the affinity is obtained by averaging the results of predictions and calculations using at least two software.

[0034] The expression level of the transcript was determined by the number of reads supporting the mutation in the RNA sequencing data.

[0035] The peptide fragments below the threshold value refer to those with affinity lower than 2000nM, 1500nM, 1000nM or 500nM, and the threshold value is the average or median value of the affinity between the fragment and each MHC allele.

[0036] When concatenating, the sequences are concatenated in order of their scores, with the sequence with the highest score at the 5' end.

[0037] The step of screening the mutation data is also included, including one or more of the following conditions:

[0038] If the sequencing depth of the mutation site in the RNA sequencing data is less than 10, the mutation is filtered out;

[0039] If the allele frequency of the mutation site is lower than 0.5%, the mutation is filtered out;

[0040] If the number of fragments containing the mutation site in the RNA sequencing data is less than 3, the mutation is filtered out.

[0041] In step 5, the length of the peptide segment is 10-40 amino acids, and is obtained by extending 5-20 amino acids to the left and / or right with the mutation site as the center. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the WES and RNAseq process required for tumor neoantigen screening;

[0043] Figure 2 This is a flow chart for bioinformatics screening and scoring of personalized neoantigen epitopes for tumors;

[0044] Figure 3It is a schematic diagram of sliding window and traversal of mutation sites.

[0045] Figure 4 This is a schematic diagram of the design method of personalized tumor neoantigen epitope mRNA vaccine;

[0046] Figure 5 This is a schematic diagram of the animal experimental administration of the B16F10 cell line neoantigen epitope mRNA vaccine;

[0047] Figure 6 This is a graph showing the changes in tumor growth curves of mice given the neoantigen mRNA vaccine in different groups of the B16F10 cell line;

[0048] Figure 7 This is a curve chart showing the changes in the survival rate of mice given the neoantigen mRNA vaccine in different groups of the B16F10 cell line;

[0049] Figure 8 This is a schematic diagram of the animal experimental administration of the 4T1 cell line neoantigen epitope mRNA vaccine;

[0050] Figure 9 This is a graph showing the changes in tumor growth curves of mice given neoantigen mRNA vaccines of different groups of 4T1 cell lines;

[0051] Figure 10 This is a curve chart showing the changes in the survival rate of mice given the neoantigen mRNA vaccine in different groups of the 4T1 cell line; DETAILED DESCRIPTION

[0052] The bioinformatics screening and scoring process of personalized tumor neoantigen epitopes involved in the present invention is as follows: Figure 1 As shown in the figure, the WES process of tumor cell DNA and normal tissue DNA used for new antigen epitope screening and the RNAseq process of tumor tissue RNA are shown in the figure. Figure 2 As shown, the details are as follows:

[0053] The tumor tissue used in the present invention is derived from tumor cell lines derived from different types of mice; the normal tissue used in the present invention is derived from the mouse tail tissue of the mouse species corresponding to the mouse-derived tumor cell line;

[0054] The method for extracting DNA and RNA from cells and tissues used in the present invention can be carried out by any existing method, and the present invention does not make specific limitations; in all embodiments of the present invention, various adherent mouse tumor cells that have been cultured in large quantities are washed twice with PBS buffer, and then trypsin is selected for complete digestion, and the cell pellet is obtained by centrifugation, and the RNA and DNA of the target cells are further extracted using the Trizol method; for the mouse tail tissue, the hair is shaved and then the tissue is digested to obtain normal tissue cells, which are washed twice with PBS buffer and then centrifuged to obtain the cell pellet, and the RNA and DNA of the target cells are further extracted using the Trizol method.

[0055] After obtaining DNA and RNA samples, Figure 2 The process diagram is as follows: WES (whole exome sequencing) and RNAseq (transcriptome sequencing) processes are carried out. The main processes include DNA / cDNA fragmentation, library construction, enrichment and NGS sequencing analysis; for DNA samples, the DNA clinical library construction process is selected for library construction, and Agilent's SureSelect non-human exon capture probe is further used to enrich the whole exon of the library. The enriched library is PCR amplified and amplified library purified according to the library quality, enrichment starting amount and throughput, and is sequenced on the machine according to the machine platform, the type of probe used for the sample and the sequencing depth; for RNA samples, the rRNA in the extracted sample RNA is removed, and then RNA is sheared, and the standard process of RNA-seq library construction is selected to construct the library, and the library is sequenced on the machine after passing the 2100 quality inspection;

[0056] After obtaining the data from the machine, the system mutation analysis was performed through the WES data and the expression analysis of the transcripts was performed through the RNAseq data. The WES data was analyzed in pairing mode. First, the raw data from the machine was preprocessed. The software fastp (version 0.20.0) was used to remove the sequencing adapters, low-quality bases and sequencing fragments, and the sequences with a quality score of less than 10 accounting for 30% of the entire sequence and the sequences with a high proportion of N bases were removed. The sequencing fragments that passed the quality control were aligned to the reference genome of the mouse (GRCm38 version) using the bwa (v0.7.17-r1188) software. The sequencing fragments that were successfully aligned were removed according to the coordinate start and end positions of the corresponding genome. The intermediate files of these steps will count the raw sequencing data, the quality control qualified data, and the related information of the alignment data, including the total number of sequencing fragments, the total number of bases, Q30, the alignment rate, the target interval alignment rate and other indicators. Specific quality control indicators include: the original WES data volume is not less than 10G, QC30 is not less than 90%, more than 60% of WES reads can be aligned to the exon region of the mouse reference genome (GRCm38), and the average depth after removing duplicates is not less than 100X. The comparison result files of tumor tissue and normal tissue will be used for system mutation detection analysis.

[0057] The present invention uses the open source software Haplotypercaller to detect somatic SNV and indels. Exclude mutation sites with a coverage depth of less than 15X, a mutation frequency of less than 2%, and strand preference, and perform transcript, amino acid sequence, and function annotations on the detected somatic mutation sites, and filter out synonymous mutation sites.

[0058] The novel antigen epitope prediction process involved in the present invention utilizes a tool pVACtools (3.0.3) that integrates multiple softwares to annotate the detected system mutations, consider the amino acid changes caused by the translation of each mutation, and predict the full-length amino acids of the wild type and mutant before and after the mutation. By sliding and traversing the amino acid fragments containing the mutation, a group of amino acid fragments corresponding to the mutation site are obtained, and the affinity of these amino acid fragments to MHC-like molecule proteins is calculated respectively, and then the average value or median value is calculated. By obtaining the average value or median value at each mutation site, a part of the sites with affinity below the threshold can be screened out. The specific sliding and traversal are as follows: Figure 3As shown, specifically: the sliding window size can be set accordingly, for example, the window size of 5-20 amino acids can be selected in sequence, and the window size can be set manually. In the following embodiments, a window of 8-14 amino acids in length is selected in sequence, that is, a window of 8, 9, 10, ..., 14 amino acids in length is used in sequence. In each fixed-length window, the window is slid in sequence along the position containing the mutation site (that is, during the sliding process of the window, the window fragment must contain the mutation site), and then a group of protein peptide chain sequences under the sliding of the window along the mutation site position under different window size conditions can be obtained respectively, and their MHC affinities are calculated respectively. The process of window traversal and sliding can be obtained by sliding window calculation of pVACtools software. Based on the simulation of the sliding window, the present invention can obtain all possible new antigen epitopes containing the single mutation for MHC affinity analysis, so that the best new antigen epitope corresponding to each mutation can be obtained as a candidate new antigen epitope.

[0059] Furthermore, the present invention uses software such as MHCflurry (2.0.1), NetMHCIIpan v4.0, NetMHCconsv1.1, NetMHCpan v4.1, SMMalign, Nnalign and other software to predict the binding affinity of a single peptide segment to the mouse MHC protein. By integrating the analysis results of multiple MHC affinity prediction software, the present invention can select the median affinity of different software for a single predicted new antigen epitope prediction result as the affinity value of a single antigen peptide. Therefore, the new antigen screening of the present invention can integrate the advantages of multiple prediction software, avoid the prediction bias of a single software, and improve the accuracy of MHC affinity prediction and the screening accuracy of new antigen epitopes.

[0060] Generally, when the affinity is lower than 500nM, the peptide is considered to be an antigenic peptide that can be transported by MHC. Therefore, all peptides with an affinity lower than 500nM are screened, that is, the median value predicted by peptides of different lengths / positions obtained by sliding windows and traversing at the same site using different software is used as the median affinity value of the peptide corresponding to the mutation. In this step, the affinity difference between wild-type peptides and mutant peptides is also considered, and the ratio of wild-type affinity to mutant affinity should be greater than or equal to 2 for subsequent MHC affinity scoring.

[0061] After completing the affinity screening of the mutation sites, the score needs to be calculated. In the scoring process, the affinity score of each mutation site needs to be obtained. The specific steps are: for the candidate tumor neoantigen epitope sites obtained by WES data screening, the MHC affinity corresponding to the mutation site is used to perform the MHC affinity score (Binding Score). The score calculation formula is as follows:

[0062]

[0063]

[0064] Binding Score is an indicator that measures the affinity between multiple amino acids obtained by sliding window and traversal operations and each MHC allele. The calculation method is to predict the affinity of the mutant amino acid obtained by sliding window and traversal operations with each MHC allele, and standardize the affinity IC50 to a value in the range of 0-1. The sum of multiple values ​​is used as the Binding Score. σ is a normalization function.

[0065] For the whole transcriptome RNAseq data of tumor tissue, the software fastp (version 0.20.0) was used to remove low-quality sequences and connectors, and sequences with a mass score of less than 10 bases accounting for 30% of the entire sequence and sequences with a high proportion of N bases were removed. The present invention also performs qualified data quality control on the RNA raw data. The specific quality control indicators are: the amount of RNA raw data exceeds 30 million 150bp pair-end reads, QC30 is not less than 90%, and more than 90% of reads can be mapped to the mouse mm10 transcriptome region. Next, the RNA data is aligned using the highly sensitive alignment method STAR (v2.7.10a) for detecting splicing sites to obtain RNA alignment results, and the reference genome and transcriptome versions are Mus_musculus.GRCm38.102. The sequencing data after quality control is aligned to the reference genome using Bowtie2 (v 2.2.9) to obtain alignment results and sort the alignment results. For RNA-Seq, repeated sequence sequences are often a natural consequence of highly expressed transcripts, so for differential analysis, no labeling / removal of repeated sequences is performed.

[0066] The present invention performs transcriptome analysis on tumor tissue RNA, aligns RNA sequencing data to the corresponding mouse reference genome, analyzes differential gene expression, and quantifies normalized expression levels using the FPKM index (Fragments Per Kilobase of exon model per Million mapped fragments, i.e., fragments per thousand bases of transcription per million mapped reads), which is calculated as follows:

[0067]

[0068] Wherein, total exon fragments: all reads on the exons of a certain gene that a certain sample is aligned to; mapped reads (millions): the sum of all reads of a certain sample; exon length (KB): the length of a certain gene (the sum of the length of the exons, in KB). The present invention uses RSEM software (v 1.3.3) to calculate the FPKM value to evaluate the expression of the gene, so as to know whether the gene of the detected somatic mutation is expressed, and is used for the subsequent screening of somatic mutation sites located on the expressed gene. Genes with FPKM values ​​greater than 0 are considered to be genes that can be expressed normally; in order to eliminate background noise, the threshold of FPKM is increased to 1.

[0069] At the same time, the present invention filters mutation files based on the comparison results of RNA-seq data. If the sequencing depth of the mutation site in the RNA comparison bam file is less than 10, or the mutation allele frequency is lower than 0.5%, or the number of mutation allele fragments is less than 3, the mutation site will be filtered and will not be included in the scope of new antigen epitope prediction.

[0070] The mutation file obtained based on WES data will add the FPKM values ​​of the genes and transcripts corresponding to each mutation site according to the numbers of the corresponding genes and transcripts, which will serve as the input file for screening tumor neoantigen epitopes.

[0071] Based on the analysis of RNAseq data, the present invention selects the expression level of the transcript at the mutation site for gene expression scoring, and the scoring calculation formula is as follows:

[0072]

[0073] Wherein, Supporting reads refers to the number of reads supporting the mutation in the RNA data. The present invention uses the square root of this value as the value of the transcript gene expression score Expression Score.

[0074] The present invention further integrates the MHC affinity score (Binding Score) corresponding to the mutation site and the gene expression score (Expression Score) of the expression level of the transcript where the mutation site is located, and performs comprehensive scoring and ranking for the subsequent new antigens screened. The calculation formula for the comprehensive score is as follows:

[0075] Total Score=Binding Score×Expression Score

[0076] After comprehensively scoring all candidate neoantigen mutant peptides, they were sorted from high to low according to the scores to obtain the corresponding tumor neoantigen epitope sites. Further, each mutant amino acid site was taken as the center, and 12 amino acids were extended to the left and right. Each mutation site corresponded to a 25-amino acid neoantigen epitope sequence, thus obtaining the final neoantigen epitope amino acid sequence.

[0077] Example 1

[0078] In this embodiment, the mouse melanoma B16F10 cell line was selected for the bioinformatics process and scoring of personalized new antigen epitope screening, and the new antigen epitopes obtained after screening and scoring were designed in series to construct the corresponding personalized new antigen epitope mRNA vaccine for in vivo efficacy verification in mice. The specific steps are as follows: the mouse melanoma B16F10 cell line was selected as the tumor tissue, and the tail tissue of 6-8 week C57BL / 6 mice was selected as the normal tissue, and the DNA, RNA of the tumor tissue and the normal tissue DNA were extracted for WES and RNAseq, and the WES data and RNAseq data obtained off the machine were scored and sorted according to the above-mentioned new antigen epitope screening process and scoring mechanism of the present invention.

[0079] Eleven high-group and 11 low-group neoantigen epitopes were screened out, and their sequence data are shown in the following table: Amino acid sequence of neoantigen epitope in high-group (B16F10-High) (peptide sequences are numbered as SEQ ID NO.1-11)

[0080] Gene Name Peptide Seq Score Rpl12 QIEVVPSASALINKALKEPPRDRKK 255.9409217 Wipi2 VTEMFNQGRAFAAVRLPFCGHKNIC 152.7617302 Nacc1 STPDLALNRMPQALSMATATAAVAV 121.1792717 Lrp10 DPFPNLNPAPAPPLACNLTLEDFYG 111.9240971 Hmgcr KNLVGSAMAGSISGYNAHAANIVTA 102.2812903 Shmt1 VNVQPYSGSPANVAVYTALVEPHGR 84.63436223 Ehbp1l1 SGTYRVGNAQPSLADCLDAGDLAQR 77.12661888 Gpatch4 AFLAQLKGSKALATSQPLTDSEPSQ 66.22396273 Abhd8 VLRVKHAGPAPILTPPPPPPEDDPG 61.64927511 Atp6v1h QDPFTVHMAARIIATLAAWGKELME 37.56247549 Ermp1 DSIKLTFEATGPRHMSFYVRTHKGS 36.62713577

[0081] Amino acid sequences of new antigen epitopes in the low group (B16F10-Low) (peptide sequences are numbered as SEQ ID NO. 12-22)

[0082] Gene Name Peptide Seq Total Score Gpaa1 QIEVVPSASALINKALKEPPRDRKK 15.37557916 Atf7 VTEMFNQGRAFAAVRLPFCGHKNIC 14.69933631 Notch2 STPDLALNRMPQALSMATATAAVAV 13.73564008 Dlg4 DPFPNLNPAPAPPLACNLTLEDFYG 8.798662602 Parp14 KNLVGSAMAGSISGYNAHAANIVTA 8.640087891 Ints11 VNVQPYSGSPANVAVYTALVEPHGR 7.910523504 Dmxl1 SGTYRVGNAQPSLADCLDAGDLAQR 7.130355465 Plec AFLAQLKGSKALATSQPLTDSEPSQ 5.207157801 Zfx VLRVKHAGPAPILTPPPPPPEDDPG 4.399730557 Rint1 QDPFTVHMAARIIATLAAWGKELME 3.970377598 Gga1 DSIKLTFEATGPRHMSFYVRTHKGS 2.89749764

[0083] Based on the successful screening of the above new antigen epitopes, the present invention sorts the screened tumor new antigen epitopes according to the scores, adds mouse exocrine signal peptide SP and MITD domain at the head and tail, respectively, and connects them in series with a universal Linker sequence to construct the high-group (B16F10-High) and low-group (B16F10-Low) new antigen epitope mRNA vaccines of the B16F10 cell line. The schematic diagram of the vaccine design method is shown in the figure: Figure 4 shown.

[0084] This patent design sorts the neoantigen epitopes according to their scores, placing the neoantigen epitopes with higher scores at the front end of the sequence and the neoantigen epitopes with lower scores at the end of the sequence, so as to ensure that the designed mRNA sequence can give priority to the expression of the neoantigen epitope peptides with higher scores as much as possible when it is translated and expressed by the ribosome in the cell; even if the ribosome translation is blocked and the mRNA is sheared, the neoantigen epitope peptides with higher scores can be expressed preferentially, showing stronger MHC molecule binding and presentation capabilities, and inducing a stronger immune response.

[0085] The mouse exocrine signal peptide SP amino acid sequence (MGWSCIILFLVATATGVHS) (SEQ ID NO. 23) and the MITD domain amino acid sequence (IVGIVAGLAVLAVVVIGAVVATVMCRRKSSGGKGGSYSQAASSDSAQGSDVSLTA) (SEQ ID NO. 24) were added to the head and tail, respectively, and connected in series with a universal linker sequence (GGSGGGGSGG) (SEQ ID NO. 25) to obtain

[0086] The amino acid sequence of the tandem mRNA coding region of the new antigen epitope in the high group (B16F10-High) is shown in SEQ ID NO. 26. The amino acid sequence of the tandem mRNA coding region of the new antigen epitope in the low group (B16F10-Low) is shown in SEQ ID NO. 27.

[0087] The amino acid sequence of the designed new antigen epitope tandem mRNA coding region was codon-optimized as the CDS region of the mRNA vaccine sequence, and the key elements of mRNA were added for sequence synthesis, including the T7 promoter sequence, the universal 5'UTR sequence, the universal 3'UTR sequence, and the 110bp Poly (A) sequence and two stop codon sequences (TGATGA). Among them, the nucleic acid sequences of the key elements are:

[0088] T7 Promoter Sequence:

[0089] TAATACGACTCACTATA(SEQ ID NO.28);

[0090] Universal 5'UTR sequence:

[0091] GAATAAACTAGTATTCTTCTGGTCCCCACAGACTCAGAGAGAACCCGCCACC (SEQ ID NO. 29);

[0092] Universal 3'UTR sequence:

[0093] CTCGAGCTGGTACTGCATGCACGCAATGCTAGCTGCCCCTTTCCCGTCCTGGGTACCCCGAGTCTCCCCGACCTCGGGTCCCAGGTATGCTCCCACCTCCACCTGCCCCACTCACCACCTCTGCTAGTTCCAGACACCTCCCAAGCACG CAGCAATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGATTAACCTTTAGCAATAAACGAAAGTTTAACTAAGCTATACTAACCCCAGGGTTGGTCAATTTCGTGCCAGCCACACCCTGGAGCTAGCAA(SEQ ID NO.30);

[0094] Poly(A) sequence:

[0095] AAAAAAAAAAAAAAAAAAAAAAAAAAAGCATATGACTAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA (SEQ ID NO. 31);

[0096] Finally, we spliced ​​and assembled the sequences and designed the high-group (B16F10-High) new antigen epitope tandem mRNA vaccine sequence as shown in SEQ ID NO. 32. As a control, the low-group (B16F10-Low) new antigen epitope tandem mRNA vaccine sequence is shown in SEQ ID NO. 33.

[0097] Furthermore, based on the smooth synthesis of the above-mentioned designed sequence, this patent constructs the synthetic sequence into the PMD18T-EGFP vector by homologous recombination or enzyme-linked method for amplification and preservation of the template plasmid; the amplified plasmid is linearized by single enzyme digestion, and then transcribed and purified in vitro to obtain the required mRNA as an mRNA vaccine targeting the new antigen epitope of the melanoma B16F10 cell line.

[0098] The four lipid components were dissolved in ethanol, and the liposome nanoparticles were prepared according to the prior art formula, using cationic lipid (SM-102): auxiliary lipid (DSPC): cholesterol: PEG lipid (DMG-PEG 2000 ) in a molar ratio of 50:10:38.5:1.5 to obtain an organic phase; the prepared mRNA vaccine is diluted with a citric acid buffer at a pH of 4 to obtain an aqueous phase, wherein the concentration of mRNA is a nitrogen-phosphorus ratio of lipid to mRNA of 6; the organic phase and the aqueous phase are added to a lipid nanoparticle microfluidic preparation system, and are fully mixed through a mixing chip at a flow rate ratio of 1:3 to form liposome nanoparticles containing mRNA; the prepared liposome nanoparticles are further diluted, replaced by tangential flow buffering and concentrated, and then the encapsulation efficiency is calculated and stored as an mRNA vaccine for subsequent application research.

[0099] In this example, the new antigen epitope mRNA vaccine of high-grade group (B16F10-High) and low-grade group (B16F10-Low) was used to conduct an in vivo animal efficacy verification experiment. 24 normal and healthy 6-8-week-old C57BL / 6 mice were selected and subcutaneously inoculated with 1x10 5 B16F10 cells were used to construct a B16F10 mouse melanoma subcutaneous model; after tumor bearing, all tumor-bearing mice were randomly divided into three groups: Blank group, B16F10-High group and B16F10-Low group, with 8 mice in each group.

[0100] The animal experiment administration scheme of this example is as follows Figure 5 As shown in the figure, the first administration started on the second day of tumor bearing, and the administration was continued at intervals of twice a week; the Blank group was subcutaneously injected with PBS, and the other groups were subcutaneously injected with 10ug of mRNA vaccine encapsulated with mRNA corresponding to the new antigen epitope. After the tumor grew to a measurable size, the size of the mouse tumor was measured regularly with a vernier caliper. The longest tumor diameter was set as A, and the minimum tumor diameter was set as B. (A*B 2 ) / 2 was used to characterize the tumor volume, draw the mouse tumor growth curve and calculate the mouse survival rate.

[0101] The results of the animal experiments in this example are as follows Figure 6As shown. From the comparison of the tumor growth curves of mice, it can be seen that compared with the Blank group injected with PBS, the tumor growth of the tumor-bearing mice injected with the B16F10-High group mRNA vaccine was significantly inhibited, indicating that the designed and prepared mRNA vaccine targeting the high-group neoantigen epitopes of the melanoma B16F10 cell line can indeed inhibit the proliferation rate of the melanoma B16F10 cell line and show a tumor growth inhibition effect; this shows that the high-group neoantigen epitopes screened and scored have specific immune activation capabilities and are effective tumor personalized neoantigens; compared with the Blank group injected with PBS, the tumor growth of the tumor-bearing mice injected with the B16F10-Low group mRNA vaccine was only weakly inhibited, indicating that the efficacy of the low-group neoantigen epitope mRNA vaccine is poor, which also shows that the low-group neoantigen epitopes screened and scored are inefficient candidate neoantigen epitopes, but still show a certain tumor growth inhibition effect.

[0102] The mouse survival rate results of this example are consistent with the mouse tumor growth curve change results. Figure 7 As shown in the figure, compared with the Blank group, the survival time of tumor-bearing mice injected with the B16F10-High group mRNA vaccine was significantly prolonged, and at the end of the experiment (60 days), several tumor-bearing mice injected with the B16F10-High group mRNA vaccine still survived. Compared with the Blank group, the survival time of tumor-bearing mice injected with the B16F10-Low group mRNA vaccine was also prolonged to a certain extent, but the extension of survival time was significantly lower than that of the high group.

[0103] Embodiment 2:

[0104] In this embodiment, the bioinformatics process and scoring of personalized new antigen epitope screening were selected for the cold tumor mouse breast cancer 4T1 cell line which is difficult to treat, and the efficacy of mRNA vaccine in mouse tumor model was verified for the new antigen after screening and scoring. The specific steps are as follows: the mouse breast cancer 4T1 cell line was selected as the tumor tissue, and the tail tissue of 6-8 week female BALB / c mice was selected as the normal tissue. The DNA, RNA and normal tissue DNA of the tumor tissue were extracted for WES and RNAseq respectively, and the WES data and RNAseq data obtained from the machine were scored and sorted according to the above-mentioned new antigen epitope screening process and scoring mechanism of the present invention.

[0105] In this example, 17 high-level group new antigen epitopes and 16 low-level group new antigen epitopes were screened for the mouse breast cancer 4T1 cell line, and their sequence data are shown in the following table:

[0106] Amino acid sequences of the high-group (4T1-High) new antigen epitopes (numbered in sequence as SEQ ID NO. 34-44)

[0107] Gene Name Peptide Seq Total Score Kank3 LQLVREQMAAALRHLRELEDQARAL 173.4830624 Pgd SAVDNCQDSWRRMISTGVQAGIPMP 86.8262694 Aspm EPGVSKALSYRAEVTPATVVVARKR 85.7641535 Macroh2a1 AVLELRKKNGTLEVAGAAISAGHGL 80.94495777 Atp10a RRPRRRRWEGRTRMVRSNLLPPLGT 46.6996635 Sf3b3 LYNLTLQRATGISFAIQGNFSGTKQ 43.37018013 Pja1 RIASQRRTTAEVAMHRSTANQSKRS 34.46383716 Rps6 APKIQRLVTPRVLQHKCRRIALKKQ 32.56857421 Mrpl22 WYLAKMIRGMSIDQALVQLEFNDKK 29.49732891 Polr2a QSLGEPATQITLNTFHYAGVSAKNV 23.42668619 Zfr AHIRGAKHQKVVTLHTKLGKPIPST 16.37748759 Igf2bp2 WAIRAIETLSGKVELHEKIMEVDYS 15.93491313 Acap2 KDPLSIAVEAANTDIVTLLRLARMN 15.20024566 Zzz3 ELLQFKKLKKQNLQQMQAESGFVQH 13.83621085 Gprc5a ICFSCLLAHALNLIKLVRGRKPLSW 12.54762508 Taf11 NVVIAMSGISKVFVWEVVEEALDVC 10.49001181 Isoc1 EAALAEIPGVRSVLLFGVETHVCIQ 9.327263175

[0108] Amino acid sequences of the low-level group (4T1-Low) new antigen epitopes (numbered in sequence as SEQ ID NO. 45-55)

[0109] Gene Name Peptide Seq Total Score Dpp9 HGSRKSSGLIVSKAPHNFQFVQKPD 6.932228354 Usp43 PLSGLNMAPHVAWRSTNSKAGPGPW 6.700274469 Fbxo38 QSNEDYPRSPLTRARSRLSHVPLIS 6.506284785 Cand1 DVFHAYLSLLTQTCPVQSWLCDPDA 3.448172616 Mrpl19 PRIQEIQVVKLEKRLEDNLLYLRDA 1.775329522 Dot1l PKRRALPTVAGLFTQSLGSPLNLNS 1.322736306 Zbtb12 HLGELAQSSVAPNTATPPQGVVKAC 1.036595602 Secisbp2l RPNQLGQAFPLSSHQSRNGNRGPVV 0.927487157 Agl HIQLHESKIVRQAGVGTKGPNEYIQ 0.608282099 Lrpprc DVASAKALYEYLTAKNMKLDDLFLK 0.36630377 Eps8 RVAAWSAWAADQGDFEEPRQYHEQE 0.243596326 Rock2 SLLDGLNSLVLVLNFPALRKNKNID 0.179792417 Rock2 ESLLDGLNSLVLVLNFPALRKNKNI 0.169509916 Myh14 YKQLPIYTEAIVEMYQGKKRHEVPP 0.166455429 Ccpg1 VNKASPVRMNDFKQLIDWYLLNELE 0 Serpinb6a LSTVEKEVTYEKCIEWTRLDKMDEE 0

[0110] Based on the successful screening of the above new antigen epitopes, the present invention selects the same design method as in Example 1, sorts the screened tumor new antigen epitopes by scoring, and constructs the high-group (4T1-High) and low-group (4T1-Low) new antigen epitope mRNA vaccines of the 4T1 cell line. The amino acid sequence of the high-group (4T1-High) tandem peptide is shown in SEQ ID NO.56, and the nucleotide sequence of the mRNA is shown in SEQ ID NO.57. As a control, the amino acid sequence of the low-group (4T1-Low) tandem peptide is shown in SEQ ID NO.58, and the nucleotide sequence of the mRNA is shown in SEQ ID NO.59.

[0111] In this example, the new antigen epitope mRNA vaccine of high-group (4T1-High) and low-group (4T1-Low) was used to conduct an in vivo animal efficacy verification experiment. 24 normal and healthy female BALB / c mice aged 6-8 weeks were selected and inoculated subcutaneously with 1×10 5 4T1 cells were used to construct a 4T1 mouse breast cancer subcutaneous model; after tumor bearing, all tumor-bearing mice were randomly divided into three groups: Blank group, 4T1-High group and 4T1-Low group, with 8 mice in each group. Figure 8 As shown, a similar administration method and time interval as in Example 1 were selected to administer the drug to the corresponding groups of mice, and the tumor growth curve of the mice was detected and the survival rate of the mice was counted.

[0112] The results of the animal experiments in this example are as follows Figure 9As shown. From the comparison of the tumor growth curves of mice, it can be seen that compared with the Blank group injected with PBS, the tumor growth of tumor-bearing mice injected with 4T1-High and 4T1-Low mRNA vaccines was significantly inhibited, indicating that the designed and prepared mRNA vaccines targeting the high-group and low-group neoantigen epitopes of the 4T1 cell line can inhibit the proliferation rate of the 4T1 breast cancer cell line and show a tumor growth inhibition effect. This shows that the two groups of neoantigen epitopes screened and scored have specific immune activation capabilities and are effective tumor personalized neoantigens. It also proves that the personalized tumor neoantigen epitope bioinformatics screening process involved in the present invention has good sensitivity and can screen out a large number of effective tumor neoantigen epitopes. At the same time, the tumor growth inhibition effect of tumor-bearing mice injected with the 4T1-High group mRNA vaccine is significantly better than that of the 4T1-Low group, indicating that the neoantigen epitopes in the high-group can activate a stronger immune effect, which further proves the accuracy of the bioinformatics process and scoring mechanism.

[0113] The mouse survival rate results of this example are as follows Figure 10 Compared with the Blank group injected with PBS, the survival time of tumor-bearing mice injected with the 4T1-High group mRNA vaccine was significantly prolonged. The injection of the 4T1-Low group mRNA vaccine also had a certain effect on prolonging the survival time of tumor-bearing mice, but the extension time was significantly lower than that of the 4T1-High group.

[0114] The experimental results of this example also prove that although there are certain differences in the effects in different tumor models, the personalized tumor neoantigen epitope bioinformatics screening process and scoring mechanism involved in the present invention are still effective.

Claims

1. An isolated DNA molecule, characterized in that It comprises a coding region, and the coding region comprises a nucleotide sequence for encoding an amino acid sequence such as any one or several tandem peptides in SEQ ID NO.34-44.

2. The isolated DNA molecule according to claim 1, characterized in that The coding region also has a 5' untranslated region and a 3' untranslated region upstream and downstream, respectively.

3. The isolated DNA molecule according to claim 1, characterized in that In the DNA molecule, the coding region contains a sequence for encoding a peptide obtained by sequentially connecting amino acid sequences such as SEQ ID NO. 34-44.

4. The isolated DNA molecule according to claim 1, characterized in that When the amino acid sequences are connected in series, they are connected through a Linker sequence; the amino acid sequence also includes a MITD domain and a signal peptide.

5. The isolated DNA molecule according to claim 1, characterized in that It contains the sequence shown in SEQ ID NO.

57.

6. A protein having a sequence of any one or several tandem peptides as in SEQ ID NOs. 34-44.

7. The protein according to claim 6, characterized in that It has the amino acid sequence shown in SEQ ID NO.

56.

8. An antigen expression vector comprising the isolated DNA molecule according to any one of claims 1 to 5.

9. An mRNA vaccine comprising the antigen expression vector according to claim 8.

10. Use of the isolated DNA molecule according to claim 1, the protein according to claim 6 or 7, the antigen expression vector according to claim 8 or the mRNA vaccine according to claim 9 in the preparation of a breast cancer vaccine.

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