| Objective:Ovarian cancer neoantigen is a mutated protein expressed only in cancer tissues and recognized by the immune system.It can enhance anti-tumor T cell response and attack ovarian cancer cells in a precise and targeted manner without damaging healthy tissues.Based on exon sequencing and transcriptome sequencing,this study added the combination of Major Histocompatibility Complex(MHC)class I molecules and mutated epitopes based on mass spectrometry to predict neoantigens,in the hope of discovering effective antigenic targets for individualized treatment of ovarian cancer patients.It lays a good foundation for the study of individualized treatment of ovarian cancer.Methods:1.Complete exon sequencing was performed on peripheral blood and tumor tissue of a patient with advanced ovarian cancer to obtain Human Leucocyte Antigen(HLA)typing and sequencing data.2.Annovar was used to annotate the sequencing data into missense mutations.Combined with the expression level of transcriptome genes and patients’HLA typing results,PSSMHCpan software was used to predict the affinity between MHC-I molecules and antigenic peptides,and EPIP software was used to predict their presentation.16 candidate antigenic peptides of ovarian cancer were screened out.3.The candidate antigenic peptide for ovarian cancer was synthesized and purified from the amino acid sequence.The molecular weight and purity of the candidate antigen peptide were determined by Mass Spectrum(MS)and Reverse Phase High-performance Liquid Chromatography(RP-HPLC).4.To establish mature Peripheral Blood Mononuclear Cell(PBMC)culture system,and stimulate PBMC of patients and healthy people with HLA type with candidate antigen peptides.Enzyme linked immunospot assay(Elispot)was used to screen out immunogenic neoantigens of ovarian cancer from candidate antigen peptides in vitro.5.Elispot-positive neoantigens were co-cultured with peripheral blood PBMC at concentrations of 10μM,50μM and 100μM,respectively,and the mixed cell phenotype was detected by flow cytometry.Results:1.Complete exon sequencing and transcriptome sequencing results showed that the patient had 22 Single Nucleotide Variants(SNVs)and1 Insertion or Deletion(Indel).2.Annovar was used to annotate the sequencing data into missense mutations,combined with the expression level of transcriptome genes and patient HLA typing results,PSSMHCpan software was used to predict affinity,and Epitope Presentation Integrated Prediction Model(Epitope Presentation Integrated Prediction)was presented.EPIP)and16 candidate antigenic peptides of ovarian cancer were screened.3.The candidate antigen peptides were used to stimulate the peripheral blood PBMC of the patients in vitro to amplify neoantigen-responsive T cells.Elispot experiment verified that 10 of the 16 peptides had the potential to become neoantigens,with a positive rate of about 62.5%.4.The proportion of CD3~+CD8~+double positive cells in the 10μM,50μM and 100μM peptide stimulation groups was 14.37%,18.50%and 23.16%,respectively,compared with the negative control.CD8+T cell response was increased at 100μM.5.The 9 candidate antigen peptides restricted by HLA-A*24:02 were used to stimulate the peripheral blood PBMC of healthy volunteers with HLA-A*24:02 in vitro.The amplified Neoantigen reactive T cells were verified by Elispot experiment,and the Elispot results of the healthy volunteers were consistent with the results of the patients.Conclusion:1.High-throughput sequencing was performed for low-grade highly differentiated ovarian cancer patients.16 candidate antigenic peptides containing mutant amino acids were successfully predicted by using two bioinformatics software,PSSMHCpan and EPIP.2.Ten candidate antigenic peptides were successfully verified to have immunogenicity through in vitro experiments,and a highly effective screening and identification platform for individual neoantigens of ovarian cancer was preliminarily established.3.Peripheral blood of healthy people with the same HLA classification can be used as a possible alternative to verify their antigenic immunogenicity. |