基于蛋白质基因组学方法的新抗原鉴定流程
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摘要
肿瘤新抗原是免疫治疗的重要靶点,但基因组数据产生的候选新抗原数量庞大,预测假阳性肽段过多,实验验证费时费力,影响肿瘤新抗原的临床应用.本研究以乳腺癌为例,使用比转录组水平筛选更严格、比细胞学实验更省时的蛋白质基因组学方法来预测和筛选新抗原.研究发现,C2 (IFN-γdominant)免疫表型的新抗原数量最多. C2免疫表型显示出最高的M1/M2巨噬细胞极化,较强的CD8信号和最大的T细胞受体多样性,这可能导致产生更多具有良好免疫原性的新抗原.另外,我们还观察到乳腺癌肿瘤突变负荷与新抗原数目之间呈正相关.通过同批样本的质谱数据进一步筛选发现,可将两万级别的预测新抗原肽候选降至几十条表达肽段,进而可分析其对应的特异或广谱突变基因.最后,我们进一步分析了新抗原的免疫原性,即被T细胞受体识别的可能性.本文利用蛋白质组学数据对基因组数据计算预测得到的候选新抗原进一步筛选,提高了新抗原预测准确性,大大缩小后续实验验证范围.该流程可为肿瘤新抗原预测与筛选研究提供参考.
Tumor neoantigens are important targets for immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, numerous false positive neoantigens will be produced, experimental verification would require immense time and experience.Specific identification of immunogenic candidate neoantigens is consequently a major challenge. Here we introduce a workflow to predict and filter neoantigens of breast cancer with proteogenomic methodology, which can be beneficial to high quality identification of neoantigens. We found that C2(IFN-γdominant)immunophenotype possessed the most number of neoantigens. C2 immunophenotype shows the highest M1/M2 macrophage polarization, a strong CD8 signal and the greatest T cell receptor(TCR) diversity, which may lead to more neoantigen number than in other immunophenotypes of breast cancer. In addition, we also observed that there is a positive linear relationship between neoantigen number and tumor mutation burden. By further screening for predicted tumor mutant peptides using mass spectral data of breast cancer, we found that more than20 000 predicted neoantigens were reduced to dozens of mutant peptides in protein expression level, the corresponding mutant genes could be further analyzed. Finally, in order to define which neoantigens were more likely to be immunogenic, TCR recognition probability was calculated using blastp method. In this study,proteomics data was used to further screen the predicted neoantigens, which improved the prediction accuracy of neoantigens, and could greatly reduce the validation scope of potential subsequent experiments. This workflow provides a new insight for tumor neoantigen prediction and screening.
Tumor neoantigens are important targets for immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, numerous false positive neoantigens will be produced, experimental verification would require immense time and experience.Specific identification of immunogenic candidate neoantigens is consequently a major challenge. Here we introduce a workflow to predict and filter neoantigens of breast cancer with proteogenomic methodology, which can be beneficial to high quality identification of neoantigens. We found that C2(IFN-γdominant)immunophenotype possessed the most number of neoantigens. C2 immunophenotype shows the highest M1/M2 macrophage polarization, a strong CD8 signal and the greatest T cell receptor(TCR) diversity, which may lead to more neoantigen number than in other immunophenotypes of breast cancer. In addition, we also observed that there is a positive linear relationship between neoantigen number and tumor mutation burden. By further screening for predicted tumor mutant peptides using mass spectral data of breast cancer, we found that more than20 000 predicted neoantigens were reduced to dozens of mutant peptides in protein expression level, the corresponding mutant genes could be further analyzed. Finally, in order to define which neoantigens were more likely to be immunogenic, TCR recognition probability was calculated using blastp method. In this study,proteomics data was used to further screen the predicted neoantigens, which improved the prediction accuracy of neoantigens, and could greatly reduce the validation scope of potential subsequent experiments. This workflow provides a new insight for tumor neoantigen prediction and screening.
引文
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[10]Liu G,Li D,Li Z,et al.PSSMHCpan:a novel PSSM-based software for predicting class I peptide-HLA binding affinity.Gigascience,2017,6(5):1-11
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[13]Reche P A,Reinherz E L.Prediction of peptide-MHC binding using profiles.Methods Mol Biol,2007,409:185-200
[14]Nielsen M,Andreatta M.NetMHCpan-3.0;improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.Genome Med,2016,8(1):33
[15]Bassani-Sternberg M,Braunlein E,Klar R,et al.Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry.Nat Commun,2016,7:13404
[16]Kalaora S,Barnea E,Merhavi-Shoham E,et al.Use of HLApeptidomics and whole exome sequencing to identify human immunogenic neo-antigens.Oncotarget,2016,7(5):5110-5117
[17]Yadav M,Jhunjhunwala S,Phung Q T,et al.Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing.Nature,2014,515(7528):572-576
[18]Tomczak K,Czerwinska P,Wiznerowicz M.The cancer genome atlas(TCGA):an immeasurable source of knowledge.Contemp Oncol(Pozn),2015,19(1A):A68-77
[19]Uniprot C.UniProt:a hub for protein information.Nucleic Acids Res,2015,43(Database issue):D204-212.
[20]Mertins P,Mani D R,Ruggles K V,et al.Proteogenomics connects somatic mutations to signalling in breast cancer.Nature,2016,534(7605):55-62
[21]Gourraud PA,Khankhanian P,Cereb N,et al.HLA diversity in the1000 genomes dataset.Plos One,2014,9(7):e97282
[22]Thorsson V,Gibbs D L,Brown S D,et al.The immune landscape of cancer.Immunity,2018,48(4):812-830
[23]Chalmers Z R,Connelly C F,Fabrizio D,et al.Analysis of 100,000human cancer genomes reveals the landscape of tumor mutational burden.Genome Med,2017,9(1):34
[24]Cox J,Mann M.MaxQuant enables high peptide identification rates,individualized p.p.b.-range mass accuracies and proteomewide protein quantification.Nat Biotechnol,2008,26(12):1367-1372
[25]Connolly R M,Fackler M J,Zhang Z,et al.Tumor and serum DNAmethylation in women receiving preoperative chemotherapy with or without vorinostat in TBCRC008.Breast Cancer Res Treat,2018,167(1):107-116
[26]Milke L,Schulz K,Weigert A,et al.Depletion of tristetraprolin in breast cancer cells increases interleukin-16 expression and promotes tumor infiltration with monocytes/macrophages.Carcinogenesis,2013,34(4):850-857
[27]Zhang C,Chen X,Li Y,et al.si-RNA-mediated silencing of ADRBK1 gene attenuates breast cancer cell proliferation.Cancer Biother Radiopharm,2014,29(8):303-309
[28]De Gregoriis G,Ramos J A,Fernandes P V,et al.DNA repair genes PAXIP1 and TP53BP1 expression is associated with breast cancer prognosis.Cancer Biol Ther,2017,18(6):439-449
[29]Liu D,Shi M,Duan C,et al.Downregulation of Erbin in Her2-overexpressing breast cancer cells promotes cell migration and induces trastuzumab resistance.Mol Immunol,2013,56(1-2):104-112
[30]Tran E,Turcotte S,Gros A,et al.Cancer immunotherapy based on mutation-specific CD4+T cells in a patient with epithelial cancer.Science,2014,344(6184):641-645
[31]Charoentong P,Finotello F,Angelova M,et al.Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade.Cell Rep,2017,18(1):248-262
[32]Schumacher T N,Schreiber R D.Neoantigens in cancer immunotherapy.Science,2015,348(6230):69-74
[2]Van Rooij N,Van Buuren M M,Philips D,et al.Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma.J Clin Oncol,2013,31(32):e439-442
[3]Gubin M M,Zhang X,Schuster H,et al.Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens.Nature,2014,515(7528):577-581
[4]Matsushita H,Vesely M D,Koboldt D C,et al.Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting.Nature,2012,482(7385):400-404
[5]Anagnostou V,Smith K N,Forde P M,et al.Evolution of neoantigen landscape during immune checkpoint blockade in nonsmall cell lung cancer.Cancer Discov,2017,7(3):264-276
[6]Khodadoust M S,Olsson N,Wagar L E,et al.Antigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens.Nature,2017,543(7647):723-727
[7]Ott P A,Hu Z,Keskin D B,et al.An immunogenic personal neoantigen vaccine for patients with melanoma.Nature,2017,547(7662):217-221
[8]Sahin U,Derhovanessian E,Miller M,et al.Personalized RNAmutanome vaccines mobilize poly-specific therapeutic immunity against cancer.Nature,2017,547(7662):222-226
[9]Keskin D B,Anandappa A J,Sun J,et al.Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial.Nature,2019,565(7738):234-239
[10]Liu G,Li D,Li Z,et al.PSSMHCpan:a novel PSSM-based software for predicting class I peptide-HLA binding affinity.Gigascience,2017,6(5):1-11
[11]Vita R,Overton J A,Greenbaum J A,et al.The immune epitope database(IEDB)3.0.Nucleic Acids Res,2015,43(Database issue):D405-412
[12]Rammensee H,Bachmann J,Emmerich N P,et al.SYFPEITHI:database for MHC ligands and peptide motifs.Immunogenetics,1999,50(3-4):213-219
[13]Reche P A,Reinherz E L.Prediction of peptide-MHC binding using profiles.Methods Mol Biol,2007,409:185-200
[14]Nielsen M,Andreatta M.NetMHCpan-3.0;improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.Genome Med,2016,8(1):33
[15]Bassani-Sternberg M,Braunlein E,Klar R,et al.Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry.Nat Commun,2016,7:13404
[16]Kalaora S,Barnea E,Merhavi-Shoham E,et al.Use of HLApeptidomics and whole exome sequencing to identify human immunogenic neo-antigens.Oncotarget,2016,7(5):5110-5117
[17]Yadav M,Jhunjhunwala S,Phung Q T,et al.Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing.Nature,2014,515(7528):572-576
[18]Tomczak K,Czerwinska P,Wiznerowicz M.The cancer genome atlas(TCGA):an immeasurable source of knowledge.Contemp Oncol(Pozn),2015,19(1A):A68-77
[19]Uniprot C.UniProt:a hub for protein information.Nucleic Acids Res,2015,43(Database issue):D204-212.
[20]Mertins P,Mani D R,Ruggles K V,et al.Proteogenomics connects somatic mutations to signalling in breast cancer.Nature,2016,534(7605):55-62
[21]Gourraud PA,Khankhanian P,Cereb N,et al.HLA diversity in the1000 genomes dataset.Plos One,2014,9(7):e97282
[22]Thorsson V,Gibbs D L,Brown S D,et al.The immune landscape of cancer.Immunity,2018,48(4):812-830
[23]Chalmers Z R,Connelly C F,Fabrizio D,et al.Analysis of 100,000human cancer genomes reveals the landscape of tumor mutational burden.Genome Med,2017,9(1):34
[24]Cox J,Mann M.MaxQuant enables high peptide identification rates,individualized p.p.b.-range mass accuracies and proteomewide protein quantification.Nat Biotechnol,2008,26(12):1367-1372
[25]Connolly R M,Fackler M J,Zhang Z,et al.Tumor and serum DNAmethylation in women receiving preoperative chemotherapy with or without vorinostat in TBCRC008.Breast Cancer Res Treat,2018,167(1):107-116
[26]Milke L,Schulz K,Weigert A,et al.Depletion of tristetraprolin in breast cancer cells increases interleukin-16 expression and promotes tumor infiltration with monocytes/macrophages.Carcinogenesis,2013,34(4):850-857
[27]Zhang C,Chen X,Li Y,et al.si-RNA-mediated silencing of ADRBK1 gene attenuates breast cancer cell proliferation.Cancer Biother Radiopharm,2014,29(8):303-309
[28]De Gregoriis G,Ramos J A,Fernandes P V,et al.DNA repair genes PAXIP1 and TP53BP1 expression is associated with breast cancer prognosis.Cancer Biol Ther,2017,18(6):439-449
[29]Liu D,Shi M,Duan C,et al.Downregulation of Erbin in Her2-overexpressing breast cancer cells promotes cell migration and induces trastuzumab resistance.Mol Immunol,2013,56(1-2):104-112
[30]Tran E,Turcotte S,Gros A,et al.Cancer immunotherapy based on mutation-specific CD4+T cells in a patient with epithelial cancer.Science,2014,344(6184):641-645
[31]Charoentong P,Finotello F,Angelova M,et al.Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade.Cell Rep,2017,18(1):248-262
[32]Schumacher T N,Schreiber R D.Neoantigens in cancer immunotherapy.Science,2015,348(6230):69-74
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