Julia Kodysh, Tim O’Donnell, A. Blázquez, J. Finnigan, N. Bhardwaj, A. Rubinsteyn
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Secondly, we trained a model of overall T-cell immunogenicity whose primary input is the predicted pMHC presentation score of any peptide-MHC combination, alongside other features such as similarity to the self proteome. This model is trained on T-cell response data deposited in the Immune Epitope Database (3). Lastly, we assembled a small dataset of peptide sequences used in neoantigen vaccine trials (1,4,5), which are labeled by whether they achieved a CD8+ or CD4+ T-cell response. This dataset allows us to explore several hypotheses about the relationship between immunogenic response and sequence similarity to both the self proteome and pathogenic proteomes. References: 1. Rubinsteyn A, Kodysh J, …, Hammerbacher J. Computational pipeline for the PGV-001 Neoantigen Vaccine Trial. Frontiers in Immunology 2018. 2. Abelin JG, Keskin DB,..., Wu CJ. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 2017. 3. Vita R, Overton JA, …, Peters B. The immune epitope database (IEDB) 3.0. Nucleic Acids Res 2014. [4. Sahin U, Derhovanessian E, …, Tureci O. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 2017. 5. Ott P, Hu Z, …, Wu CJ. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 2017. Citation Format: Julia Kodysh, Tim O9Donnell, Ana B. Blazquez, John Finnigan, Nina Bhardwaj, Alex Rubinsteyn. Improved neoantigen vaccine selection by combining prediction of pMHC presentation and T-cell epitopes [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. 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This model is trained on T-cell response data deposited in the Immune Epitope Database (3). Lastly, we assembled a small dataset of peptide sequences used in neoantigen vaccine trials (1,4,5), which are labeled by whether they achieved a CD8+ or CD4+ T-cell response. This dataset allows us to explore several hypotheses about the relationship between immunogenic response and sequence similarity to both the self proteome and pathogenic proteomes. References: 1. Rubinsteyn A, Kodysh J, …, Hammerbacher J. Computational pipeline for the PGV-001 Neoantigen Vaccine Trial. Frontiers in Immunology 2018. 2. Abelin JG, Keskin DB,..., Wu CJ. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 2017. 3. Vita R, Overton JA, …, Peters B. The immune epitope database (IEDB) 3.0. Nucleic Acids Res 2014. [4. Sahin U, Derhovanessian E, …, Tureci O. 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引用次数: 0
摘要
OpenVax小组在西奈山帮助启动了两项新抗原疫苗临床试验(NCT02721043, NCT02721043),该试验基于一个简单的乘法排序标准,该标准赋予表达同等权重,并预测突变肽的I类MHC结合亲和力(1)。该海报旨在更好地为我们的排序方法奠定基础,以便在多个免疫学数据来源中选择新抗原疫苗的内容。通过将RNA表达和MHC亲和力与质谱鉴定的pMHC配体联系起来,我们建立了一个更好的MHC- i在细胞表面呈递的模型(2)。其次,我们训练了一个整体t细胞免疫原性模型,其主要输入是任何肽-MHC组合的预测pMHC呈递评分,以及其他特征,如与自身蛋白质组的相似性。该模型是根据储存在免疫表位数据库(Immune Epitope Database)中的t细胞应答数据进行训练的(3)。最后,我们收集了一个用于新抗原疫苗试验(1,4,5)的肽序列的小数据集,这些序列通过它们是否达到CD8+或CD4+ t细胞应答来标记。该数据集允许我们探索免疫原性反应与自身蛋白质组和致病性蛋白质组序列相似性之间关系的几个假设。引用:1。李建军,李建军,李建军,等。ppv001新抗原疫苗的临床研究进展。免疫学前沿2018。2. 艾柏林JG,凯斯金DB,…吴俊杰。质谱分析hla相关肽在单等位细胞使更准确的表位预测。2017年的免疫力。3.李建军,李建军,李建军,等。免疫表位数据库(IEDB) 3.0。核酸学报,2014。(4。李建军,张建军,张建军,等。RNA突变体疫苗可激活肿瘤多特异性免疫。2017年自然。5. 赵鹏,胡忠,吴俊杰。黑色素瘤患者的免疫原性个人新抗原疫苗。2017年自然。引文格式:Julia kodhh, Tim O9Donnell, Ana B. Blazquez, John Finnigan, Nina Bhardwaj, Alex rubinstein。结合pMHC呈递和t细胞表位预测改进新抗原疫苗选择[摘要]。第四届CRI-CIMT-EATI-AACR国际癌症免疫治疗会议:将科学转化为生存;2018年9月30日至10月3日;纽约,纽约。费城(PA): AACR;癌症免疫学杂志,2019;7(2增刊):摘要nr B080。
Abstract B080: Improved neoantigen vaccine selection by combining prediction of pMHC presentation and T-cell epitopes
The OpenVax group has helped initiate two neoantigen vaccine clinical trials (NCT02721043, NCT02721043) at Mount Sinai based on a simple multiplicative ranking criterion which assigns equal weight to expression and predicted Class I MHC binding affinity of mutated peptides (1). This poster seeks to better ground our ranking method for selecting the contents of neoantigen vaccines in several sources of immunological data. We built a better model of MHC-I presentation on the cell surface by relating RNA expression and MHC affinity to pMHC ligands identified with mass spectrometry (2). Secondly, we trained a model of overall T-cell immunogenicity whose primary input is the predicted pMHC presentation score of any peptide-MHC combination, alongside other features such as similarity to the self proteome. This model is trained on T-cell response data deposited in the Immune Epitope Database (3). Lastly, we assembled a small dataset of peptide sequences used in neoantigen vaccine trials (1,4,5), which are labeled by whether they achieved a CD8+ or CD4+ T-cell response. This dataset allows us to explore several hypotheses about the relationship between immunogenic response and sequence similarity to both the self proteome and pathogenic proteomes. References: 1. Rubinsteyn A, Kodysh J, …, Hammerbacher J. Computational pipeline for the PGV-001 Neoantigen Vaccine Trial. Frontiers in Immunology 2018. 2. Abelin JG, Keskin DB,..., Wu CJ. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 2017. 3. Vita R, Overton JA, …, Peters B. The immune epitope database (IEDB) 3.0. Nucleic Acids Res 2014. [4. Sahin U, Derhovanessian E, …, Tureci O. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 2017. 5. Ott P, Hu Z, …, Wu CJ. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 2017. Citation Format: Julia Kodysh, Tim O9Donnell, Ana B. Blazquez, John Finnigan, Nina Bhardwaj, Alex Rubinsteyn. Improved neoantigen vaccine selection by combining prediction of pMHC presentation and T-cell epitopes [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B080.