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预先存在的异源T细胞免疫和新抗原免疫原性。

Pre-existing heterologous T-cell immunity and neoantigen immunogenicity.

作者信息

Leng Qibin, Tarbe Marion, Long Qi, Wang Feng

机构信息

Affiliated Cancer Hospital & Institute of Guangzhou Medical University State Key Laboratory of Respiratory Diseases, Guangzhou Medical University Guangzhou China.

The Joint Center for Infection and Immunity Guangzhou Women and Children's Medical Center Guangzhou Institute of Pediatrics Guangzhou Medical University Guangzhou China.

出版信息

Clin Transl Immunology. 2020 Mar 21;9(3):e01111. doi: 10.1002/cti2.1111. eCollection 2020.

DOI:10.1002/cti2.1111
PMID:32211191
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7085466/
Abstract

Neoantigens are tumor-specific mutated proteins that are exempt from central tolerance and are therefore capable of efficiently eliciting effective T-cell responses. The identification of immunogenic neoantigens in tumor-specific mutated proteins has promising clinical implications for cancer immunotherapy. However, the factors that may contribute to neoantigen immunogenicity are not yet fully understood. Through molecular mimicry of antigens arising during cancer progression, the gut microbiota and previously encountered pathogens potentially have profound impacts on T-cell responses to previously unencountered tumor neoantigens. Here, we review the characteristics of immunogenic neoantigens and how host exposure to microbes may affect T-cell responses to neoantigens. We address the hypothesis that pre-existing heterologous memory T-cell immunity is a major factor that influences neoantigen immunogenicity in individual cancer patients. Accumulating data suggest that differences in individual histories of microbial exposure should be taken into account during the optimisation of algorithms that predict neoantigen immunogenicity.

摘要

新抗原是肿瘤特异性突变蛋白,不受中枢耐受影响,因此能够有效引发有效的T细胞反应。在肿瘤特异性突变蛋白中鉴定免疫原性新抗原对癌症免疫治疗具有广阔的临床应用前景。然而,可能导致新抗原免疫原性的因素尚未完全明确。通过对癌症进展过程中出现的抗原进行分子模拟,肠道微生物群和先前接触过的病原体可能对T细胞对先前未接触过的肿瘤新抗原的反应产生深远影响。在此,我们综述了免疫原性新抗原的特征以及宿主接触微生物如何影响T细胞对新抗原的反应。我们探讨了这样一个假说,即预先存在的异源记忆T细胞免疫是影响个体癌症患者新抗原免疫原性的主要因素。越来越多的数据表明,在优化预测新抗原免疫原性的算法时,应考虑个体微生物接触史的差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197e/7085466/37c832fde6da/CTI2-9-e01111-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197e/7085466/37c832fde6da/CTI2-9-e01111-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197e/7085466/37c832fde6da/CTI2-9-e01111-g001.jpg

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本文引用的文献

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Predicting HLA class II antigen presentation through integrated deep learning.通过集成深度学习预测 HLA Ⅱ类抗原呈递
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Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction.
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