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目前用于预测癌症特异性T细胞免疫的工具。

Current tools for predicting cancer-specific T cell immunity.

作者信息

Gfeller David, Bassani-Sternberg Michal, Schmidt Julien, Luescher Immanuel F

机构信息

Ludwig Center for Cancer Research, University of Lausanne, Epalinges, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.

Department of Oncology, University Hospital of Lausanne , Lausanne, Switzerland.

出版信息

Oncoimmunology. 2016 Apr 25;5(7):e1177691. doi: 10.1080/2162402X.2016.1177691. eCollection 2016 Jul.

Abstract

Tumor exome and RNA sequencing data provide a systematic and unbiased view on cancer-specific expression, over-expression, and mutations of genes, which can be mined for personalized cancer vaccines and other immunotherapies. Of key interest are tumor-specific mutations, because T cells recognizing neoepitopes have the potential to be highly tumoricidal. Here, we review recent developments and technical advances in identifying MHC class I and class II-restricted tumor antigens, especially neoantigen derived MHC ligands, including in silico predictions, immune-peptidome analysis by mass spectrometry, and MHC ligand validation by biochemical methods on T cells.

摘要

肿瘤外显子组和RNA测序数据为癌症特异性基因表达、过表达及突变提供了系统且无偏差的视角,可用于挖掘个性化癌症疫苗及其他免疫疗法。肿瘤特异性突变是关键关注点,因为识别新表位的T细胞具有高效杀瘤潜力。在此,我们综述了在鉴定MHC I类和II类限制性肿瘤抗原,尤其是新抗原衍生的MHC配体方面的最新进展和技术进步,包括计算机预测、通过质谱进行免疫肽组分析以及通过生化方法在T细胞上进行MHC配体验证。

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