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本文引用的文献

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Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes.使用 DNA 条码标记的肽-MHC-I 多聚体大规模检测抗原特异性 T 细胞。
Nat Biotechnol. 2016 Oct;34(10):1037-1045. doi: 10.1038/nbt.3662. Epub 2016 Aug 29.
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The Ensembl Variant Effect Predictor.Ensembl变异效应预测器。
Genome Biol. 2016 Jun 6;17(1):122. doi: 10.1186/s13059-016-0974-4.
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FRED 2: an immunoinformatics framework for Python.FRED 2:一个用于Python的免疫信息学框架。
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NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.NetMHCpan-3.0;整合来自多个受体和肽长度数据集的信息,改进对与MHC I类分子结合的预测。
Genome Med. 2016 Mar 30;8(1):33. doi: 10.1186/s13073-016-0288-x.
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Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma.转移性黑色素瘤中抗PD-1治疗反应的基因组和转录组特征
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Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade.克隆性新抗原引发T细胞免疫反应性以及对免疫检查点阻断的敏感性。
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pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens.pVAC-Seq:一种基于基因组引导的计算机模拟方法来鉴定肿瘤新抗原。
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Mutanome directed cancer immunotherapy.基于肿瘤突变组的癌症免疫治疗。
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9
Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes.I类HLA基因中癌症相关体细胞突变的综合分析
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10
Tumor neoantigens: building a framework for personalized cancer immunotherapy.肿瘤新抗原:构建个性化癌症免疫治疗的框架
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MuPeXI:从肿瘤测序数据预测新抗原表位

MuPeXI: prediction of neo-epitopes from tumor sequencing data.

作者信息

Bjerregaard Anne-Mette, Nielsen Morten, Hadrup Sine Reker, Szallasi Zoltan, Eklund Aron Charles

机构信息

Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet 208, 2800, Lyngby, Denmark.

Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina.

出版信息

Cancer Immunol Immunother. 2017 Sep;66(9):1123-1130. doi: 10.1007/s00262-017-2001-3. Epub 2017 Apr 20.

DOI:10.1007/s00262-017-2001-3
PMID:28429069
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11028452/
Abstract

Personalization of immunotherapies such as cancer vaccines and adoptive T cell therapy depends on identification of patient-specific neo-epitopes that can be specifically targeted. MuPeXI, the mutant peptide extractor and informer, is a program to identify tumor-specific peptides and assess their potential to be neo-epitopes. The program input is a file with somatic mutation calls, a list of HLA types, and optionally a gene expression profile. The output is a table with all tumor-specific peptides derived from nucleotide substitutions, insertions, and deletions, along with comprehensive annotation, including HLA binding and similarity to normal peptides. The peptides are sorted according to a priority score which is intended to roughly predict immunogenicity. We applied MuPeXI to three tumors for which predicted MHC-binding peptides had been screened for T cell reactivity, and found that MuPeXI was able to prioritize immunogenic peptides with an area under the curve of 0.63. Compared to other available tools, MuPeXI provides more information and is easier to use. MuPeXI is available as stand-alone software and as a web server at http://www.cbs.dtu.dk/services/MuPeXI .

摘要

癌症疫苗和过继性T细胞疗法等免疫疗法的个性化取决于对可被特异性靶向的患者特异性新抗原表位的识别。MuPeXI(突变肽提取器和信息器)是一个用于识别肿瘤特异性肽并评估其成为新抗原表位潜力的程序。该程序的输入是一个包含体细胞突变调用、HLA类型列表以及可选的基因表达谱的文件。输出是一个表格,其中包含所有源自核苷酸替换、插入和缺失的肿瘤特异性肽,以及全面的注释,包括HLA结合和与正常肽的相似性。这些肽根据一个优先级分数进行排序,该分数旨在大致预测免疫原性。我们将MuPeXI应用于三种肿瘤,针对这些肿瘤已筛选了预测的MHC结合肽的T细胞反应性,发现MuPeXI能够以0.63的曲线下面积对免疫原性肽进行优先级排序。与其他可用工具相比,MuPeXI提供了更多信息且更易于使用。MuPeXI可作为独立软件使用,也可通过网络服务器访问,网址为http://www.cbs.dtu.dk/services/MuPeXI 。