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透明细胞肾细胞癌患者肿瘤浸润 T 细胞识别来自点突变和移码突变的新抗原。

Tumor-Infiltrating T Cells From Clear Cell Renal Cell Carcinoma Patients Recognize Neoepitopes Derived From Point and Frameshift Mutations.

机构信息

Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.

Center for Cancer Immune Therapy, Copenhagen University Hospital, Copenhagen, Denmark.

出版信息

Front Immunol. 2020 Mar 12;11:373. doi: 10.3389/fimmu.2020.00373. eCollection 2020.

DOI:10.3389/fimmu.2020.00373
PMID:32226429
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7080703/
Abstract

Mutation-derived neoantigens are important targets for T cell-mediated reactivity toward tumors and, due to their unique tumor expression, an attractive target for immunotherapy. Neoepitope-specific T cells have been detected across a number of solid cancers with high mutational burden tumors, but neoepitopes have been mostly selected from single nucleotide variations (SNVs), and little focus has been given to neoepitopes derived from in-frame and frameshift indels, which might be equally important and potentially highly immunogenic. Clear cell renal cell carcinomas (ccRCCs) are medium-range mutational burden tumors with a high pan-cancer proportion of frameshift mutations. In this study, the mutational landscape of tumors from six RCC patients was analyzed by whole-exome sequencing (WES) of DNA from tumor fragments (TFs), autologous tumor cell lines (TCLs), and tumor-infiltrating lymphocytes (TILs, germline reference). Neopeptides were predicted using MuPeXI, and patient-specific peptide-MHC (pMHC) libraries were created for all neopeptides with a rank score < 2 for binding to the patient's HLAs. T cell recognition toward neoepitopes in TILs was evaluated using the technology of DNA barcode-labeled pMHC multimers. The patient-specific libraries consisted of, on average, 258 putative neopeptides (range, 103-397, = 6). In four patients, WES was performed on two different sources (TF and TCL), whereas in two patients, WES was performed only on TF. Most of the peptides were predicted from both sources. However, a fraction was predicted from one source only. Among the total predicted neopeptides, 16% were derived from frameshift indels. T cell recognition of 52 neoepitopes was detected across all patients (range, 4-18, = 6) and spanning two to five HLA restrictions per patient. On average, 21% of the recognized neoepitopes were derived from frameshift indels (range, 0-43%, = 6). Thus, frameshift indels are equally represented in the pool of immunogenic neoepitopes as SNV-derived neoepitopes. This suggests the importance of a broad neopeptide prediction strategy covering multiple sources of tumor material, and including different genetic alterations. This study, for the first time, describes the T cell recognition of frameshift-derived neoepitopes in RCC and determines their immunogenic profile.

摘要

突变衍生的新抗原是 T 细胞介导的肿瘤反应的重要靶点,由于其在肿瘤中的独特表达,成为免疫治疗的有吸引力的靶点。在具有高突变负担的多种实体瘤中已经检测到了新表位特异性 T 细胞,但新表位主要是从单核苷酸变异 (SNV) 中选择的,而很少关注框移和框移插入缺失衍生的新表位,这些新表位可能同样重要,并且具有潜在的高度免疫原性。透明细胞肾细胞癌 (ccRCC) 是中等突变负担的肿瘤,在泛癌中具有较高的框移突变比例。在这项研究中,通过对肿瘤片段 (TFs)、自体肿瘤细胞系 (TCLs) 和肿瘤浸润淋巴细胞 (TILs,种系参考) 的 DNA 进行全外显子组测序 (WES),分析了 6 名 RCC 患者的肿瘤突变情况。使用 MuPeXI 预测新肽,为所有结合评分<2 的新肽创建患者特异性肽-MHC (pMHC) 文库。使用 DNA 条码标记的 pMHC 多聚体技术评估 TIL 中针对新表位的 T 细胞识别。患者特异性文库平均包含 258 个假定的新肽 (范围为 103-397, = 6)。在 4 名患者中,对两种不同来源 (TF 和 TCL) 进行了 WES,而在 2 名患者中,仅对 TF 进行了 WES。大多数肽来自两个来源。然而,一部分只来自一个来源。在预测的所有新肽中,有 16%来自框移插入缺失。在所有患者中都检测到了 52 个新表位的 T 细胞识别 (范围为 4-18, = 6),跨越每位患者的两个至五个 HLA 限制。平均而言,21%的识别新表位来自框移插入缺失 (范围为 0-43%, = 6)。因此,框移插入缺失与 SNV 衍生的新表位一样,在免疫原性新表位池中具有代表性。这表明需要采用广泛的新肽预测策略,涵盖肿瘤材料的多种来源,并包括不同的遗传改变。这项研究首次描述了 RCC 中框移衍生的新表位的 T 细胞识别,并确定了它们的免疫表型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c08b/7080703/163130cfdc0d/fimmu-11-00373-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c08b/7080703/9598dd743b1a/fimmu-11-00373-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c08b/7080703/b7b54f72bad4/fimmu-11-00373-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c08b/7080703/163130cfdc0d/fimmu-11-00373-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c08b/7080703/9598dd743b1a/fimmu-11-00373-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c08b/7080703/b7b54f72bad4/fimmu-11-00373-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c08b/7080703/163130cfdc0d/fimmu-11-00373-g0003.jpg

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