• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

CSiN 评分评估肿瘤新生抗原性,结合克隆性和免疫原性预测免疫治疗结果。

Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes.

机构信息

Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

Division of Hematology/Oncology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

出版信息

Sci Immunol. 2020 Feb 21;5(44). doi: 10.1126/sciimmunol.aaz3199.

DOI:10.1126/sciimmunol.aaz3199
PMID:32086382
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7239327/
Abstract

Lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy. Tumor neoantigens are critical targets of the host antitumor immune response, and their presence correlates with the efficacy of immunotherapy treatment. Many studies involving assessment of tumor neoantigens principally focus on total neoantigen load, which simplistically treats all neoantigens equally. Neoantigen load has been linked with treatment response and prognosis in some studies but not others. We developed a Cauchy-Schwarz index of Neoantigens (CSiN) score to better account for the degree of concentration of immunogenic neoantigens in truncal mutations. Unlike total neoantigen load determinations, CSiN incorporates the effect of both clonality and MHC binding affinity of neoantigens when characterizing tumor neoantigen profiles. By analyzing the clinical responses in 501 treated patients with cancer (with most receiving checkpoint inhibitors) and the overall survival of 1978 patients with cancer at baseline, we showed that CSiN scores predict treatment response to checkpoint inhibitors and prognosis in patients with melanoma, lung cancer, and kidney cancer. CSiN score substantially outperformed prior genetics-based prediction methods of responsiveness and fills an important gap in research involving assessment of tumor neoantigen burden.

摘要

对检查点抑制剂缺乏反应是癌症免疫治疗现代时代的一个核心问题。肿瘤新生抗原是宿主抗肿瘤免疫反应的关键靶标,其存在与免疫治疗效果相关。许多涉及评估肿瘤新生抗原的研究主要集中在总新生抗原负荷上,这种方法简单地将所有新生抗原同等对待。在一些研究中,新生抗原负荷与治疗反应和预后相关,但在其他研究中则不然。我们开发了一种新生抗原的柯西-施瓦茨指数(CSiN)评分,以更好地反映主干突变中免疫原性新生抗原的集中程度。与总新生抗原负荷测定不同,CSiN 在描述肿瘤新生抗原谱时,同时考虑了新生抗原的克隆性和 MHC 结合亲和力的影响。通过分析 501 名接受癌症治疗(大多数接受检查点抑制剂治疗)患者的临床反应和 1978 名基线癌症患者的总生存率,我们表明 CSiN 评分可预测黑色素瘤、肺癌和肾癌患者对检查点抑制剂的治疗反应和预后。CSiN 评分大大优于先前基于遗传学的反应预测方法,并填补了评估肿瘤新生抗原负担研究中的一个重要空白。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1282/7239327/18482b0ed029/nihms-1581910-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1282/7239327/deb28601e20e/nihms-1581910-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1282/7239327/f9dc5878e6e7/nihms-1581910-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1282/7239327/18482b0ed029/nihms-1581910-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1282/7239327/deb28601e20e/nihms-1581910-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1282/7239327/f9dc5878e6e7/nihms-1581910-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1282/7239327/18482b0ed029/nihms-1581910-f0003.jpg

相似文献

1
Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes.CSiN 评分评估肿瘤新生抗原性,结合克隆性和免疫原性预测免疫治疗结果。
Sci Immunol. 2020 Feb 21;5(44). doi: 10.1126/sciimmunol.aaz3199.
2
A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy.一种新抗原适应性模型可预测肿瘤对检查点阻断免疫疗法的反应。
Nature. 2017 Nov 23;551(7681):517-520. doi: 10.1038/nature24473. Epub 2017 Nov 8.
3
The Neoantigen Landscape of Mycosis Fungoides.蕈样肉芽肿的新抗原景观。
Front Immunol. 2020 Nov 23;11:561234. doi: 10.3389/fimmu.2020.561234. eCollection 2020.
4
pTuneos: prioritizing tumor neoantigens from next-generation sequencing data.pTuneos:从下一代测序数据中优先选择肿瘤新生抗原。
Genome Med. 2019 Oct 30;11(1):67. doi: 10.1186/s13073-019-0679-x.
5
Evolution of Neoantigen Landscape during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer.非小细胞肺癌免疫检查点阻断治疗期间新抗原格局的演变
Cancer Discov. 2017 Mar;7(3):264-276. doi: 10.1158/2159-8290.CD-16-0828. Epub 2016 Dec 28.
6
Neoantigen vaccine: an emerging tumor immunotherapy.肿瘤新生抗原疫苗:一种新兴的肿瘤免疫疗法。
Mol Cancer. 2019 Aug 23;18(1):128. doi: 10.1186/s12943-019-1055-6.
7
ARID1A-Deficient Tumors Acquire Immunogenic Neoantigens during the Development of Resistance to Targeted Therapy.ARID1A 缺陷型肿瘤在对靶向治疗产生耐药性的过程中获得免疫原性新抗原。
Cancer Res. 2024 Sep 4;84(17):2792-2805. doi: 10.1158/0008-5472.CAN-23-2846.
8
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。
Science. 2016 Mar 25;351(6280):1463-9. doi: 10.1126/science.aaf1490. Epub 2016 Mar 3.
9
Construction and validation of an immunoediting-based optimized neoantigen load (ioTNL) model to predict the response and prognosis of immune checkpoint therapy in various cancers.基于免疫编辑的优化新抗原负荷(ioTNL)模型的构建与验证,用于预测多种癌症中免疫检查点治疗的反应和预后。
Aging (Albany NY). 2022 May 25;14(10):4586-4605. doi: 10.18632/aging.204101.
10
Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis.插入缺失衍生的肿瘤特异性新抗原和免疫表型:泛癌分析。
Lancet Oncol. 2017 Aug;18(8):1009-1021. doi: 10.1016/S1470-2045(17)30516-8. Epub 2017 Jul 7.

引用本文的文献

1
NeoPrecis: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes.NeoPrecis:通过整合合格的免疫原性和克隆性感知新抗原图谱增强免疫治疗反应预测
bioRxiv. 2025 Jul 27:2025.07.23.666355. doi: 10.1101/2025.07.23.666355.
2
Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes.通过深度学习分析肿瘤中B细胞受体库的抗原结合亲和力可预测免疫检查点抑制剂的治疗效果。
Nat Cancer. 2025 Jun 27. doi: 10.1038/s43018-025-01001-5.
3
CapHLA: a comprehensive tool to predict peptide presentation and binding to HLA class I and class II.

本文引用的文献

1
Tumor mutational load predicts survival after immunotherapy across multiple cancer types.肿瘤突变负荷可预测多种癌症类型免疫治疗后的生存情况。
Nat Genet. 2019 Feb;51(2):202-206. doi: 10.1038/s41588-018-0312-8. Epub 2019 Jan 14.
2
Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors.微卫星稳定型实体瘤中免疫检查点阻断反应的基因组相关性。
Nat Genet. 2018 Sep;50(9):1271-1281. doi: 10.1038/s41588-018-0200-2. Epub 2018 Aug 27.
3
An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors.
CapHLA:一种预测肽段呈递以及与HLA I类和II类分子结合的综合工具。
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbae595.
4
Mapping cellular interactions from spatially resolved transcriptomics data.从空间分辨转录组学数据中绘制细胞相互作用图谱。
Nat Methods. 2024 Oct;21(10):1830-1842. doi: 10.1038/s41592-024-02408-1. Epub 2024 Sep 3.
5
Development and Clinical Applications of Therapeutic Cancer Vaccines with Individualized and Shared Neoantigens.具有个体化和共享新抗原的治疗性癌症疫苗的研发与临床应用
Vaccines (Basel). 2024 Jun 27;12(7):717. doi: 10.3390/vaccines12070717.
6
PKD1 mutant clones within cirrhotic livers inhibit steatohepatitis without promoting cancer.在肝硬化肝脏中,PKD1 突变克隆抑制脂肪性肝炎而不促进癌症。
Cell Metab. 2024 Aug 6;36(8):1711-1725.e8. doi: 10.1016/j.cmet.2024.05.015. Epub 2024 Jun 19.
7
Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.用于预测肿瘤免疫检查点抑制剂疗效的生物标志物和计算模型。
Front Immunol. 2024 Mar 8;15:1368749. doi: 10.3389/fimmu.2024.1368749. eCollection 2024.
8
DNA Methylation Signatures Correlate with Response to Immune Checkpoint Inhibitors in Metastatic Melanoma.DNA 甲基化特征与转移性黑色素瘤对免疫检查点抑制剂的反应相关。
Target Oncol. 2024 Mar;19(2):263-275. doi: 10.1007/s11523-024-01041-4. Epub 2024 Feb 24.
9
Immunotherapy resistance in solid tumors: mechanisms and potential solutions.实体瘤免疫治疗耐药:机制与潜在解决方案。
Cancer Biol Ther. 2024 Dec 31;25(1):2315655. doi: 10.1080/15384047.2024.2315655. Epub 2024 Feb 22.
10
Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates.使用新抗原候选物进行多实例学习以预测免疫检查点阻断疗效
iScience. 2023 Sep 22;26(11):108014. doi: 10.1016/j.isci.2023.108014. eCollection 2023 Nov 17.
利用肿瘤移植体剖析肾细胞癌肿瘤微环境的实证方法确定了与预后炎症因子缺失的联系。
Cancer Discov. 2018 Sep;8(9):1142-1155. doi: 10.1158/2159-8290.CD-17-1246. Epub 2018 Jun 8.
4
Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma.阿替利珠单抗单药或联合贝伐珠单抗与舒尼替尼治疗肾细胞癌的临床活性和分子相关性。
Nat Med. 2018 Jun;24(6):749-757. doi: 10.1038/s41591-018-0053-3. Epub 2018 Jun 4.
5
Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer.晚期非小细胞肺癌患者对联合免疫治疗反应的基因组特征。
Cancer Cell. 2018 May 14;33(5):843-852.e4. doi: 10.1016/j.ccell.2018.03.018. Epub 2018 Apr 12.
6
Tracking Cancer Evolution Reveals Constrained Routes to Metastases: TRACERx Renal.追踪癌症演进揭示转移受限途径:TRACERx 肾脏。
Cell. 2018 Apr 19;173(3):581-594.e12. doi: 10.1016/j.cell.2018.03.057. Epub 2018 Apr 12.
7
Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations.癌症中的抗程序性死亡蛋白1(Anti-PD-1)和抗细胞毒性T淋巴细胞相关蛋白4(Anti-CTLA-4)疗法:作用机制、疗效及局限性
Front Oncol. 2018 Mar 28;8:86. doi: 10.3389/fonc.2018.00086. eCollection 2018.
8
Host expression of PD-L1 determines efficacy of PD-L1 pathway blockade-mediated tumor regression.宿主表达 PD-L1 决定 PD-L1 通路阻断介导的肿瘤消退疗效。
J Clin Invest. 2018 Feb 1;128(2):805-815. doi: 10.1172/JCI96113. Epub 2018 Jan 16.
9
Accuracy of Programs for the Determination of Human Leukocyte Antigen Alleles from Next-Generation Sequencing Data.基于新一代测序数据测定人类白细胞抗原等位基因程序的准确性
Front Immunol. 2017 Dec 13;8:1815. doi: 10.3389/fimmu.2017.01815. eCollection 2017.
10
Neoantigen Targeting-Dawn of a New Era in Cancer Immunotherapy?新抗原靶向——癌症免疫治疗新时代的曙光?
Front Immunol. 2017 Dec 19;8:1848. doi: 10.3389/fimmu.2017.01848. eCollection 2017.