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J Immunother Cancer. 2021 Oct;9(10). doi: 10.1136/jitc-2021-002451.
3
Tumor microenvironment is associated with clinical and genetic properties of diffuse gliomas and predicts overall survival.肿瘤微环境与弥漫性神经胶质瘤的临床和遗传特征相关,并可预测总体生存率。
Cancer Immunol Immunother. 2022 Apr;71(4):953-966. doi: 10.1007/s00262-021-03058-4. Epub 2021 Sep 17.
4
The 2021 WHO Classification of Tumors of the Central Nervous System: a summary.2021 年世卫组织中枢神经系统肿瘤分类:概述。
Neuro Oncol. 2021 Aug 2;23(8):1231-1251. doi: 10.1093/neuonc/noab106.
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Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients.中国脑胶质瘤基因组图谱(CGGA):来自中国脑胶质瘤患者的功能基因组数据的综合资源。
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6
Immune classification of clear cell renal cell carcinoma.透明细胞肾细胞癌的免疫分类。
Sci Rep. 2021 Feb 22;11(1):4338. doi: 10.1038/s41598-021-83767-z.
7
Effects of oncolytic viruses and viral vectors on immunity in glioblastoma.溶瘤病毒和病毒载体对胶质母细胞瘤免疫的影响。
Gene Ther. 2022 Apr;29(3-4):115-126. doi: 10.1038/s41434-020-00207-9. Epub 2020 Nov 15.
8
Clinical practice guidelines for the management of adult diffuse gliomas.成人弥漫性胶质瘤治疗的临床实践指南。
Cancer Lett. 2021 Feb 28;499:60-72. doi: 10.1016/j.canlet.2020.10.050. Epub 2020 Nov 6.
9
Management of glioblastoma: State of the art and future directions.脑胶质瘤的治疗:现状与未来方向。
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10
Effect of Nivolumab vs Bevacizumab in Patients With Recurrent Glioblastoma: The CheckMate 143 Phase 3 Randomized Clinical Trial.纳武利尤单抗对比贝伐珠单抗治疗复发性胶质母细胞瘤患者的效果:CheckMate 143 期随机临床试验。
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胶质母细胞瘤的免疫分类及其预后意义。

Immunological classification of glioblastoma and its prognostic implications.

作者信息

Wang Qiangwei, Lin Weiwei, Liu Tianjian, Hu Jue, Zhu Yongjian

机构信息

Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine Hangzhou 310009, Zhejiang, China.

Clinical Research Center for Neurological Diseases of Zhejiang Province Hangzhou 310009, Zhejiang, China.

出版信息

Am J Transl Res. 2022 Nov 15;14(11):8009-8022. eCollection 2022.

PMID:36505340
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9730108/
Abstract

OBJECTIVES

The progress of immunotherapy for glioblastoma (GBM) is currently slow. To improve immunotherapy, we need a deeper understanding of the immune microenvironment of GBM. Here, we aimed to establish a classification system based on immune expression profile in GBM.

METHODS

Immune gene expression profiles of 152 patients with GBM from The Cancer Genome Atlas (TCGA) were used to identify subtypes by consensus clustering, and the classification system was reproduced in the two validation datasets (CGGA and GSE16011). Clinical information, molecular characteristics, immune infiltration, and genomic variation were integrated to characterize the subtypes.

RESULTS

Two distinct immune subtypes in GBM were successfully identified and validated. The Im2 subtype was closely related to -wildtype and combined +7/-10, while the Im1 subtype was associated with mutation. Survival curve analysis showed that the Im2 subtype was associated with significantly shorter survival than the Im1 subtype. Im2 showed a high immune score and stromal score, low tumor purity, enrichment of macrophages, and high immune checkpoint and gene expression. Im1 was characterized by low immune score and stromal score, high tumor purity, enrichment of lymphocytes, and low immune checkpoint and gene expression. Finally, we developed an immune-related signature in GBM with better prognosis prediction.

CONCLUSIONS

Our study confirmed the immune heterogeneity of GBM and might provide valuable classification for immunotherapy.

摘要

目的

目前胶质母细胞瘤(GBM)免疫治疗进展缓慢。为改善免疫治疗,我们需要更深入了解GBM的免疫微环境。在此,我们旨在基于GBM的免疫表达谱建立一种分类系统。

方法

利用来自癌症基因组图谱(TCGA)的152例GBM患者的免疫基因表达谱,通过一致性聚类确定亚型,并在两个验证数据集(CGGA和GSE16011)中重现该分类系统。整合临床信息、分子特征、免疫浸润和基因组变异以表征这些亚型。

结果

成功鉴定并验证了GBM中的两种不同免疫亚型。Im2亚型与野生型以及联合的+7/-10密切相关,而Im1亚型与突变相关。生存曲线分析表明,Im2亚型的生存时间显著短于Im1亚型。Im2显示出高免疫评分和基质评分、低肿瘤纯度、巨噬细胞富集以及高免疫检查点和基因表达。Im1的特征是低免疫评分和基质评分、高肿瘤纯度、淋巴细胞富集以及低免疫检查点和基因表达。最后,我们在GBM中开发了一种具有更好预后预测能力的免疫相关特征。

结论

我们的研究证实了GBM的免疫异质性,并可能为免疫治疗提供有价值的分类。