Dataset Information

Immunological classification of glioblastoma and its prognostic implications.

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 IDH-wildtype and combined +7/-10, while the Im1 subtype was associated with IDH 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 HLA gene expression. Im1 was characterized by low immune score and stromal score, high tumor purity, enrichment of lymphocytes, and low immune checkpoint and HLA 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.

SUBMITTER: Wang Q

PROVIDER: S-EPMC9730108 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Immunological classification of glioblastoma and its prognostic implications.

Wang Qiangwei Q Lin Weiwei W Liu Tianjian T Hu Jue J Zhu Yongjian Y

American journal of translational research 20221115 11

<h4>Objectives</h4>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.<h4>Methods</h4>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 tw ...[more]

PMID: 36505340

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Project description:BackgroundThis study was designed to explore the progression patterns of IDH-wildtype glioblastoma (GBM) at first recurrence after chemoradiotherapy.MethodsRecords from 247 patients who underwent progression after diagnosis of IDH-wildtype GBM was retrospectively reviewed. Progression patterns were classified as either local, distant, subependymal or leptomeningeal dissemination based on the preoperative and serial postoperative radiographic images. The clinical and molecular characteristics of different progression patterns were analyzed.ResultsA total of 186 (75.3%) patients had local progression, 15 (6.1%) patients had distant progression, 33 (13.3%) patients had subependymal dissemination, and 13 (5.3%) patients had leptomeningeal dissemination. The most favorable survival occurred in patients with local progression, while no significant difference of survival was found among patients with distant progression, subependymal or leptomeningeal dissemination who were thereby reclassified into non-local group. Multivariable analysis showed that chemotherapy was a protective factor for non-local progression, while gender of male, subventricular zone (SVZ) involvement and O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation were confirmed as risk factors for non-local progression (P < 0.05). Based on the factors screened by multivariable analysis, a nomogram was constructed which conferred high accuracy in predicting non-local progression. Patients in non-local group could be divided into long- and short-term survivors who differed in the rates of SVZ involvement, MGMT promoter methylation and reirradiation (P < 0.05), and a nomogram integrating these factors showed high accuracy in predicting long-term survivors.ConclusionPatients harboring different progression patterns conferred distinct clinical and molecular characteristics. Our nomograms could provide theoretical references for physicians to make more personalized and precise treatment decisions.

Dataset Information

Immunological classification of glioblastoma and its prognostic implications.

Objectives

Methods

Results

Conclusions

Publications

Immunological classification of glioblastoma and its prognostic implications.

Similar Datasets

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