Classification of diffuse lower‐grade glioma based on immunological profiling
Transcriptomic data derived from bulk sequencing have been applied to delineate the tumor microenvironment (TME) and define immune subtypes in various cancers, which may facilitate the design of immunotherapy treatment strategies. We herein gathered published gene expression data from diffuse lower‐...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-09-01
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| Series: | Molecular Oncology |
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| Online Access: | https://doi.org/10.1002/1878-0261.12707 |
| _version_ | 1818199040495452160 |
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| author | Fan Wu Zhi‐Liang Wang Kuan‐Yu Wang Guan‐Zhang Li Rui‐Chao Chai Yu‐Qing Liu Hao‐Yu Jiang You Zhai Yue‐Mei Feng Zheng Zhao Wei Zhang |
| author_facet | Fan Wu Zhi‐Liang Wang Kuan‐Yu Wang Guan‐Zhang Li Rui‐Chao Chai Yu‐Qing Liu Hao‐Yu Jiang You Zhai Yue‐Mei Feng Zheng Zhao Wei Zhang |
| author_sort | Fan Wu |
| collection | DOAJ |
| description | Transcriptomic data derived from bulk sequencing have been applied to delineate the tumor microenvironment (TME) and define immune subtypes in various cancers, which may facilitate the design of immunotherapy treatment strategies. We herein gathered published gene expression data from diffuse lower‐grade glioma (LGG) patients to identify immune subtypes. Based on the immune gene profiles of 402 LGG patients from The Cancer Genome Atlas, we performed consensus clustering to determine robust clusters of patients, and evaluated their reproducibility in three Chinese Glioma Genome Atlas cohorts. We further integrated immunogenomics methods to characterize the immune environment of each subtype. Our analysis identified and validated three immune subtypes—Im1, Im2, and Im3—characterized by differences in lymphocyte signatures, somatic DNA alterations, and clinical outcomes. Im1 had a higher infiltration of CD8+ T cells, Th17, and mast cells. Im2 was defined by elevated cytolytic activity, exhausted CD8+ T cells, macrophages, higher levels of aneuploidy, and tumor mutation burden, and these patients had worst outcome. Im3 displayed more prominent T helper cell and APC coinhibition signatures, with elevated pDCs and macrophages. Each subtype was associated with distinct somatic alterations. Moreover, we applied graph structure learning‐based dimensionality reduction to the immune landscape and revealed significant intracluster heterogeneity with Im2 subtype. Finally, we developed and validated an immune signature with better performance of prognosis prediction. Our results demonstrated the immunological heterogeneity within diffuse LGG and provided valuable stratification for the design of future immunotherapy. |
| first_indexed | 2024-12-12T02:15:27Z |
| format | Article |
| id | doaj.art-238a5db78908445a9967a767b7c5a9e8 |
| institution | Directory Open Access Journal |
| issn | 1574-7891 1878-0261 |
| language | English |
| last_indexed | 2024-12-12T02:15:27Z |
| publishDate | 2020-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Molecular Oncology |
| spelling | doaj.art-238a5db78908445a9967a767b7c5a9e82022-12-22T00:41:48ZengWileyMolecular Oncology1574-78911878-02612020-09-011492081209510.1002/1878-0261.12707Classification of diffuse lower‐grade glioma based on immunological profilingFan Wu0Zhi‐Liang Wang1Kuan‐Yu Wang2Guan‐Zhang Li3Rui‐Chao Chai4Yu‐Qing Liu5Hao‐Yu Jiang6You Zhai7Yue‐Mei Feng8Zheng Zhao9Wei Zhang10Department of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaDepartment of Molecular Neuropathology Beijing Neurosurgical Institute Capital Medical University Beijing ChinaTranscriptomic data derived from bulk sequencing have been applied to delineate the tumor microenvironment (TME) and define immune subtypes in various cancers, which may facilitate the design of immunotherapy treatment strategies. We herein gathered published gene expression data from diffuse lower‐grade glioma (LGG) patients to identify immune subtypes. Based on the immune gene profiles of 402 LGG patients from The Cancer Genome Atlas, we performed consensus clustering to determine robust clusters of patients, and evaluated their reproducibility in three Chinese Glioma Genome Atlas cohorts. We further integrated immunogenomics methods to characterize the immune environment of each subtype. Our analysis identified and validated three immune subtypes—Im1, Im2, and Im3—characterized by differences in lymphocyte signatures, somatic DNA alterations, and clinical outcomes. Im1 had a higher infiltration of CD8+ T cells, Th17, and mast cells. Im2 was defined by elevated cytolytic activity, exhausted CD8+ T cells, macrophages, higher levels of aneuploidy, and tumor mutation burden, and these patients had worst outcome. Im3 displayed more prominent T helper cell and APC coinhibition signatures, with elevated pDCs and macrophages. Each subtype was associated with distinct somatic alterations. Moreover, we applied graph structure learning‐based dimensionality reduction to the immune landscape and revealed significant intracluster heterogeneity with Im2 subtype. Finally, we developed and validated an immune signature with better performance of prognosis prediction. Our results demonstrated the immunological heterogeneity within diffuse LGG and provided valuable stratification for the design of future immunotherapy.https://doi.org/10.1002/1878-0261.12707diffuse lower‐grade gliomaimmune classificationprognosistumor microenvironment |
| spellingShingle | Fan Wu Zhi‐Liang Wang Kuan‐Yu Wang Guan‐Zhang Li Rui‐Chao Chai Yu‐Qing Liu Hao‐Yu Jiang You Zhai Yue‐Mei Feng Zheng Zhao Wei Zhang Classification of diffuse lower‐grade glioma based on immunological profiling Molecular Oncology diffuse lower‐grade glioma immune classification prognosis tumor microenvironment |
| title | Classification of diffuse lower‐grade glioma based on immunological profiling |
| title_full | Classification of diffuse lower‐grade glioma based on immunological profiling |
| title_fullStr | Classification of diffuse lower‐grade glioma based on immunological profiling |
| title_full_unstemmed | Classification of diffuse lower‐grade glioma based on immunological profiling |
| title_short | Classification of diffuse lower‐grade glioma based on immunological profiling |
| title_sort | classification of diffuse lower grade glioma based on immunological profiling |
| topic | diffuse lower‐grade glioma immune classification prognosis tumor microenvironment |
| url | https://doi.org/10.1002/1878-0261.12707 |
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