Evidence map›Paper›PMID 32392361›Full record

ArticleMolecular oncology2020

Classification of diffuse lower-grade glioma based on immunological profiling.

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 and 1 more

Open access · goldAbstract read
In one paragraph

Article in Molecular oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
7.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

44 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.

  1. Pooled it
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  7. Identifying Diffuse Glioma Subtypes Based on Pathway Enrichment Evaluation.Interdisciplinary sciences, computational life sciences · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 1 institution in 1 country.

Fan WuDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Zhi-Liang WangDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Kuan-Yu WangDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Guan-Zhang LiDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Rui-Chao ChaiDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Yu-Qing LiuDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Hao-Yu JiangDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
You ZhaiDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Yue-Mei FengDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Zheng ZhaoDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Wei ZhangDepartment of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.ORCID 0000-0001-7800-3189
Capital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AdultBrain NeoplasmsCluster AnalysisFemaleGenome, HumanGliomaHumansLymphocytes, Tumor-InfiltratingMaleNeoplasm GradingPrognosisProportional Hazards ModelsReproducibility of ResultsTreatment Outcomediffuse lower-grade gliomaimmune classificationprognosistumor microenvironment

Identifiers

PMID32392361
PMCPMC7463381
OpenAlexW3023158855

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.