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Ji Q, Zheng Y, Zhou L, Chen F, Li W. Unveiling divergent treatment prognoses in IDHwt-GBM subtypes through multiomics clustering: a swift dual MRI-mRNA model for precise subtype prediction. J Transl Med 2024; 22:578. [PMID: 38890658 PMCID: PMC11186189 DOI: 10.1186/s12967-024-05401-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND IDH1-wildtype glioblastoma multiforme (IDHwt-GBM) is a highly heterogeneous and aggressive brain tumour characterised by a dismal prognosis and significant challenges in accurately predicting patient outcomes. To address these issues and personalise treatment approaches, we aimed to develop and validate robust multiomics molecular subtypes of IDHwt-GBM. Through this, we sought to uncover the distinct molecular signatures underlying these subtypes, paving the way for improved diagnosis and targeted therapy for this challenging disease. METHODS To identify stable molecular subtypes among 184 IDHwt-GBM patients from TCGA, we used the consensus clustering method to consolidate the results from ten advanced multiomics clustering approaches based on mRNA, lncRNA, and mutation data. We developed subtype prediction models using the PAM and machine learning algorithms based on mRNA and MRI data for enhanced clinical utility. These models were validated in five independent datasets, and an online interactive system was created. We conducted a comprehensive assessment of the clinical impact, drug treatment response, and molecular associations of the IDHwt-GBM subtypes. RESULTS In the TCGA cohort, two molecular subtypes, class 1 and class 2, were identified through multiomics clustering of IDHwt-GBM patients. There was a significant difference in survival between Class 1 and Class 2 patients, with a hazard ratio (HR) of 1.68 [1.15-2.47]. This difference was validated in other datasets (CGGA: HR = 1.75[1.04, 2.94]; CPTAC: HR = 1.79[1.09-2.91]; GALSS: HR = 1.66[1.09-2.54]; UCSF: HR = 1.33[1.00-1.77]; UPENN HR = 1.29[1.04-1.58]). Additionally, class 2 was more sensitive to treatment with radiotherapy combined with temozolomide, and this sensitivity was validated in the GLASS cohort. Correspondingly, class 2 and class 1 exhibited significant differences in mutation patterns, enriched pathways, programmed cell death (PCD), and the tumour immune microenvironment. Class 2 had more mutation signatures associated with defective DNA mismatch repair (P = 0.0021). Enriched pathways of differentially expressed genes in class 1 and class 2 (P-adjust < 0.05) were mainly related to ferroptosis, the PD-1 checkpoint pathway, the JAK-STAT signalling pathway, and other programmed cell death and immune-related pathways. The different cell death modes and immune microenvironments were validated across multiple datasets. Finally, our developed survival prediction model, which integrates molecular subtypes, age, and sex, demonstrated clinical benefits based on the decision curve in the test set. We deployed the molecular subtyping prediction model and survival prediction model online, allowing interactive use and facilitating user convenience. CONCLUSIONS Molecular subtypes were identified and verified through multiomics clustering in IDHwt-GBM patients. These subtypes are linked to specific mutation patterns, the immune microenvironment, prognoses, and treatment responses.
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Affiliation(s)
- Qiang Ji
- Department of Neuro-Oncology, Cancer Center, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Yi Zheng
- Department of Neuro-Oncology, Cancer Center, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Lili Zhou
- Department of Neuro-Oncology, Cancer Center, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Feng Chen
- Department of Neuro-Oncology, Cancer Center, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Wenbin Li
- Department of Neuro-Oncology, Cancer Center, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
- National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China.
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Drexler R, Khatri R, Schüller U, Eckhardt A, Ryba A, Sauvigny T, Dührsen L, Mohme M, Ricklefs T, Bode H, Hausmann F, Huber TB, Bonn S, Voß H, Neumann JE, Silverbush D, Hovestadt V, Suvà ML, Lamszus K, Gempt J, Westphal M, Heiland DH, Hänzelmann S, Ricklefs FL. Temporal change of DNA methylation subclasses between matched newly diagnosed and recurrent glioblastoma. Acta Neuropathol 2024; 147:21. [PMID: 38244080 PMCID: PMC10799798 DOI: 10.1007/s00401-023-02677-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/08/2023] [Accepted: 12/24/2023] [Indexed: 01/22/2024]
Abstract
The longitudinal transition of phenotypes is pivotal in glioblastoma treatment resistance and DNA methylation emerged as an important tool for classifying glioblastoma phenotypes. We aimed to characterize DNA methylation subclass heterogeneity during progression and assess its clinical impact. Matched tissues from 47 glioblastoma patients were subjected to DNA methylation profiling, including CpG-site alterations, tissue and serum deconvolution, mass spectrometry, and immunoassay. Effects of clinical characteristics on temporal changes and outcomes were studied. Among 47 patients, 8 (17.0%) had non-matching classifications at recurrence. In the remaining 39 cases, 28.2% showed dominant DNA methylation subclass transitions, with 72.7% being a mesenchymal subclass. In general, glioblastomas with a subclass transition showed upregulated metabolic processes. Newly diagnosed glioblastomas with mesenchymal transition displayed increased stem cell-like states and decreased immune components at diagnosis and exhibited elevated immune signatures and cytokine levels in serum. In contrast, tissue of recurrent glioblastomas with mesenchymal transition showed increased immune components but decreased stem cell-like states. Survival analyses revealed comparable outcomes for patients with and without subclass transitions. This study demonstrates a temporal heterogeneity of DNA methylation subclasses in 28.2% of glioblastomas, not impacting patient survival. Changes in cell state composition associated with subclass transition may be crucial for recurrent glioblastoma targeted therapies.
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Affiliation(s)
- Richard Drexler
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Robin Khatri
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ulrich Schüller
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Pediatric Hematology and Oncology, Research Institute Children's Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Alicia Eckhardt
- Department of Pediatric Hematology and Oncology, Research Institute Children's Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
- Department of Radiation Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alice Ryba
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Thomas Sauvigny
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Lasse Dührsen
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Malte Mohme
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Tammo Ricklefs
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Helena Bode
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Fabian Hausmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias B Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hannah Voß
- Section of Mass Spectrometric Proteomics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Julia E Neumann
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dana Silverbush
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Volker Hovestadt
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mario L Suvà
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Katrin Lamszus
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Jens Gempt
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Manfred Westphal
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany
| | - Dieter H Heiland
- Department of Neurosurgery, Medical Center University of Freiburg, Freiburg, Germany
| | - Sonja Hänzelmann
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Franz L Ricklefs
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
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Jaraíz-Rodríguez M, Del Prado L, Balsa E. Metabolic remodeling in astrocytes: Paving the path to brain tumor development. Neurobiol Dis 2023; 188:106327. [PMID: 37839712 DOI: 10.1016/j.nbd.2023.106327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/11/2023] [Accepted: 10/12/2023] [Indexed: 10/17/2023] Open
Abstract
The brain is a highly metabolic organ, composed of multiple cell classes, that controls crucial functions of the body. Although neurons have traditionally been the main protagonist, astrocytes have gained significant attention over the last decade. In this regard, astrocytes are a type of glial cells that have recently emerged as critical regulators of central nervous system (CNS) function and play a significant role in maintaining brain energy metabolism. However, in certain scenarios, astrocyte behavior can go awry, which poses a significant threat to brain integrity and function. This is definitively the case for mutations that turn normal astrocytes and astrocytic precursors into gliomas, an aggressive type of brain tumor. In addition, healthy astrocytes can interact with tumor cells, becoming part of the tumor microenvironment and influencing disease progression. In this review, we discuss the recent evidence suggesting that disturbed metabolism in astrocytes can contribute to the development and progression of fatal human diseases such as cancer. Emphasis is placed on detailing the molecular bases and metabolic pathways of this disease and highlighting unique metabolic vulnerabilities that can potentially be exploited to develop successful therapeutic opportunities.
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Affiliation(s)
- Myriam Jaraíz-Rodríguez
- Centro de Biología Molecular Severo Ochoa (CBMSO), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain
| | - Lucia Del Prado
- Centro de Biología Molecular Severo Ochoa (CBMSO), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain
| | - Eduardo Balsa
- Centro de Biología Molecular Severo Ochoa (CBMSO), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain; Instituto Universitario de Biología Molecular - IUBM (Universidad Autónoma de Madrid), Madrid, Spain.
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