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Malta TM, Sabedot TS, Morosini NS, Datta I, Garofano L, Vallentgoed W, Varn FS, Aldape K, D'Angelo F, Bakas S, Barnholtz-Sloan JS, Gan HK, Hasanain M, Hau AC, Johnson KC, Cazacu S, deCarvalho AC, Khasraw M, Kocakavuk E, Kouwenhoven MC, Migliozzi S, Niclou SP, Niers JM, Ormond DR, Paek SH, Reifenberger G, Sillevis Smitt PA, Smits M, Stead LF, van den Bent MJ, Van Meir EG, Walenkamp A, Weiss T, Weller M, Westerman BA, Ylstra B, Wesseling P, Lasorella A, French PJ, Poisson LM, Verhaak RG, Iavarone A, Noushmehr H. The Epigenetic Evolution of Glioma Is Determined by the IDH1 Mutation Status and Treatment Regimen. Cancer Res 2024; 84:741-756. [PMID: 38117484 PMCID: PMC10911804 DOI: 10.1158/0008-5472.can-23-2093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 09/15/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023]
Abstract
Tumor adaptation or selection is thought to underlie therapy resistance in glioma. To investigate longitudinal epigenetic evolution of gliomas in response to therapeutic pressure, we performed an epigenomic analysis of 132 matched initial and recurrent tumors from patients with IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) glioma. IDHwt gliomas showed a stable epigenome over time with relatively low levels of global methylation. The epigenome of IDHmut gliomas showed initial high levels of genome-wide DNA methylation that was progressively reduced to levels similar to those of IDHwt tumors. Integration of epigenomics, gene expression, and functional genomics identified HOXD13 as a master regulator of IDHmut astrocytoma evolution. Furthermore, relapse of IDHmut tumors was accompanied by histologic progression that was associated with survival, as validated in an independent cohort. Finally, the initial cell composition of the tumor microenvironment varied between IDHwt and IDHmut tumors and changed differentially following treatment, suggesting increased neoangiogenesis and T-cell infiltration upon treatment of IDHmut gliomas. This study provides one of the largest cohorts of paired longitudinal glioma samples with epigenomic, transcriptomic, and genomic profiling and suggests that treatment of IDHmut glioma is associated with epigenomic evolution toward an IDHwt-like phenotype. SIGNIFICANCE Standard treatments are related to loss of DNA methylation in IDHmut glioma, resulting in epigenetic activation of genes associated with tumor progression and alterations in the microenvironment that resemble treatment-naïve IDHwt glioma.
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Affiliation(s)
- Tathiane M. Malta
- School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Thais S. Sabedot
- Hermelin Brain Tumor Center, Henry Ford Health System, Detroit, Michigan
| | | | - Indrani Datta
- Hermelin Brain Tumor Center, Henry Ford Health System, Detroit, Michigan
| | - Luciano Garofano
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
| | - Wies Vallentgoed
- Neurology Department, The Brain Tumour Center, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Frederick S. Varn
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | | | - Fulvio D'Angelo
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Hui K. Gan
- Olivia Newton-John Cancer Research Institute, Austin Health, Heidelberg, Melbourne, Australia
| | - Mohammad Hasanain
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
| | | | - Kevin C. Johnson
- Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut
| | - Simona Cazacu
- Hermelin Brain Tumor Center, Henry Ford Health System, Detroit, Michigan
| | - Ana C. deCarvalho
- Hermelin Brain Tumor Center, Henry Ford Health System, Detroit, Michigan
| | | | - Emre Kocakavuk
- Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut
- Department of Hematology and Stem Cell Transplantation, West German Cancer Center (WTZ), National Center for Tumor Diseases (NCT) West, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Mathilde C.M. Kouwenhoven
- Department of Neurology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Simona Migliozzi
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
| | | | - Johanna M. Niers
- Department of Neurology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - D. Ryan Ormond
- University of Colorado School of Medicine, Department of Neurosurgery, Aurora, Colorado
| | - Sun Ha Paek
- Department of Neurosurgery, Cancer Research Institute, Hypoxia Ischemia Disease Institute, Seoul National University, Seoul, Republic of Korea (South)
| | - Guido Reifenberger
- Institute of Neuropathology, Heinrich Heine University, Dusseldorf, Germany
| | - Peter A. Sillevis Smitt
- Department of Neurology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- The Brain Tumour Centre, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Lucy F. Stead
- Leeds Institute of Medical Research, University of Leeds, Leeds, United Kingdom
| | - Martin J. van den Bent
- Department of Neurology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- The Brain Tumour Centre, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Erwin G. Van Meir
- Department of Neurosurgery and O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, Alabama
| | | | - Tobias Weiss
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Bart A. Westerman
- Department of Neurology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Bauke Ylstra
- Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Pieter Wesseling
- Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Brain Tumor Center Amsterdam, Cancer Center Amsterdam, Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands
- Laboratory for Childhood Cancer Pathology, Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
| | - Anna Lasorella
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
- Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, Florida
| | - Pim J. French
- Neurology Department, The Brain Tumour Center, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Laila M. Poisson
- Hermelin Brain Tumor Center, Henry Ford Health System, Detroit, Michigan
| | - Roel G.W. Verhaak
- Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut
- Department of Neurosurgery, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Antonio Iavarone
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida
| | - Houtan Noushmehr
- Hermelin Brain Tumor Center, Henry Ford Health System, Detroit, Michigan
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An Y, Wang Q, Zhang L, Sun F, Zhang G, Dong H, Li Y, Peng Y, Li H, Zhu W, Ji S, Wang Y, Guo X. OSlgg: An Online Prognostic Biomarker Analysis Tool for Low-Grade Glioma. Front Oncol 2020; 10:1097. [PMID: 32775301 PMCID: PMC7381343 DOI: 10.3389/fonc.2020.01097] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 06/02/2020] [Indexed: 12/28/2022] Open
Abstract
Glioma is the most frequent primary brain tumor that causes high mortality and morbidity with poor prognosis. There are four grades of gliomas, I to IV, among which grade II and III are low-grade glioma (LGG). Although less aggressive, LGG almost universally progresses to high-grade glioma and eventual causes death if lacking of intervention. Current LGG treatment mainly depends on surgical resection followed by radiotherapy and chemotherapy, but the survival rates of LGG patients are low. Therefore, it is necessary to use prognostic biomarkers to classify patients into subgroups with different risks and guide clinical managements. Using gene expression profiling and long-term follow-up data, we established an Online consensus Survival analysis tool for LGG named OSlgg. OSlgg is comprised of 720 LGG cases from two independent cohorts. To evaluate the prognostic potency of genes, OSlgg employs the Kaplan-Meier plot with hazard ratio and p value to assess the prognostic significance of genes of interest. The reliability of OSlgg was verified by analyzing 86 previously published prognostic biomarkers of LGG. Using OSlgg, we discovered two novel potential prognostic biomarkers (CD302 and FABP5) of LGG, and patients with the elevated expression of either CD302 or FABP5 present the unfavorable survival outcome. These two genes may be novel risk predictors for LGG patients after further validation. OSlgg is public and free to the users at http://bioinfo.henu.edu.cn/LGG/LGGList.jsp.
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Affiliation(s)
- Yang An
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Qiang Wang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Lu Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Fengjie Sun
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Guosen Zhang
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Huan Dong
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Yingkun Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Yanyu Peng
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Haojie Li
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Wan Zhu
- Department of Anesthesia, Stanford University, Stanford, CA, United States
| | - Shaoping Ji
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
| | - Yunlong Wang
- Henan Bioengineering Research Center, Zhengzhou, China
| | - Xiangqian Guo
- Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, School of Basic Medical Sciences, School of Software, Henan University, Kaifeng, China
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Quiñones-Hinojosa A, Sanai N, Smith JS, McDermott MW. Techniques to assess the proliferative potential of brain tumors. J Neurooncol 2005; 74:19-30. [PMID: 16078103 DOI: 10.1007/s11060-004-5758-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Assessment of brain tumor proliferative potential provides important prognostic information that supplements standard histopathologic grading. Many laboratories rely on mitotic figures to quantify the proliferative potential of brain tumors, but this conventional cellular proliferative index is subject to inter-observer variability and not consistently predictive for low-and high-grade tumors. Recent advancements in technology have made it possible to use proliferative indices as a standard supplement in pathology laboratories. Non-invasive tumor tissue measurements of cell proliferation can be performed using- bromodeoxyuridine labeling index (BrdU LI), flow cytometry (FCM), MIB-1 antibody to the Ki-67 antigen (MIB-1), proliferating cell nuclear antigen (PCNA), and argyrophilic nucleolar organizing regions (AgNOR). Each of these assays has been described in the literature with respect to its ability to predict tumor grade or outcome. At the present time MIB-1 and AgNOR are the simplest and most reliable of these techniques. In addition, advances in our understanding of the genetic alterations associated with proliferation promise to provide more specific markers of proliferative potential. Beyond the pathology laboratory, radiographic studies such as positron emission tomography (PET), single photon emission computed tomography (SPECT), and most recently magnetic resonance spectroscopy (MRS) have been used as follow-up measures, assessing response to treatment and tumor recurrence, rather than as predictors of response to treatment. These radiographic tools, however, have the potential to provide an assessment of tumor proliferation without the need for invasive measures. In this article, we present a review of the current techniques utilized to understand the proliferative potential of brain tumors.
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Affiliation(s)
- Alfredo Quiñones-Hinojosa
- Department of Neurological Surgery, and Brain Tumor Research Center, University of California, San Francisco, 505 Parnassus Avenue, Moffitt Hospital Room M779, Box 0112, San Francisco, CA 94143-0112, USA.
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