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Zhu H, Li Y, Ding Y, Liu Y, Shen N, Xie Y, Yan S, Liu D, Zhang X, Li L, Zhu W. Multi-pool chemical exchange saturation transfer MRI in glioma grading, molecular subtyping and evaluating tumor proliferation. J Neurooncol 2024:10.1007/s11060-024-04729-9. [PMID: 38874844 DOI: 10.1007/s11060-024-04729-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024]
Abstract
PURPOSE To evaluate the performance of multi-pool Chemical exchange saturation transfer (CEST) MRI in prediction of glioma grade, isocitrate dehydrogenase (IDH) mutation, alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss and Ki-67 labeling index (LI), based on the fifth edition of the World Health Organization classification of central nervous system tumors (WHO CNS5). METHODS 95 patients with adult-type diffuse gliomas were analyzed. The amide, direct water saturation (DS), nuclear Overhauser enhancement (NOE), semi-solid magnetization transfer (MT) and amine signals were derived using Lorentzian fitting, and asymmetry-based amide proton transfer-weighted (APTwasym) signal was calculated. The mean value of tumor region was measured and intergroup differences were estimated using student-t test. The receiver operating curve (ROC) and area under the curve (AUC) analysis were used to evaluate the diagnostic performance of signals and their combinations. Spearman correlation analysis was performed to evaluate tumor proliferation. RESULTS The amide and DS signals were significantly higher in high-grade gliomas compared to low-grade gliomas, as well as in IDH-wildtype gliomas compared to IDH-mutant gliomas (all p < 0.001). The DS, MT and amine signals showed significantly differences between ATRX loss and retention in grade 2/3 IDH-mutant gliomas (all p < 0.05). The combination of signals showed the highest AUC in prediction of grade (0.857), IDH mutation (0.814) and ATRX loss (0.769). Additionally, the amide and DS signals were positively correlated with Ki-67 LI (both p < 0.001). CONCLUSION Multi-pool CEST MRI demonstrated good potential to predict glioma grade, IDH mutation, ATRX loss and Ki-67 LI.
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Affiliation(s)
- Hongquan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China
| | - Yuanhao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China
| | - Yuejie Ding
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China
| | - Yufei Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China
| | - Nanxi Shen
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China
| | - Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China
| | - Su Yan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China
| | - Dong Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China
| | - Xiaoxiao Zhang
- Department of Clinical, Philips Healthcare, Wuhan, China
| | - Li Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China.
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, PR China.
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Yan X, Li J, Zhang Y, Liang C, Liang P, Li T, Liu Q, Hui X. Alterations in cellular metabolism under different grades of glioma staging identified based on a multi-omics analysis strategy. Front Endocrinol (Lausanne) 2023; 14:1292944. [PMID: 38111705 PMCID: PMC10726964 DOI: 10.3389/fendo.2023.1292944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 10/30/2023] [Indexed: 12/20/2023] Open
Abstract
Glioma is a type of brain tumor closely related to abnormal cell metabolism. Firstly, multiple combinatorial sequencing studies have revealed this relationship. Genomic studies have identified gene mutations and gene expression disorders related to the development of gliomas, which affect cell metabolic pathways. In addition, transcriptome studies have revealed the genes and regulatory networks that regulate cell metabolism in glioma tissues. Metabonomics studies have shown that the metabolic pathway of glioma cells has changed, indicating their distinct energy and nutritional requirements. This paper focuses on the retrospective analysis of multiple groups combined with sequencing to analyze the changes in various metabolites during metabolism in patients with glioma. Finally, the changes in genes, regulatory networks, and metabolic pathways regulating cell metabolism in patients with glioma under different metabolic conditions were discussed. It is also proposed that multi-group metabolic analysis is expected to better understand the mechanism of abnormal metabolism of gliomas and provide more personalized methods and guidance for early diagnosis, treatment, and prognosis evaluation of gliomas.
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Affiliation(s)
- Xianlei Yan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Jinwei Li
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yang Zhang
- Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Cong Liang
- Department of Pharmacy, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Pengcheng Liang
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Tao Li
- Department of Medical Imaging, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Quan Liu
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Xuhui Hui
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Arzanforoosh F, Berman AJL, Smits M, Warnert EAH. Streamlined quantitative BOLD for detecting visual stimulus-induced changes in oxygen extraction fraction in healthy participants: toward clinical application in human glioma. MAGMA (NEW YORK, N.Y.) 2023; 36:975-984. [PMID: 37556086 PMCID: PMC10667381 DOI: 10.1007/s10334-023-01110-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 08/10/2023]
Abstract
OBJECTIVE Monitoring brain oxygenation is critical in brain tumors, as low oxygenation influences tumor growth, pathological angiogenesis, and treatment resistance. This study examined the ability of the streamlined quantitative (sq)BOLD MRI technique to detect oxygenation changes in healthy individuals, as well as its potential application in a clinical setting. METHODS We used the asymmetric spin echo (ASE) technique with FLAIR preparation, along with model-based Bayesian inference to quantify the reversible transverse relaxation rate (R2') and oxygen extraction fraction (OEF) across the brain at baseline and during visual stimulation in eight healthy participants at 3T; and two patients with glioma at rest only. RESULTS Comparing sqBOLD-derived parameters between baseline and visual stimulation revealed a decrease in OEF from 0.56 ± 0.09 at baseline to 0.54 ± 0.07 at the activated state (p = 0.04, paired t test) within a functional localizer-defined volume of interest, and a decline in R2' from 6.5 ± 1.3s-1 at baseline to 6.2 ± 1.4s-1 at the activated state (p = 0.006, paired t test) in the visual cortex. CONCLUSION The sqBOLD technique is sensitive enough to detect and quantify changes in oxygenation in the healthy brain and shows potential for integration into clinical settings to provide valuable information on oxygenation in glioma.
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Affiliation(s)
- Fatemeh Arzanforoosh
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Avery J L Berman
- Department of Physics, Carleton University, Ottawa, ON, Canada
- University of Ottawa Institute of Mental Health Research, Ottawa, ON, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Medical Delta, Delft, The Netherlands
| | - Esther A H Warnert
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
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Strowd R, Ellingson B, Raymond C, Yao J, Wen PY, Ahluwalia M, Piotrowski A, Desai A, Clarke JL, Lieberman FS, Desideri S, Nabors LB, Ye X, Grossman S. Activity of a first-in-class oral HIF2-alpha inhibitor, PT2385, in patients with first recurrence of glioblastoma. J Neurooncol 2023; 165:101-112. [PMID: 37864646 PMCID: PMC10863646 DOI: 10.1007/s11060-023-04456-7] [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: 09/05/2023] [Accepted: 09/19/2023] [Indexed: 10/23/2023]
Abstract
INTRODUCTION Hypoxia inducible factor 2-alpha (HIF2α) mediates cellular responses to hypoxia and is over-expressed in glioblastoma (GBM). PT2385 is an oral HIF2α inhibitor with in vivo activity against GBM. METHODS A two-stage single-arm open-label phase II study of adults with GBM at first recurrence following chemoradiation with measurable disease was conducted through the Adult Brain Tumor Consortium. PT2385 was administered at the phase II dose (800 mg b.i.d.). The primary outcome was objective radiographic response (ORR = complete response + partial response, CR + PR); secondary outcomes were safety, overall survival (OS), and progression free survival (PFS). Exploratory objectives included pharmacokinetics (day 15 Cmin), pharmacodynamics (erythropoietin, vascular endothelial growth factor), and pH-weighted amine- chemical exchange saturation transfer (CEST) MRI to quantify tumor acidity at baseline and explore associations with drug response. Stage 1 enrolled 24 patients with early stoppage for ≤ 1 ORR. RESULTS Of the 24 enrolled patients, median age was 62.1 (38.7-76.7) years, median KPS 80, MGMT promoter was methylated in 46% of tumors. PT2385 was well tolerated. Grade ≥ 3 drug-related adverse events were hypoxia (n = 2), hyponatremia (2), lymphopenia (1), anemia (1), and hyperglycemia (1). No objective radiographic responses were observed; median PFS was 1.8 months (95% CI 1.6-2.5) and OS was 7.7 months (95% CI 4.9-12.6). Drug exposure varied widely and did not differ by corticosteroid use (p = 0.12), antiepileptics (p = 0.09), or sex (p = 0.37). Patients with high systemic exposure had significantly longer PFS (6.7 vs 1.8 months, p = 0.009). Baseline acidity by pH-weighted CEST MRI correlated significantly with treatment duration (R2 = 0.49, p = 0.017). Non-enhancing infiltrative disease with high acidity gave rise to recurrence. CONCLUSIONS PT2385 monotherapy had limited activity in first recurrent GBM. Drug exposure was variable. Signals of activity were observed in GBM patients with high systemic exposure and acidic lesions on CEST imaging. A second-generation HIF2α inhibitor is being studied.
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Affiliation(s)
- Roy Strowd
- Wake Forest University School of Medicine, 1 Medical Center Boulevard, Winston Salem, NC, 27104, USA.
| | | | | | - Jingwen Yao
- University of California Los Angeles, Los Angeles, CA, USA
| | | | | | | | - Arati Desai
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - L Burt Nabors
- University of Alabama at Birmingham, Birmingham, AL, USA
| | - Xiaobu Ye
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins, Baltimore, MD, USA
| | - Stuart Grossman
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins, Baltimore, MD, USA
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Jiang S, Wen Z, Ahn SS, Cai K, Paech D, Eberhart CG, Zhou J. Applications of chemical exchange saturation transfer magnetic resonance imaging in identifying genetic markers in gliomas. NMR IN BIOMEDICINE 2023; 36:e4731. [PMID: 35297117 PMCID: PMC10557022 DOI: 10.1002/nbm.4731] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/07/2022] [Accepted: 03/14/2022] [Indexed: 05/23/2023]
Abstract
Chemical exchange saturation transfer (CEST) imaging is an important molecular magnetic resonance imaging technique that can image numerous low-concentration biomolecules with water-exchangeable protons (such as cellular proteins) and tissue pH. CEST, or more specially amide proton transfer-weighted imaging, has been widely used for the detection, diagnosis, and response assessment of brain tumors, and its feasibility in identifying molecular markers in gliomas has also been explored in recent years. In this paper, after briefing on the basic principles and quantification methods of CEST imaging, we review its early applications in identifying isocitrate dehydrogenase mutation status, MGMT methylation status, 1p/19q deletion status, and H3K27M mutation status in gliomas. Finally, we discuss the limitations or weaknesses in these studies.
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Affiliation(s)
- Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Kejia Cai
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Daniel Paech
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
- Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | | | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
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Cho NS, Hagiwara A, Yao J, Nathanson DA, Prins RM, Wang C, Raymond C, Desousa BR, Divakaruni A, Morrow DH, Nghiemphu PL, Lai A, Liau LM, Everson RG, Salamon N, Pope WB, Cloughesy TF, Ellingson BM. Amine-weighted chemical exchange saturation transfer magnetic resonance imaging in brain tumors. NMR IN BIOMEDICINE 2023; 36:e4785. [PMID: 35704275 DOI: 10.1002/nbm.4785] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 05/31/2022] [Accepted: 06/10/2022] [Indexed: 05/23/2023]
Abstract
Amine-weighted chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is particularly valuable as an amine- and pH-sensitive imaging technique in brain tumors, targeting the intrinsically high concentration of amino acids with exchangeable amine protons and reduced extracellular pH in brain tumors. Amine-weighted CEST MRI contrast is dependent on the glioma genotype, likely related to differences in degree of malignancy and metabolic behavior. Amine-weighted CEST MRI may provide complementary value to anatomic imaging in conventional and exploratory therapies in brain tumors, including chemoradiation, antiangiogenic therapies, and immunotherapies. Continual improvement and clinical testing of amine-weighted CEST MRI has the potential to greatly impact patients with brain tumors by understanding vulnerabilities in the tumor microenvironment that may be therapeutically exploited.
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Affiliation(s)
- Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Robert M Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Brandon R Desousa
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Ajit Divakaruni
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Danielle H Morrow
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California, USA
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- UCLA Brain Tumor Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA
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Proposal to Consider Chemical/Physical Microenvironment as a New Therapeutic Off-Target Approach. Pharmaceutics 2022; 14:pharmaceutics14102084. [PMID: 36297518 PMCID: PMC9611316 DOI: 10.3390/pharmaceutics14102084] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/22/2022] Open
Abstract
The molecular revolution could lead drug discovery from chance observation to the rational design of new classes of drugs that could simultaneously be more effective and less toxic. Unfortunately, we are witnessing some failure in this sense, and the causes of the crisis involve a wide range of epistemological and scientific aspects. In pharmacology, one key point is the crisis of the paradigm the “magic bullet”, which is to design therapies based on specific molecular targets. Drug repurposing is one of the proposed ways out of the crisis and is based on the off-target effects of known drugs. Here, we propose the microenvironment as the ideal place to direct the off-targeting of known drugs. While it has been extensively investigated in tumors, the generation of a harsh microenvironment is also a phenotype of the vast majority of chronic diseases. The hostile microenvironment, on the one hand, reduces the efficacy of both chemical and biological drugs; on the other hand, it dictates a sort of “Darwinian” selection of those cells armed to survive in such hostile conditions. This opens the way to the consideration of the microenvironment as a convenient target for pharmacological action, with a clear example in proton pump inhibitors.
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Yao J, Hagiwara A, Oughourlian TC, Wang C, Raymond C, Pope WB, Salamon N, Lai A, Ji M, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. Diagnostic and Prognostic Value of pH- and Oxygen-Sensitive Magnetic Resonance Imaging in Glioma: A Retrospective Study. Cancers (Basel) 2022; 14:2520. [PMID: 35626127 PMCID: PMC9139712 DOI: 10.3390/cancers14102520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 01/19/2023] Open
Abstract
Characterization of hypoxia and tissue acidosis could advance the understanding of glioma biology and improve patient management. In this study, we evaluated the ability of a pH- and oxygen-sensitive magnetic resonance imaging (MRI) technique to differentiate glioma genotypes, including isocitrate dehydrogenase (IDH) mutation, 1p/19q co-deletion, and epidermal growth factor receptor (EGFR) amplification, and investigated its prognostic value. A total of 159 adult glioma patients were scanned with pH- and oxygen-sensitive MRI at 3T. We quantified the pH-sensitive measure of magnetization transfer ratio asymmetry (MTRasym) and oxygen-sensitive measure of R2’ within the tumor region-of-interest. IDH mutant gliomas showed significantly lower MTRasym × R2’ (p < 0.001), which differentiated IDH mutation status with sensitivity and specificity of 90.0% and 71.9%. Within IDH mutants, 1p/19q codeletion was associated with lower tumor acidity (p < 0.0001, sensitivity 76.9%, specificity 91.3%), while IDH wild-type, EGFR-amplified gliomas were more hypoxic (R2’ p = 0.024, sensitivity 66.7%, specificity 76.9%). Both R2’ and MTRasym × R2’ were significantly associated with patient overall survival (R2’: p = 0.045; MTRasym × R2’: p = 0.002) and progression-free survival (R2’: p = 0.010; MTRasym × R2’: p < 0.0001), independent of patient age, treatment status, and IDH status. The pH- and oxygen-sensitive MRI is a clinically feasible and potentially valuable imaging technique for distinguishing glioma subtypes and providing additional prognostic value to clinical practice.
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Affiliation(s)
- Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Talia C. Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
- Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Whitney B. Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
| | - Albert Lai
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Matthew Ji
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Phioanh L. Nghiemphu
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Linda M. Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA;
| | - Timothy F. Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA
| | - Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, CA 90024, USA; (J.Y.); (A.H.); (T.C.O.); (C.W.); (C.R.)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA; (W.B.P.); (N.S.)
- UCLA Neuro-Oncology Program, University of California, Los Angeles, CA 90024, USA; (A.L.); (M.J.); (P.L.N.); (T.F.C.)
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Hagiwara A, Yao J, Raymond C, Cho NS, Everson R, Patel K, Morrow DH, Desousa BR, Mareninov S, Chun S, Nathanson DA, Yong WH, Andrei G, Divakaruni AS, Salamon N, Pope WB, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. "Aerobic glycolytic imaging" of human gliomas using combined pH-, oxygen-, and perfusion-weighted magnetic resonance imaging. Neuroimage Clin 2022; 32:102882. [PMID: 34911188 PMCID: PMC8609049 DOI: 10.1016/j.nicl.2021.102882] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/10/2021] [Accepted: 11/11/2021] [Indexed: 01/24/2023]
Abstract
Aerobic glycolytic imaging combines pH-, oxygen-, and perfusion-weighted MRI. Aerobic glycolytic imaging depicts abnormal glucose metabolism of gliomas. IDH wild-type gliomas show higher aerobic glycolytic index compared with mutants. Aerobic glycolytic index in IDH wild-type glioma is correlated with glucose uptake. Aerobic glycolytic index in IDH mutant glioma is correlated to lactate transporters.
Purpose To quantify abnormal metabolism of diffuse gliomas using “aerobic glycolytic imaging” and investigate its biological correlation. Methods All subjects underwent a pH-weighted amine chemical exchange saturation transfer spin-and-gradient-echo echoplanar imaging (CEST-SAGE-EPI) and dynamic susceptibility contrast perfusion MRI. Relative oxygen extraction fraction (rOEF) was estimated as the ratio of reversible transverse relaxation rate R2′ to normalized relative cerebral blood volume. An aerobic glycolytic index (AGI) was derived by the ratio of pH-weighted image contrast (MTRasym at 3.0 ppm) to rOEF. AGI was compared between different tumor types (N = 51, 30 IDH mutant and 21 IDH wild type). Metabolic MR parameters were correlated with 18F-FDG uptake (N = 8, IDH wild-type glioblastoma), expression of key glycolytic proteins using immunohistochemistry (N = 38 samples, 21 from IDH mutant and 17 from IDH wild type), and bioenergetics analysis on purified tumor cells (N = 7, IDH wild-type high grade). Results AGI was significantly lower in IDH mutant than wild-type gliomas (0.48 ± 0.48 vs. 0.70 ± 0.48; P = 0.03). AGI was strongly correlated with 18F-FDG uptake both in non-enhancing tumor (Spearman, ρ = 0.81; P = 0.01) and enhancing tumor (ρ = 0.81; P = 0.01). AGI was significantly correlated with glucose transporter 3 (ρ = 0.71; P = 0.004) and hexokinase 2 (ρ = 0.73; P = 0.003) in IDH wild-type glioma, and monocarboxylate transporter 1 (ρ = 0.59; P = 0.009) in IDH mutant glioma. Additionally, a significant correlation was found between AGI derived from bioenergetics analysis and that estimated from MRI (ρ = 0.79; P = 0.04). Conclusion AGI derived from molecular MRI was correlated with glucose uptake (18F-FDG and glucose transporter 3/hexokinase 2) and cellular AGI in IDH wild-type gliomas, whereas AGI in IDH mutant gliomas appeared associated with monocarboxylate transporter density.
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Affiliation(s)
- Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA; Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Richard Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kunal Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Danielle H Morrow
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Brandon R Desousa
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Sergey Mareninov
- Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Saewon Chun
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - William H Yong
- Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Gafita Andrei
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Ajit S Divakaruni
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA, USA; UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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10
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Hagiwara A, Tatekawa H, Yao J, Raymond C, Everson R, Patel K, Mareninov S, Yong WH, Salamon N, Pope WB, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. Visualization of tumor heterogeneity and prediction of isocitrate dehydrogenase mutation status for human gliomas using multiparametric physiologic and metabolic MRI. Sci Rep 2022; 12:1078. [PMID: 35058510 PMCID: PMC8776874 DOI: 10.1038/s41598-022-05077-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 01/19/2023] Open
Abstract
This study aimed to differentiate isocitrate dehydrogenase (IDH) mutation status with the voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and to discover biological underpinnings of the clusters. A total of 69 patients with treatment-naïve diffuse glioma were scanned with pH-sensitive amine chemical exchange saturation transfer MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach was used for feature extraction from acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH status. The highest performance to predict IDH mutation status was found for 10-class clustering, with a mean area under the curve, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. Targeted biopsies revealed that the tissues with labels 7-10 showed high expression levels of hypoxia-inducible factor 1-alpha, glucose transporter 3, and hexokinase 2, which are typical of IDH wild-type glioma, whereas those with labels 1 showed low expression of these proteins. In conclusion, A machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.
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Affiliation(s)
- Akifumi Hagiwara
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.258269.20000 0004 1762 2738Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Hiroyuki Tatekawa
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.261445.00000 0001 1009 6411Department of Diagnostic and Interventional Radiology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Jingwen Yao
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA USA
| | - Catalina Raymond
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Richard Everson
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Kunal Patel
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Sergey Mareninov
- grid.19006.3e0000 0000 9632 6718Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - William H. Yong
- grid.19006.3e0000 0000 9632 6718Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Noriko Salamon
- grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Whitney B. Pope
- grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Phioanh L. Nghiemphu
- grid.19006.3e0000 0000 9632 6718UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Linda M. Liau
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
| | - Timothy F. Cloughesy
- grid.19006.3e0000 0000 9632 6718UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Benjamin M. Ellingson
- grid.19006.3e0000 0000 9632 6718UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA 90024 USA ,grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718UCLA Neuro-Oncology Program, University of California, Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
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11
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Mancini L, Casagranda S, Gautier G, Peter P, Lopez B, Thorne L, McEvoy A, Miserocchi A, Samandouras G, Kitchen N, Brandner S, De Vita E, Torrealdea F, Rega M, Schmitt B, Liebig P, Sanverdi E, Golay X, Bisdas S. CEST MRI provides amide/amine surrogate biomarkers for treatment-naïve glioma sub-typing. Eur J Nucl Med Mol Imaging 2022; 49:2377-2391. [PMID: 35029738 PMCID: PMC9165287 DOI: 10.1007/s00259-022-05676-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/31/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE Accurate glioma classification affects patient management and is challenging on non- or low-enhancing gliomas. This study investigated the clinical value of different chemical exchange saturation transfer (CEST) metrics for glioma classification and assessed the diagnostic effect of the presence of abundant fluid in glioma subpopulations. METHODS Forty-five treatment-naïve glioma patients with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status received CEST MRI (B1rms = 2μT, Tsat = 3.5 s) at 3 T. Magnetization transfer ratio asymmetry and CEST metrics (amides: offset range 3-4 ppm, amines: 1.5-2.5 ppm, amide/amine ratio) were calculated with two models: 'asymmetry-based' (AB) and 'fluid-suppressed' (FS). The presence of T2/FLAIR mismatch was noted. RESULTS IDH-wild type had higher amide/amine ratio than IDH-mutant_1p/19qcodel (p < 0.022). Amide/amine ratio and amine levels differentiated IDH-wild type from IDH-mutant (p < 0.0045) and from IDH-mutant_1p/19qret (p < 0.021). IDH-mutant_1p/19qret had higher amides and amines than IDH-mutant_1p/19qcodel (p < 0.035). IDH-mutant_1p/19qret with AB/FS mismatch had higher amines than IDH-mutant_1p/19qret without AB/FS mismatch ( < 0.016). In IDH-mutant_1p/19qret, the presence of AB/FS mismatch was closely related to the presence of T2/FLAIR mismatch (p = 0.014). CONCLUSIONS CEST-derived biomarkers for amides, amines, and their ratio can help with histomolecular staging in gliomas without intense contrast enhancement. T2/FLAIR mismatch is reflected in the presence of AB/FS CEST mismatch. The AB/FS CEST mismatch identifies glioma subgroups that may have prognostic and clinical relevance.
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Affiliation(s)
- Laura Mancini
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK.
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK.
| | | | | | | | | | - Lewis Thorne
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Andrew McEvoy
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Anna Miserocchi
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - George Samandouras
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Neil Kitchen
- Department of Neurosurgery, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Sebastian Brandner
- Division of Neuropathology, UCL Queen Square Institute of Neurology, London, UK
- The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Enrico De Vita
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Francisco Torrealdea
- University College Hospital, University College of London Hospitals NHS Foundation Trust, London, UK
| | - Marilena Rega
- University College Hospital, University College of London Hospitals NHS Foundation Trust, London, UK
| | | | | | - Eser Sanverdi
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Xavier Golay
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Sotirios Bisdas
- Box65, Lysholm Department of Neuroradiology, The National Hospital for Neurology & Neurosurgery, University College London Hospitals NHS Foundation Trust, 8-11 Queen Square, London, WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
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12
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Diagnosis of Glioblastoma by Immuno-Positron Emission Tomography. Cancers (Basel) 2021; 14:cancers14010074. [PMID: 35008238 PMCID: PMC8750680 DOI: 10.3390/cancers14010074] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/16/2021] [Accepted: 12/21/2021] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Neuroimaging has transformed the way brain tumors are diagnosed and treated. Although different non-invasive modalities provide very helpful information, in some situations, they present a limited value. By merging the specificity of antibodies with the resolution, sensitivity, and quantitative capabilities of positron emission tomography (PET), “Immuno-PET” allows us to conduct the non-invasive diagnosis and monitoring of patients over time using antibody-based probes as an in vivo, integrated, quantifiable, 3D, full-body “immunohistochemistry”, like a “virtual biopsy”. This review provides and focuses on immuno-PET applications and future perspectives of this promising imaging approach for glioblastoma. Abstract Neuroimaging has transformed neuro-oncology and the way that glioblastoma is diagnosed and treated. Magnetic Resonance Imaging (MRI) is the most widely used non-invasive technique in the primary diagnosis of glioblastoma. Although MRI provides very powerful anatomical information, it has proven to be of limited value for diagnosing glioblastomas in some situations. The final diagnosis requires a brain biopsy that may not depict the high intratumoral heterogeneity present in this tumor type. The revolution in “cancer-omics” is transforming the molecular classification of gliomas. However, many of the clinically relevant alterations revealed by these studies have not yet been integrated into the clinical management of patients, in part due to the lack of non-invasive biomarker-based imaging tools. An innovative option for biomarker identification in vivo is termed “immunotargeted imaging”. By merging the high target specificity of antibodies with the high spatial resolution, sensitivity, and quantitative capabilities of positron emission tomography (PET), “Immuno-PET” allows us to conduct the non-invasive diagnosis and monitoring of patients over time using antibody-based probes as an in vivo, integrated, quantifiable, 3D, full-body “immunohistochemistry” in patients. This review provides the state of the art of immuno-PET applications and future perspectives on this imaging approach for glioblastoma.
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13
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Fathi Kazerooni A, Bagley SJ, Akbari H, Saxena S, Bagheri S, Guo J, Chawla S, Nabavizadeh A, Mohan S, Bakas S, Davatzikos C, Nasrallah MP. Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. Cancers (Basel) 2021; 13:cancers13235921. [PMID: 34885031 PMCID: PMC8656630 DOI: 10.3390/cancers13235921] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary Radiomics and radiogenomics offer new insight into high-grade glioma biology, as well as into glioma behavior in response to standard therapies. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the role of radiomics in providing more accurate diagnoses, prognostication, and surveillance of patients with high-grade glioma, and on the potential application of radiomics in clinical practice, with the overarching goal of advancing precision medicine for optimal patient care. Abstract Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of high-grade gliomas, through radiomics and radiogenomics. This has raised hopes for characterizing noninvasive and in vivo biomarkers for prediction of patient survival, tumor recurrence, and genomics and therefore encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans may allow prediction of the loci of future tumor recurrence and thereby aid in planning the course of treatment for the patients, such as optimizing the extent of resection and the dose and target area of radiation. Imaging signatures of tumor genomics can help in identifying the patients who benefit from certain targeted therapies. Specifying molecular properties of gliomas and prediction of their changes over time and with treatment would allow optimization of treatment. In this article, we provide neuro-oncology, neuropathology, and computational perspectives on the promise of radiomics and radiogenomics for allowing personalized treatments of patients with gliomas and discuss the challenges and limitations of these methods in multi-institutional clinical trials and suggestions to mitigate the issues and the future directions.
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Affiliation(s)
- Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Stephen J. Bagley
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Sanjay Saxena
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Sina Bagheri
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Jun Guo
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Ali Nabavizadeh
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA; (A.F.K.); (H.A.); (S.S.); (J.G.); (A.N.); (S.M.); (S.B.); (C.D.)
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; (S.B.); (S.C.)
| | - MacLean P. Nasrallah
- Glioblastoma Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence:
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14
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Ellingson BM, Wen PY, Cloughesy TF. Therapeutic Response Assessment of High-Grade Gliomas During Early-Phase Drug Development in the Era of Molecular and Immunotherapies. Cancer J 2021; 27:395-403. [PMID: 34570454 PMCID: PMC8480435 DOI: 10.1097/ppo.0000000000000543] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
ABSTRACT Several new therapeutic strategies have emerged over the past decades to address unmet clinical needs in high-grade gliomas, including targeted molecular agents and various forms of immunotherapy. Each of these strategies requires addressing fundamental questions, depending on the stage of drug development, including ensuring drug penetration into the brain, engagement of the drug with the desired target, biologic effects downstream from the target including metabolic and/or physiologic changes, and identifying evidence of clinical activity that could be expanded upon to increase the likelihood of a meaningful survival benefit. The current review article highlights these strategies and outlines how imaging technology can be used for therapeutic response evaluation in both targeted and immunotherapies in early phases of drug development in high-grade gliomas.
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Affiliation(s)
- Benjamin M. Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Patrick Y. Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard University, Boston, MA
| | - Timothy F. Cloughesy
- UCLA Neuro Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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15
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Akbari H, Kazerooni AF, Ware JB, Mamourian E, Anderson H, Guiry S, Sako C, Raymond C, Yao J, Brem S, O'Rourke DM, Desai AS, Bagley SJ, Ellingson BM, Davatzikos C, Nabavizadeh A. Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging. Sci Rep 2021; 11:15011. [PMID: 34294864 PMCID: PMC8298590 DOI: 10.1038/s41598-021-94560-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 06/28/2021] [Indexed: 11/22/2022] Open
Abstract
Glioblastoma (GBM) has high metabolic demands, which can lead to acidification of the tumor microenvironment. We hypothesize that a machine learning model built on temporal principal component analysis (PCA) of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI can be used to estimate tumor acidity in GBM, as estimated by pH-sensitive amine chemical exchange saturation transfer echo-planar imaging (CEST-EPI). We analyzed 78 MRI scans in 32 treatment naïve and post-treatment GBM patients. All patients were imaged with DSC-MRI, and pH-weighting that was quantified from CEST-EPI estimation of the magnetization transfer ratio asymmetry (MTRasym) at 3 ppm. Enhancing tumor (ET), non-enhancing core (NC), and peritumoral T2 hyperintensity (namely, edema, ED) were used to extract principal components (PCs) and to build support vector machines regression (SVR) models to predict MTRasym values using PCs. Our predicted map correlated with MTRasym values with Spearman's r equal to 0.66, 0.47, 0.67, 0.71, in NC, ET, ED, and overall, respectively (p < 0.006). The results of this study demonstrates that PCA analysis of DSC imaging data can provide information about tumor pH in GBM patients, with the strongest association within the peritumoral regions.
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Affiliation(s)
- Hamed Akbari
- Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey B Ware
- Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hannah Anderson
- Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, USA
| | - Samantha Guiry
- Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Donald M O'Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arati S Desai
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen J Bagley
- Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ali Nabavizadeh
- Department of Radiology, Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, USA.
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16
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Suzuki K, Kawai N, Ogawa T, Miyake K, Shinomiya A, Yamamoto Y, Nishiyama Y, Tamiya T. Hypoxia and glucose metabolism assessed by FMISO and FDG PET for predicting IDH1 mutation and 1p/19q codeletion status in newly diagnosed malignant gliomas. EJNMMI Res 2021; 11:67. [PMID: 34291337 PMCID: PMC8295439 DOI: 10.1186/s13550-021-00806-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 06/24/2021] [Indexed: 11/22/2022] Open
Abstract
Background Tumor hypoxia and glycolysis have been recognized as determinant factors characterizing tumor aggressiveness in malignant gliomas. To clarify in vivo hypoxia and glucose metabolism in relation to isocitrate dehydrogenase (IDH) mutation and chromosome 1p and 19q (1p/19q) codeletion status, we retrospectively analyzed hypoxia as assessed by positron emission tomography (PET) with [18F]-fluoromisonidazole (FMISO) and glucose metabolism as assessed by PET with [18F]-fluoro-2-deoxy-d-glucose (FDG) in newly diagnosed malignant gliomas. Methods In total, 87 patients with newly diagnosed supratentorial malignant (WHO grade III and IV) gliomas were enrolled in this study. They underwent PET studies with FMISO and FDG before surgery. The molecular features and histopathological diagnoses based on the 2016 WHO classification were determined using surgical specimens. Maximal tumor-to-normal ratio (TNR) was calculated for FDG PET, and maximal tumor-to-blood SUV ratio (TBR) was calculated for FMISO PET. The PET uptake values in relation to IDH mutation and 1p/19q codeletion status were statistically analyzed. Results In all tumors and malignant astrocytomas, the median FMISO TBR in IDH-wildtype tumors was significantly higher than that in IDH-mutant tumors (P < 0.001 and P < 0.01, respectively). In receiver operating characteristic (ROC) analysis, the area under the curve showed that the sensitivity for the discrimination was moderate (0.7–0.8) and the specificity was low (0.65–0.68). In the same population, the median FDG TNR in IDH-wildtype tumors tended to be higher than that in IDH-mutant tumors, but the difference was not statistically significant. In WHO grade III anaplastic astrocytomas, there were no significant differences in median FMISO TBR or FDG TNR between IDH-mutant and IDH-wildtype tumors. In IDH-mutant WHO grade III anaplastic gliomas, there were no significant differences in median FMISO TBR or FDG TNR between anaplastic astrocytomas and anaplastic oligodendrogliomas. Conclusions Tumor hypoxia as assessed by FMISO PET was informative for prediction of the IDH mutation status in newly diagnosed malignant gliomas. However, the accuracy of the discrimination was not satisfactory for clinical application. On the other hand, glucose metabolism as assessed by FDG PET could not differentiate the IDH-mutant status. Moreover, PET studies using FMISO and FDG could not predict IDH mutation and 1p/19q codeletion status in WHO grade III tumors.
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Affiliation(s)
- Kenta Suzuki
- Department of Neurological Surgery, Faculty of Medicine, Kagawa University, Miki-cho, Kagawa, Japan
| | - Nobuyuki Kawai
- Department of Neurological Surgery, Kagawa Rehabilitation Hospital, 1114 Tamura-cho, Takamatsu-shi, Kagawa, 761-8057, Japan.
| | - Tomoya Ogawa
- Department of Neurological Surgery, Faculty of Medicine, Kagawa University, Miki-cho, Kagawa, Japan
| | - Keisuke Miyake
- Department of Neurological Surgery, Faculty of Medicine, Kagawa University, Miki-cho, Kagawa, Japan
| | - Aya Shinomiya
- Department of Neurological Surgery, Faculty of Medicine, Kagawa University, Miki-cho, Kagawa, Japan
| | - Yuka Yamamoto
- Department of Diagnostic Radiology, Faculty of Medicine, Kagawa University, Miki-cho, Kagawa, Japan
| | - Yoshihiro Nishiyama
- Department of Diagnostic Radiology, Faculty of Medicine, Kagawa University, Miki-cho, Kagawa, Japan
| | - Takashi Tamiya
- Department of Neurological Surgery, Faculty of Medicine, Kagawa University, Miki-cho, Kagawa, Japan
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17
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The Acidic Brain-Glycolytic Switch in the Microenvironment of Malignant Glioma. Int J Mol Sci 2021; 22:ijms22115518. [PMID: 34073734 PMCID: PMC8197239 DOI: 10.3390/ijms22115518] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 05/16/2021] [Accepted: 05/19/2021] [Indexed: 12/15/2022] Open
Abstract
Malignant glioma represents a fatal disease with a poor prognosis and development of resistance mechanisms against conventional therapeutic approaches. The distinct tumor zones of this heterogeneous neoplasm develop their own microenvironment, in which subpopulations of cancer cells communicate. Adaptation to hypoxia in the center of the expanding tumor mass leads to the glycolytic and angiogenic switch, accompanied by upregulation of different glycolytic enzymes, transporters, and other metabolites. These processes render the tumor microenvironment more acidic, remodel the extracellular matrix, and create energy gradients for the metabolic communication between different cancer cells in distinct tumor zones. Escape mechanisms from hypoxia-induced cell death and energy deprivation are the result. The functional consequences are more aggressive and malignant behavior with enhanced proliferation and survival, migration and invasiveness, and the induction of angiogenesis. In this review, we go from the biochemical principles of aerobic and anaerobic glycolysis over the glycolytic switch, regulated by the key transcription factor hypoxia-inducible factor (HIF)-1α, to other important metabolic players like the monocarboxylate transporters (MCTs)1 and 4. We discuss the metabolic symbiosis model via lactate shuttling in the acidic tumor microenvironment and highlight the functional consequences of the glycolytic switch on glioma malignancy. Furthermore, we illustrate regulation by micro ribonucleic acids (miRNAs) and the connection between isocitrate dehydrogenase (IDH) mutation status and glycolytic metabolism. Finally, we give an outlook about the diagnostic and therapeutic implications of the glycolytic switch and the relation to tumor immunity in malignant glioma.
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18
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Yao J, Wang C, Raymond C, Bergstrom B, Chen X, Das K, Dinh H, Kim ZS, Le AN, Lim MWJ, Pham JAN, Prusan JD, Rao SS, Nathanson DA, Ellingson BM. A physical phantom for amine chemical exchange saturation transfer (CEST) MRI. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 34:569-580. [PMID: 33484366 DOI: 10.1007/s10334-020-00902-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 11/13/2020] [Accepted: 12/09/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To develop a robust amine chemical exchange saturation transfer (CEST) physical phantom, validate the temporal stability, and create a supporting software for automatic image processing and quality assurance. MATERIALS AND METHODS The phantom was designed as an assembled laser-cut acrylic rack and 18 vials of phantom solutions, prepared with different pHs, glycine concentrations, and gadolinium concentrations. We evaluated glycine concentrations using ultraviolet absorbance for 70 days and measured the pH, relaxation rates, and CEST contrast for 94 days after preparation. We used Spearman's correlation to determine if glycine degraded over time. Linear regression and Bland-Altman analysis were performed between baseline and follow-up measurements of pH and MRI properties. RESULTS No degradation of glycine was observed (p > 0.05). The pH and MRI measurements stayed stable for 3 months and showed high consistency across time points (R2 = 1.00 for pH, R1, R2, and CEST contrast), which was further validated by the Bland-Altman plots. Examples of automatically generated reports are provided. DISCUSSION We designed a physical phantom for amine CEST-MRI, which is easy to assemble and transfer, holds 18 different solutions, and has excellent short-term chemical and MRI stability. We believe this robust phantom will facilitate the development of novel sequences and cross-scanners validations.
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Affiliation(s)
- Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Departments of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Departments of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Departments of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Blake Bergstrom
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Xing Chen
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kaveri Das
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering Innovation and Design, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Huy Dinh
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Zoe S Kim
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Angela N Le
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Matthew W J Lim
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jane A N Pham
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Joseph D Prusan
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sriram S Rao
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Departments of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
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19
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Miyake K, Suzuki K, Ogawa T, Ogawa D, Hatakeyama T, Shinomiya A, Kudomi N, Yamamoto Y, Nishiyama Y, Tamiya T. Multiple positron emission tomography tracers for use in the classification of gliomas according to the 2016 World Health Organization criteria. Neurooncol Adv 2020; 3:vdaa172. [PMID: 33681765 PMCID: PMC7920529 DOI: 10.1093/noajnl/vdaa172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background The molecular diagnosis of gliomas such as isocitrate dehydrogenase (IDH) status (wild-type [wt] or mutation [mut]) is especially important in the 2016 World Health Organization (WHO) classification. Positron emission tomography (PET) has afforded molecular and metabolic diagnostic imaging. The present study aimed to define the interrelationship between the 2016 WHO classification of gliomas and the integrated data from PET images using multiple tracers, including 18F-fluorodeoxyglucose (18F-FDG), 11C-methionine (11C-MET), 18F-fluorothymidine (18F-FLT), and 18F-fluoromisonidazole (18F-FMISO). Methods This retrospective, single-center study comprised 113 patients with newly diagnosed glioma based on the 2016 WHO criteria. Patients were divided into 4 glioma subtypes (Mut, Codel, Wt, and glioblastoma multiforme [GBM]). Tumor standardized uptake value (SUV) divided by mean normal cortical SUV (tumor–normal tissue ratio [TNR]) was calculated for 18F-FDG, 11C-MET, and 18F-FLT. Tumor–blood SUV ratio (TBR) was calculated for 18F-FMISO. To assess the diagnostic accuracy of PET tracers in distinguishing glioma subtypes, a comparative analysis of TNRs and TBR as well as the metabolic tumor volume (MTV) were calculated by Scheffe's multiple comparison procedure for each PET tracer following the Kruskal–Wallis test. Results The differences in mean 18F-FLT TNR and 18F-FMISO TBR were significant between GBM and other glioma subtypes (P < .001). Regarding the comparison between Gd-T1WI volumes and 18F-FLT MTVs or 18F-FMISO MTVs, we identified significant differences between Wt and Mut or Codel (P < .01). Conclusion Combined administration of 4 PET tracers might aid in the preoperative differential diagnosis of gliomas according to the 2016 WHO criteria.
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Affiliation(s)
- Keisuke Miyake
- Department of Neurological Surgery, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
| | - Kenta Suzuki
- Department of Neurological Surgery, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
| | - Tomoya Ogawa
- Department of Neurological Surgery, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
| | - Daisuke Ogawa
- Department of Neurological Surgery, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
| | - Tetsuhiro Hatakeyama
- Department of Neurological Surgery, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
| | - Aya Shinomiya
- Department of Neurological Surgery, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
| | - Nobuyuki Kudomi
- Department of Medical Physics, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
| | - Yuka Yamamoto
- Department of Radiology, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
| | - Yoshihiro Nishiyama
- Department of Radiology, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
| | - Takashi Tamiya
- Department of Neurological Surgery, Kagawa University, Faculty of Medicine, Ikenobe, Miki-Cho, Kita-gun, Kagawa, Japan
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20
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Hub gene identification and prognostic model construction for isocitrate dehydrogenase mutation in glioma. Transl Oncol 2020; 14:100979. [PMID: 33290989 PMCID: PMC7720094 DOI: 10.1016/j.tranon.2020.100979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 11/09/2020] [Accepted: 11/25/2020] [Indexed: 12/13/2022] Open
Abstract
We identified ten hub genes which were driving IDH status in GBM and LGG. We constructed a prognostic model for IDH-mutant patients. Our findings have important clinical implications for accurate treatment in glioma.
Our study attempted to identify hub genes related to isocitrate dehydrogenase (IDH) mutation in glioma and develop a prognostic model for IDH-mutant glioma patients. In a first step, ten hub genes significantly associated with the IDH status were identified by weighted gene coexpression analysis (WGCNA). The functional enrichment analysis demonstrated that the most enriched terms of these hub genes were cadherin binding and glutathione metabolism. Three of these hub genes were significantly linked with the survival of glioma patients. 328 samples of IDH-mutant glioma were separated into two datasets: a training set (N = 228) and a test set (N = 100). Based on the training set, we identified two IDH-mutant subtypes with significantly different pathological features by using consensus clustering. A 31 gene-signature was identified by the least absolute shrinkage and selection operator (LASSO) algorithm and used for establishing a differential prognostic model for IDH-mutant patients. In addition, the test set was employed for validating the prognostic model, and the model was proven to be of high value in classifying prognostic information of samples. The functional annotation revealed that the genes related to the model were mainly enriched in nuclear division, DNA replication, and cell cycle. Collectively, this study provided novel insights into the molecular mechanism of IDH mutation in glioma, and constructed a prognostic model which can be effective for predicting prognosis of glioma patients with IDH-mutation, which might promote the development of IDH target agents in glioma therapies and contribute to accurate prognostication and management in IDH-mutant glioma patients.
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21
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Oughourlian TC, Yao J, Hagiwara A, Nathanson DA, Raymond C, Pope WB, Salamon N, Lai A, Ji M, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. Relative oxygen extraction fraction (rOEF) MR imaging reveals higher hypoxia in human epidermal growth factor receptor (EGFR) amplified compared with non-amplified gliomas. Neuroradiology 2020; 63:857-868. [PMID: 33106922 DOI: 10.1007/s00234-020-02585-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 10/13/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE Epidermal growth factor receptor (EGFR) amplification promotes gliomagenesis and is linked to lack of oxygen within the tumor microenvironment. Using hypoxia-sensitive spin-and-gradient echo echo-planar imaging and perfusion MRI, we investigated the influence of EGFR amplification on tissue oxygen availability and utilization in human gliomas. METHODS This study included 72 histologically confirmed EGFR-amplified and non-amplified glioma patients. Reversible transverse relaxation rate (R2'), relative cerebral blood volume (rCBV), and relative oxygen extraction fraction (rOEF) were calculated for the contrast-enhancing and non-enhancing tumor regions. Using Student t test or Wilcoxon rank-sum test, median R2', rCBV, and rOEF were compared between EGFR-amplified and non-amplified gliomas. ROC analysis was performed to assess the ability of imaging characteristics to discriminate EGFR amplification status. Overall survival (OS) was determined using univariate and multivariate cox models. Kaplan-Meier survival curves were plotted and compared using the log-rank test. RESULTS EGFR amplified gliomas exhibited significantly higher median R2' and rOEF than non-amplified gliomas. ROC analysis suggested that R2' (AUC = 0.7190; P = 0.0048) and rOEF (AUC = 0.6959; P = 0.0156) could separate EGFR status. Patients with EGFR-amplified gliomas had a significantly shorter OS than non-amplified patients. Univariate cox regression analysis determined both R2' and rOEF significantly influence OS. No significant difference was observed in rCBV between patient cohorts nor was rCBV found to be an effective differentiator of EGFR status. CONCLUSION Imaging of tumor oxygen characteristics revealed EGFR-amplified gliomas to be more hypoxic and contribute to shorter patient survival than EGFR non-amplified gliomas.
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Affiliation(s)
- Talia C Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Albert Lai
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Matthew Ji
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. .,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
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22
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Human IDH mutant 1p/19q co-deleted gliomas have low tumor acidity as evidenced by molecular MRI and PET: a retrospective study. Sci Rep 2020; 10:11922. [PMID: 32681084 PMCID: PMC7367867 DOI: 10.1038/s41598-020-68733-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 06/01/2020] [Indexed: 01/19/2023] Open
Abstract
Co-deletion of 1p/19q is a hallmark of oligodendroglioma and predicts better survival. However, little is understood about its metabolic characteristics. In this study, we aimed to explore the extracellular acidity of WHO grade II and III gliomas associated with 1p/19q co-deletion. We included 76 glioma patients who received amine chemical exchange saturation transfer (CEST) imaging at 3 T. Magnetic transfer ratio asymmetry (MTRasym) at 3.0 ppm was used as the pH-sensitive CEST biomarker, with higher MTRasym indicating lower pH. To control for the confounder factors, T2 relaxometry and l-6-18F-fluoro-3,4-dihydroxyphenylalnine (18F-FDOPA) PET data were collected in a subset of patients. We found a significantly lower MTRasym in 1p/19q co-deleted gliomas (co-deleted, 1.17% ± 0.32%; non-co-deleted, 1.72% ± 0.41%, P = 1.13 × 10−7), while FDOPA (P = 0.92) and T2 (P = 0.61) were not significantly affected. Receiver operating characteristic analysis confirmed that MTRasym could discriminate co-deletion status with an area under the curve of 0.85. In analysis of covariance, 1p/19q co-deletion status was the only significant contributor to the variability in MTRasym when controlling for age and FDOPA (P = 2.91 × 10−3) or T2 (P = 8.03 × 10−6). In conclusion, 1p/19q co-deleted gliomas were less acidic, which may be related to better prognosis. Amine CEST-MRI may serve as a non-invasive biomarker for identifying 1p/19q co-deletion status.
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Kaufmann TJ. A new study in contrasts: brain MRI for the depiction of tumor metabolism. Neuro Oncol 2019; 21:1095-1096. [PMID: 31271202 PMCID: PMC7594574 DOI: 10.1093/neuonc/noz121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023] Open
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