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Dono A, Amsbaugh M, Martir M, Smilie RH, Riascos RF, Zhu JJ, Hsu S, Kim DH, Tandon N, Ballester LY, Blanco AI, Esquenazi Y. Genomic alterations predictive of response to radiosurgery in recurrent IDH-WT glioblastoma. J Neurooncol 2021; 152:153-162. [PMID: 33492602 PMCID: PMC8354320 DOI: 10.1007/s11060-020-03689-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 12/26/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Despite aggressive treatment, glioblastoma invariably recurs. The optimal treatment for recurrent glioblastoma (rGBM) is not well defined. Stereotactic radiosurgery (SRS) for rGBM has demonstrated favorable outcomes for selected patients; however, its efficacy in molecular GBM subtypes is unknown. We sought to identify genetic alterations that predict response/outcomes from SRS in rGBM-IDH-wild-type (IDH-WT). METHODS rGBM-IDH-WT patients undergoing SRS at first recurrence and tested by next-generation sequencing (NGS) were reviewed (2009-2018). Demographic, clinical, and molecular characteristics were evaluated. NGS interrogating 205-genes was performed. Primary outcome was survival from GK-SRS assessed by Kaplan-Meier method and multivariable Cox proportional-hazards. RESULTS Sixty-three lesions (43-patients) were treated at 1st recurrence. Median age was 61-years. All patients were treated with resection and chemoradiotherapy. Median time from diagnosis to 1st recurrence was 8.7-months. Median cumulative volume was 2.895 cm3 and SRS median marginal dose was 18 Gy (median isodose-54%). Bevacizumab was administered in 81.4% patients. PFS from SRS was 12.9-months. Survival from SRS was 18.2-months. PTEN-mutant patients had a longer PFS (p = 0.049) and survival from SRS (p = 0.013) in multivariable analysis. Although no statistically significant PTEN-mutants patients had higher frequency of radiation necrosis (21.4% vs. 3.4%) and lower in-field recurrence (28.6% vs. 37.9%) compared to PTEN-WT patients. CONCLUSIONS SRS is a safe and effective treatment option for selected rGBM-IDH-WT patients following first recurrence. rGBM-IDH-WT harboring PTEN-mutation have improved survival with salvage SRS compared to PTEN-WT patients. PTEN may be used as a molecular biomarker to identify a subset of rGBM patients who may benefit the most from SRS.
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Affiliation(s)
- Antonio Dono
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Pathology and Laboratory Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Mark Amsbaugh
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Memorial Hermann Hospital-TMC, Houston, TX, USA
| | - Magda Martir
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Memorial Hermann Hospital-TMC, Houston, TX, USA
| | - Richard H Smilie
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Roy F Riascos
- Memorial Hermann Hospital-TMC, Houston, TX, USA
- Department of Diagnostic and Interventional Imaging, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jay-Jiguang Zhu
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Memorial Hermann Hospital-TMC, Houston, TX, USA
| | - Sigmund Hsu
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Memorial Hermann Hospital-TMC, Houston, TX, USA
| | - Dong H Kim
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Memorial Hermann Hospital-TMC, Houston, TX, USA
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Memorial Hermann Hospital-TMC, Houston, TX, USA
| | - Leomar Y Ballester
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Pathology and Laboratory Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Memorial Hermann Hospital-TMC, Houston, TX, USA.
| | - Angel I Blanco
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Memorial Hermann Hospital-TMC, Houston, TX, USA
| | - Yoshua Esquenazi
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Memorial Hermann Hospital-TMC, Houston, TX, USA.
- Center for Precision Health, School of Biomedical Informatics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
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Yan LF, Sun YZ, Zhao SS, Hu YC, Han Y, Li G, Zhang X, Tian Q, Liu ZC, Yang Y, Nan HY, Yu Y, Sun Q, Zhang J, Chen P, Hu B, Li F, Han TH, Wang W, Cui GB. Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best? Cancer Manag Res 2019; 11:9989-10000. [PMID: 31819632 PMCID: PMC6885544 DOI: 10.2147/cmar.s197839] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 09/30/2019] [Indexed: 12/13/2022] Open
Abstract
Purpose This study aims to incorporate informative histogram indicator analyses and advanced multimodal MRI parameters to differentiate low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to explore the features associated with patients’ survival. Patients and methods A total of 120 patients with pathologically confirmed LGGs or HGGs receiving conventional and advanced MRI such as three-dimensional arterial spin labeling (3D-ASL), intravoxel incoherent motion-diffusion weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) were included. The mean and histogram indicators from advanced MRI were calculated from the entire tumor. The efficacies of a single indicator or multiple parameters were tested in distinguishing HGGs from LGGs and predicting patients’ survival. Receiver operating characteristic (ROC) curve and multivariable stepwise logistic regression were used to evaluate the diagnostic efficacies. Leave-one-out cross-validation was further used to validate the accuracy of the parameter sets in glioma grading. Log-rank test using the Kaplan–Meier curve was utilized to predict patients’ survival. Results Overall, parameters from DCE-MRI performed better than those from 3D-ASL or IVIM-DWI in both glioma grading and survival prediction. The histogram metrics of Ve were demonstrated to have higher accuracies (the accuracies for Extended Tofts_Vemean and Extended Tofts_Vemedian were 68.33% and 71.67%, respectively, while those for the Incremental_Vemean and Incremental_Ve75th were 68.33% and 72.50%, respectively) in grading LGGs from HGGs. The combination of Tofts_Ve histogram metrics was the one with the highest accuracy (81.67%) and area under ROC curve (AUC = 0.840). On the other hand, Patlak_Ktrans95th (AUC = 0.9265) and Extended Tofts_Ve95th (AUC = 0.9154) performed better than their corresponding means (Patlak_Ktransmean: AUC = 0.9118 and Extended Tofts_Vemean: AUC = 0.9044) in predicting patients’ overall survival (OS) at 18-month follow-up. Conclusion DCE-MRI-derived histogram features from the entire tumor were promising metrics for glioma grading and OS prediction. Combining single modal histogram features improved glioma grading. Trial registration NCT 02622620.
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Affiliation(s)
- Lin-Feng Yan
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Ying-Zhi Sun
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Sha-Sha Zhao
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Yu-Chuan Hu
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Yu Han
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Gang Li
- Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Xin Zhang
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Qiang Tian
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Zhi-Cheng Liu
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Yang Yang
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Hai-Yan Nan
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Ying Yu
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Qian Sun
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Jin Zhang
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Ping Chen
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Bo Hu
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Fei Li
- Student Brigade, Fourth Military Medical University, Xi'an, Shaanxi 710032, People's Republic of China
| | - Teng-Hui Han
- Student Brigade, Fourth Military Medical University, Xi'an, Shaanxi 710032, People's Republic of China
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
| | - Guang-Bin Cui
- Department of Radiology & Functional and Molecular Imaging Key Laboratory of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, People's Republic of China
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Katrib A, Jeong HH, Fransen NL, Henzel KS, Miller JA. An Inflammatory Landscape for Preoperative Neurologic Deficits in Glioblastoma. Front Genet 2019; 10:488. [PMID: 31231419 PMCID: PMC6559211 DOI: 10.3389/fgene.2019.00488] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 05/06/2019] [Indexed: 01/11/2023] Open
Abstract
Introduction: Patients with glioblastoma (GBM), one of the most aggressive forms of primary brain tumors, exhibit a wide range of neurologic signs, ranging from headaches to neurologic deficits and cognitive impairment, at first clinical presentation. While such variability is attributed to inter-individual differences in increased intracranial pressure, tumor infiltration, and vascular compromise, a direct association with disease stage, tumor size and location, edema, and necrotic cell death has yet to be established. The lack of specificity of neurologic symptoms often confounds the diagnosis of GBM. It also limits clinicians' ability to elect treatment regimens that not only prolong survival but also promote symptom management and high quality of life. Methods: To decipher the heterogeneous presentation of neurologic symptoms in GBM, we investigated differences in the molecular makeup of tumors from patients with and without preoperative neurologic deficits. We used the Ivy GAP (Ivy Glioblastoma Atlas Project) database to integrate RNA sequencing data from histologically defined GBM tumor compartments and neurologic examination records for 41 patients. We investigated the association of neurologic deficits with various tumor and patient attributes. We then performed differential gene expression and co-expression network analysis to identify a transcriptional signature specific to neurologic deficits in GBM. Using functional enrichment analysis, we finally provided a comprehensive and detailed characterization of involved pathways and gene interactions. Results: An exploratory investigation of the association of tumor and patient variables with the early development of neurologic deficits in GBM revealed a lack of robust and consistent clinicopathologic prognostic factors. We detected significant differences in the expression of 728 genes (FDR-adjusted p-value ≤ 0.05 and relative fold-change ≥ 1.5), unique to the cellular tumor (CT) anatomical compartment, between neurologic deficit groups. Upregulated differentially expressed genes in CT were enriched for mesenchymal subtype-predictive genes. Applying a systems approach, we then identified co-expressed gene sets that correlated with neurological deficit manifestation (FDR-adjusted p-value < 0.1). Collectively, these findings uncovered significantly enriched immune activation, oxidative stress response, and cytokine-mediated proinflammatory processes. Conclusion: Our study posits that inflammatory processes, as well as a mesenchymal tumor subtype, are implicated in the pathophysiology of preoperative neurologic deficits in GBM.
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Affiliation(s)
- Amal Katrib
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, United States
- Institute for Systems Biology, Seattle, WA, United States
| | - Hyun-Hwan Jeong
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX, United States
| | - Nina L. Fransen
- Department of Neuroimmunology, Netherlands Brain Bank, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
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Chambless LB, Kistka HM, Parker SL, Hassam-Malani L, McGirt MJ, Thompson RC. The relative value of postoperative versus preoperative Karnofsky Performance Scale scores as a predictor of survival after surgical resection of glioblastoma multiforme. J Neurooncol 2014; 121:359-64. [DOI: 10.1007/s11060-014-1640-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2014] [Accepted: 10/18/2014] [Indexed: 10/24/2022]
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Wang C, Yu G, Liu J, Wang J, Zhang Y, Zhang X, Zhou Z, Huang Z. Downregulation of PCDH9 predicts prognosis for patients with glioma. J Clin Neurosci 2012; 19:541-5. [PMID: 22300792 DOI: 10.1016/j.jocn.2011.04.047] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2011] [Revised: 04/12/2011] [Accepted: 04/23/2011] [Indexed: 10/14/2022]
Abstract
Recent evidence has indicated that biological markers are essential in estimating the prognosis of patients with gliomas. The aim of this study was to determine the status and clinical significance of a novel tumor suppressor, PCDH9 (protocadherin 9) in glioma using tissue microarrays and immunohistochemistry. Normal brain tissue showed strong positive immunostaining for PCDH9, but this was downregulated in the primary cerebral glial tumor samples (51.7%). Loss of PCDH9 expression was associated significantly with a higher histological grade. Survival analysis demonstrated that patients with PCDH9-negative tumors had significantly shorter survival times than those with PCDH9-positive tumors and that PCDH9 was an independent prognostic factor. Our results suggest that PCDH9 might function as a tumor suppressor during cancer development and progression and could be regarded as a useful biomarker for predicting the outcome of patients with cerebral glial tumors.
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Affiliation(s)
- Chunlin Wang
- Department of Neurosurgery, The 105th Hospital of PLA, 424 West Changjiang Road, Hefei 230000, Anhui Province, China
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Zöllner FG, Emblem KE, Schad LR. Support vector machines in DSC-based glioma imaging: suggestions for optimal characterization. Magn Reson Med 2011; 64:1230-6. [PMID: 20564592 DOI: 10.1002/mrm.22495] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Dynamic susceptibility contrast magnetic resonance perfusion imaging (DSC-MRI) is a useful method to characterize gliomas. Recently, support vector machines (SVMs) have been introduced as means to prospectively characterize new patients based on information from previous patients. Based on features derived from automatically segmented tumor volumes from 101 DSC-MR examinations, four different SVM models were compared. All SVM models achieved high prediction accuracies (>82%) after rebalancing the training data sets to equal amounts of samples per class. Best discrimination was obtained using a SVM model with a radial basis function kernel. A correct prediction of low-grade glioma was obtained at 83% (true positive rate) and for high-grade glioma at 91% (true negative rate) on the independent test data set. In conclusion, the combination of automated tumor segmentation followed by SVM classification is feasible. Thereby, a powerful tool is available to characterize glioma presurgically in patients.
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Affiliation(s)
- Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
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