scholarly journals P01.075 Understanding cognitive functioning in diffuse glioma patients: the relevance of IDH mutation status and functional connectivity

2018 ◽  
Vol 20 (suppl_3) ◽  
pp. iii247-iii247 ◽  
Author(s):  
J Derks ◽  
S Kulik ◽  
P Wesseling ◽  
T Numan ◽  
A Hillebrand ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Functioning ◽  
Diffuse Glioma ◽  
Idh Mutation ◽  
Mutation Status

scholarly journals Understanding cognitive functioning in glioma patients: The relevance of IDH‐mutation status and functional connectivity

2019 ◽  
Vol 9 (4) ◽  
pp. e01204 ◽  
Author(s):  
Jolanda Derks ◽  
Shanna Kulik ◽  
Pieter Wesseling ◽  
Tianne Numan ◽  
Arjan Hillebrand ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Functioning ◽  
Idh Mutation ◽  
Mutation Status

NIMG-36. VISUALIZATION OF TUMOR HETEROGENEITY AND PREDICTION OF ISOCITRATE DEHYDROGENASE MUTATION STATUS FOR HUMAN GLIOMAS BY USING MULTIPARAMETRIC PHYSIOLOGIC AND METABOLIC MRI

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi136-vi137
Author(s):  
Akifumi Hagiwara ◽  
Hiroyuki Tatekawa ◽  
Yao Jingwen ◽  
Catalina Raymond ◽  
Richard Everson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity And Specificity ◽  
Isocitrate Dehydrogenase ◽  
Glucose Transporter ◽  
Support Vector ◽  
Diffuse Glioma ◽  
Idh Mutation ◽  
Self Organizing Map ◽  
Mutation Status ◽  
Isocitrate Dehydrogenase Mutation

Abstract Preoperative prediction of isocitrate dehydrogenase mutation status is clinically meaningful, but remains challenging. This study aimed to predict the isocitrate dehydrogenase (IDH) status of gliomas by using the machine learning voxel-wise clustering method of multiparametric physiologic and metabolic magnetic resonance imaging (MRI) and to show the association of the created cluster labels with the glucose metabolism status of the tumors. Sixty-nine patients with diffuse glioma were scanned by pH-sensitive MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach, including the generation of a self-organizing map followed by the K-means clustering, was used for voxel-wise feature extraction from the acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH mutation status. Bootstrapping and leave-one-out cross-validation were used to calculate the area under the curve (AUC) of receiver operating characteristic curves, accuracy, sensitivity, and specificity for evaluating performance. Targeted biopsies were performed for 14 patients to explore the relationship between clustered labels and the expression of key glycolytic proteins determined using immunohistochemistry. The highest prediction performance to differentiate IDH status was found for 10-class clustering, with a mean AUC, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. The tissues with labels 7 + 8 + 9 + 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. Our machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.

Intraoperative assessment of isocitrate dehydrogenase mutation status in human gliomas using desorption electrospray ionization–mass spectrometry

2020 ◽  
Vol 132 (1) ◽  
pp. 180-187 ◽  
Author(s):  
Clint M. Alfaro ◽  
Valentina Pirro ◽  
Michael F. Keating ◽  
Eyas M. Hattab ◽  
R. Graham Cooks ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
Isocitrate Dehydrogenase ◽  
Tumor Resection ◽  
Desorption Electrospray Ionization ◽  
Extent Of Resection ◽  
Ionization Mass ◽  
Idh Mutation ◽  
Mutation Status ◽  
Desi Ms

OBJECTIVEThe authors describe a rapid intraoperative ambient ionization mass spectrometry (MS) method for determining isocitrate dehydrogenase (IDH) mutation status from glioma tissue biopsies. This method offers new glioma management options and may impact extent of resection goals. Assessment of the IDH mutation is key for accurate glioma diagnosis, particularly for differentiating diffuse glioma from other neoplastic and reactive inflammatory conditions, a challenge for the standard intraoperative diagnostic consultation that relies solely on morphology.METHODSBanked glioma specimens (n = 37) were analyzed by desorption electrospray ionization–MS (DESI-MS) to develop a diagnostic method to detect the known altered oncometabolite in IDH-mutant gliomas, 2-hydroxyglutarate (2HG). The method was used intraoperatively to analyze tissue smears obtained from glioma patients undergoing resection and to rapidly diagnose IDH mutation status (< 5 minutes). Fifty-one tumor core biopsies from 25 patients (14 wild type [WT] and 11 mutant) were examined and data were analyzed using analysis of variance and receiver operating characteristic curve analysis.RESULTSThe optimized DESI-MS method discriminated between IDH-WT and IDH-mutant gliomas, with an average sensitivity and specificity of 100%. The average normalized DESI-MS 2HG signal was an order of magnitude higher in IDH-mutant glioma than in IDH-WT glioma. The DESI 2HG signal intensities correlated with independently measured 2HG concentrations (R2 = 0.98). In 1 case, an IDH1 R132H–mutant glioma was misdiagnosed as a demyelinating condition by frozen section histology during the intraoperative consultation, and no resection was performed pending the final pathology report. A second craniotomy and tumor resection was performed after the final pathology provided a diagnosis most consistent with an IDH-mutant glioblastoma. During the second craniotomy, high levels of 2HG in the tumor core biopsies were detected.CONCLUSIONSThis study demonstrates the capability to differentiate rapidly between IDH-mutant gliomas and IDH-WT conditions by DESI-MS during tumor resection. DESI-MS analysis of tissue smears is simple and can be easily integrated into the standard intraoperative pathology consultation. This approach may aid in solving differential diagnosis problems associated with low-grade gliomas and could influence intraoperative decisions regarding extent of resection, ultimately improving patient outcome. Research is ongoing to expand the patient cohort, systematically validate the DESI-MS method, and investigate the relationships between 2HG and tumor heterogeneity.

scholarly journals Classification of Brain Tumor IDH Status using MRI and Deep Learning

2019 ◽  
Author(s):  
Sahil Nalawade ◽  
Gowtham Murugesan ◽  
Maryam Vejdani-Jahromi ◽  
Ryan A. Fisicaro ◽  
Chandan Ganesh Bangalore Yogananda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Test Accuracy ◽  
Low Grade ◽  
Idh Mutation ◽  
Validation Data ◽  
Mutation Status ◽  
Radiologic Imaging ◽  
Address Data

AbstractIsocitrate dehydrogenase (IDH) mutation status is an important marker in glioma diagnosis and therapy. We propose a novel automated pipeline for predicting IDH status noninvasively using deep learning and T2-weighted (T2w) MR images with minimal preprocessing (N4 bias correction and normalization to zero mean and unit variance). T2w MRI and genomic data were obtained from The Cancer Imaging Archive dataset (TCIA) for 260 subjects (120 High grade and 140 Low grade gliomas). A fully automated 2D densely connected model was trained to classify IDH mutation status on 208 subjects and tested on another held-out set of 52 subjects, using 5-fold cross validation. Data leakage was avoided by ensuring subject separation during the slice-wise randomization. Mean classification accuracy of 90.5% was achieved for each axial slice in predicting the three classes of no tumor, IDH mutated and IDH wild-type. Test accuracy of 83.8% was achieved in predicting IDH mutation status for individual subjects on the test dataset of 52 subjects. We demonstrate a deep learning method to predict IDH mutation status using T2w MRI alone. Radiologic imaging studies using deep learning methods must address data leakage (subject duplication) in the randomization process to avoid upward bias in the reported classification accuracy.

PATH-26. EVALUATING THE DISTRIBUTION AND PROGNOSIS OF IMMUNE CELL SIGNATURES OF THE TUMOR MICRO-ENVIRONMENT IN DIFFUSE GLIOMAS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi120-vi120
Author(s):  
Bharati Mehani ◽  
Saleembhasha Asanigari ◽  
Hye-Jung Chung ◽  
Kenneth Aldape
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Immune Cell ◽  
Tumor Grade ◽  
Cell Types ◽  
Idh Mutation ◽  
Mutation Status ◽  
Cluster Analyses ◽  
Diffuse Gliomas ◽  
Cell Expression

Abstract The tumor micro-environment (TME) plays an important role in the biology of cancer, including gliomas. Single cell studies have highlighted the role of specific TME components in gliomas, and the methods to deconvolve bulk profiling data may serve to complement these studies on clinically annotated tumors. In this study, we estimated cell type proportions in 3 large glioma datasets (TCGA, CGGA-325, CGGA-693) using CIBERSORTx. Using a signature matrix comprising 22 immune cell types, we identified IDH mutation status-specific immune cell distributions and found that the proportions of 10 cell types were significantly different between IDHmut and IDHwt tumors across the 3 datasets. Looking further within IDHmut tumors, we found that monocytes were enriched in 1p/19q non-co-deleted tumors across the 3 glioma datasets, consistent with prior single cell studies. We then examined estimated gene expression among immune cell types relative to IDH mutation status and found clear separation of gene expression in 15 of 22 cell types in all 3 datasets. When we applied these 22 gene expression signatures in each tumor sample onto cluster-of-cluster analyses to identify tumor groups with distinct immune signature patterns, we found that samples were distributed largely according to the IDH status in all 3 datasets, confirming that immune cell expression is distinct based on IDH status. Among IDH-specific groups, cluster-of-cluster analyses showed that immune cell-based cluster groups had distinct survival outcomes, and that IDHwt samples were distributed significantly based on tumor grades as well as based on EGFR overexpression. Among IDHmut tumors, the distributions of tumor grade and 1p/19q co-deletion status were significantly different in the immune-based clusters in 2 of the 3 datasets examined. Overall, these results highlight the biological and clinical significance of the immune cell environment in gliomas, including distinctions based on IDH mutation status as well as prognosis within IDH-specific groups.

Intraoperative Assessment of IDH Mutation Status and Tumor Invasioni in Glioma

2021 ◽  
Vol 156 (Supplement_1) ◽  
pp. S141-S142
Author(s):  
H Brown ◽  
R Chen ◽  
R Cooks ◽  
D Garcia ◽  
K Chaichana ◽  
...  
Keyword(s):  
Unmet Need ◽  
Lipid Profiles ◽  
Ionization Mass ◽  
Idh Mutation ◽  
Fresh Tissue ◽  
Mutation Status ◽  
Ms Analysis ◽  
Idh Mutations ◽  
Desi Ms ◽  
Sensitivity Specificity

Abstract Introduction/Objective Maximizing surgical resection in gliomas, while avoiding compromising non-infiltrated tissue, is associated with survival benefit. Current methodologies are suboptimal in providing rapid, intraoperative molecular characterization of tissue. We address this unmet need by using desorption electrospray ionization mass spectrometry (DESI-MS) for the intraoperative molecular assessment of gliomas. Methods/Case Report This prospective study uses intraoperative DESI-MS analysis of fresh tissue to evaluate IDH mutations via 2-hydroxyglutarate intensity and TCP via measurement of N-acetylaspartic acid (NAA) intensity and characteristic lipid profiles in less than three minutes. Blinded review of the tissue smears by a neuropathologist is used to validate IDH mutation status and TCP estimates. Results (if a Case Study enter NA) Presently, 529 biopsies from 85 enrolled patients have been collected and analyzed at two institutions. TCP assessment based on NAA intensity in 203 biopsies at the first institution yielded sensitivity, specificity, and accuracy values of 91, 76, and 83%, whereas TCP estimates via characteristic lipid profiles yielded 76, 85, and 81%, respectively. Assessment of IDH mutation status of 71 core biopsies yielded sensitivity, specificity, and accuracy values of 89, 100, and 94%. Ongoing validation of the methodology is being performed at a second institution, where we have collected 282 biopsies from 36 patients. IDH mutation assessment of the first 15 patients indicate 100% sensitivity, specificity, and accuracy. Conclusion This study represents the first and largest study using DESI-MS for the intraoperative evaluation of IDH status and TCP measurement in gliomas. Prospectively, we propose to modify our DESI-MS system to allow estimation of IDH mutation status and TCP in surgical cavities without the need for a biopsy by placing a surgical material along the margin and transferring material from the blot to a microscope slide prior to DESI-MS analysis. We envision molecular analysis by DESI-MS as a complementary technique to histopathology capable of providing additional clinical information in near real-time.

scholarly journals Ultra-high-field sodium MRI as biomarker for tumor extent, grade and IDH mutation status in glioma patients

2020 ◽  
Vol 28 ◽  
pp. 102427
Author(s):  
Sebastian Regnery ◽  
Nicolas G.R. Behl ◽  
Tanja Platt ◽  
Nina Weinfurtner ◽  
Paul Windisch ◽  
...  
Keyword(s):  
High Field ◽  
Idh Mutation ◽  
Ultra High Field ◽  
Mutation Status ◽  
Sodium Mri ◽  
Tumor Extent

scholarly journals IDH mutation status is associated with distinct vascular gene expression signatures in lower-grade gliomas

2018 ◽  
Vol 20 (11) ◽  
pp. 1505-1516 ◽  
Author(s):  
Lei Zhang ◽  
Liqun He ◽  
Roberta Lugano ◽  
Kenney Roodakker ◽  
Michael Bergqvist ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Lower Grade ◽  
Idh Mutation ◽  
Wild Type ◽  
Who Grade ◽  
Mutation Status ◽  
Grade Ii ◽  
Grade Ii Glioma

Abstract Background Vascular gene expression patterns in lower-grade gliomas (LGGs; diffuse World Health Organization [WHO] grades II–III gliomas) have not been thoroughly investigated. The aim of this study was to molecularly characterize LGG vessels and determine if tumor isocitrate dehydrogenase (IDH) mutation status affects vascular phenotype. Methods Gene expression was analyzed using an in-house dataset derived from microdissected vessels and total tumor samples from human glioma in combination with expression data from 289 LGG samples available in the database of The Cancer Genome Atlas. Vascular protein expression was examined by immunohistochemistry in human brain tumor tissue microarrays (TMAs) representing WHO grades II–IV gliomas and nonmalignant brain samples. Regulation of gene expression was examined in primary endothelial cells in vitro. Results Gene expression analysis of WHO grade II glioma indicated an intermediate stage of vascular abnormality, less severe than that of glioblastoma vessels but distinct from normal vessels. Enhanced expression of laminin subunit alpha 4 (LAMA4) and angiopoietin 2 (ANGPT2) in WHO grade II glioma was confirmed by staining of human TMAs. IDH wild-type LGGs displayed a specific angiogenic gene expression signature, including upregulation of ANGPT2 and serpin family H (SERPINH1), connected to enhanced endothelial cell migration and matrix remodeling. Transcription factor analysis indicated increased transforming growth factor beta (TGFβ) and hypoxia signaling in IDH wild-type LGGs. A subset of genes specifically induced in IDH wild-type LGG vessels was upregulated by stimulation of endothelial cells with TGFβ2, vascular endothelial growth factor, or cobalt chloride in vitro. Conclusion IDH wild-type LGG vessels are molecularly distinct from the vasculature of IDH-mutated LGGs. TGFβ and hypoxia-related signaling pathways may be potential targets for anti-angiogenic therapy of IDH wild-type LGG.

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