Preoperative inflammation markers and IDH mutation status predict glioblastoma patient survival

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.

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Predicting IDH mutation status of lower grade gliomas using standard MR imaging features and apparent diffusion coefficient

Chinese Journal of Academic Radiology ◽

10.1007/s42058-021-00077-z ◽

2021 ◽

Author(s):

Shuangshuang Song ◽

Jiankun Xu ◽

Leiming Wang ◽

Fan Yu ◽

Yueshan Piao ◽

...

Keyword(s):

Diffusion Coefficient ◽

Mr Imaging ◽

Apparent Diffusion Coefficient ◽

Lower Grade ◽

Imaging Features ◽

Idh Mutation ◽

Mutation Status ◽

Apparent Diffusion

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A Baseline for Predicting Glioblastoma Patient Survival Time with Classical Statistical Models and Primitive Features Ignoring Image Information

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - Lecture Notes in Computer Science ◽

10.1007/978-3-030-46640-4_24 ◽

2020 ◽

pp. 254-261

Author(s):

Florian Kofler ◽

Johannes C. Paetzold ◽

Ivan Ezhov ◽

Suprosanna Shit ◽

Daniel Krahulec ◽

...

Keyword(s):

Survival Time ◽

Statistical Models ◽

Patient Survival ◽

Image Information ◽

Glioblastoma Patient ◽

Patient Survival Time

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Classification of Brain Tumor IDH Status using MRI and Deep Learning

10.1101/757344 ◽

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.

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

Neuro-Oncology ◽

10.1093/neuonc/noab196.478 ◽

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.

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Intraoperative Assessment of IDH Mutation Status and Tumor Invasioni in Glioma

American Journal of Clinical Pathology ◽

10.1093/ajcp/aqab191.302 ◽

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.

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