Isocitrate Dehydrogenase Genotype Research Articles

Identification of T2W hypointense ring as a novel noninvasive indicator for glioma grade and IDH genotype

BackgroundThis study aimed to evaluate the T2W hypointense ring and T2-FLAIR mismatch signs in gliomas and use these signs to construct prediction models for glioma grading and isocitrate dehydrogenase (IDH) mutation status.MethodsTwo independent radiologists retrospectively evaluated 207 glioma patients to assess the presence of T2W hypointense ring and T2-FLAIR mismatch signs. The inter-rater reliability was calculated using the Cohen’s kappa statistic. Two logistic regression models were constructed to differentiate glioma grade and predict IDH genotype noninvasively, respectively. Receiver operating characteristic (ROC) analysis was used to evaluate the developed models.ResultsOf the 207 patients enrolled (119 males and 88 females, mean age 51.6 ± 14.8 years), 45 cases were low-grade gliomas (LGGs), 162 were high-grade gliomas (HGGs), 55 patients had IDH mutations, and 116 were IDH wild-type. The number of T2W hypointense ring signs was higher in HGGs compared to LGGs (p < 0.001) and higher in the IDH wild-type group than in the IDH mutant group (p < 0.001). There were also significant differences in T2-FLAIR mismatch signs between HGGs and LGGs, as well as between IDH mutant and wild-type groups (p < 0.001). Two predictive models incorporating T2W hypointense ring, absence of T2-FLAIR mismatch, and age were constructed. The area under the ROC curve (AUROC) was 0.940 for predicting HGGs (95% CI = 0.907–0.972) and 0.830 for differentiating IDH wild-type (95% CI = 0.757–0.904).ConclusionsThe combination of T2W hypointense ring, absence of T2-FLAIR mismatch, and age demonstrate good predictive capability for HGGs and IDH wild-type. These findings suggest that MRI can be used noninvasively to predict glioma grading and IDH mutation status, which may have important implications for patient management and treatment planning.

Can the metabolic characteristics of diffuse glioma on &lt;sup&gt;11&lt;/sup&gt;C-methionine PET/CT serve as a marker of its IDH status? Cross sectional study

INTRODUCTION: Since 2016, molecular markers, in particular, mutations in isocitrate dehydrogenase (IDH) 1 and 2, have been introduced as a classifying feature of cerebral gliomas that provided superior prognostication. The search for non-invasive biomarkers of the molecular profile of gliomas is necessary to improve the quality of preoperative diagnostics, identify patients with good and poor prognosis and determine treatment tactics.OBJECTIVE: Was to study the relationship between the IDH genotype of diffuse cerebral gliomas and metabolic biomarkers according to the results of PET/CT with [11C]methionine.MATERIALS AND METHOD: The results of PET/CT with 11C-methionine were identified to a retrospective analysis of 260 patients aged 18 to 75 years (median 40 years) with untreated cerebral glioma. Based on histological and molecular genetic studies of the surgical material including the determination of a mutation in the isocitrate dehydrogenase 1 (IDH1132H) gene, diffuse gliomas were classified according to the 2016 WHO classification of CNS tumors. Metabolic biomarkers included the calculation of tumor-to-brain ratio of 11С-methionine (TBRmax, TBRpeak and TBRmean) as well as the metabolic tumor volume (MTV). Statistics. Non-parametric tests were performed to compare the differences among patient groups. ROC curve analysis was performed to screen the optimal parameter and its best cutoff value for the discrimination of glioma genotype. All data analyses were performed using “Statistica 10,0” and “MedCalc” ststistical software. p-values less than 0.05 were considered statistically significant.RESULTS: According to the 2016 WHO classification astrocytic and oligodendroglial tumors of the adult type were divided into three groups: astrocytic gliomas with a mutation in the IDH1 gene (IDH1 mut) (n=95), astrocytic gliomas without a mutation in the IDH1 gene (IDH1 wild type — IDH1 wt) (n=103), and IDH1-mutant oligodendrogliomas (n=62). Significant differences in all ratios between the three molecular groups of gliomas were established. TBRmax cutoff of 2.27 differentiated between IDH1 wt and IDH1 mut gliomas with a sensitivity of 61% and a specificity of 77% (area under curve — AUC 0.752). When considering subgroups of gliomas that are homogeneous in terms of the IDH1 status or Grade, the dependence of TBR on the glioma histotype and grading was additionally established. In IDH1 mut oligodendrogliomas, TBR was significantly higher than in mutant astrocytomas, and in IDH1 wt astrocytomas, significant differences in TBR were established between Grade 2 and Grade 3–4. TBRmax was not a predictor of glioma type according to the WHO 2016 classification due to significant overlap of individual of TBR values. But TBRmax allowed diagnosing a cluster of malignant gliomas, including glioblastoma and astrocytoma Grade 3 IDH wt, as well as oligodendroglioma Grade 3 IDH1 mut, with a sensitivity of 65% and a specificity of 89% (AUC 0.848) at a cutoff of TBR=2.7. A strong correlation between the three tumor-to-brain ratios allows any ratio to be used in diagnostics. There were no significant differences in MTV between molecular types of gliomas.DISCUSSION: Distinguishing glioma types based on the 2016 WHO classification of the CNS tumors on the basis of 11Cmethionine uptake seems to be not reliable due to many factors that affect its uptake. In astrocytomas high TBR is associated with malignant grade and wild type IDH1 gene. However, the lack of differences in TBR between these astrocytomas and Grade 3 IDH1-mutant oligodendrogliomas does not allow one to predict the IDH1 status of the tumor in the absence of other radiological signs of the glioma histotype. The absence of differences in TBR between Grade 2 and Grade 3 astrocytomas IDH1 mut supports the view that they are considered as a single subgroup of lower grade gliomas. CONCLUSION: PET/CT with 11C-methionine has limited potential to assess the IDH status of diffuse gliomas. High TBR is associated with malignant glioma with wild-type IDH1 gene or oligodendroglial structure.

Ultra-high b-value DWI accurately identifies isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas.

To distinguish isocitrate dehydrogenase (IDH) genotypes and tumor subtypes of adult-type diffuse gliomas based on the fifth edition of the World Health Organization classification of central nervous system tumors (WHO CNS5) in 2021 using standard, high, and ultra-high b-value diffusion-weighted imaging (DWI). This prospective study enrolled 70 patients with adult-type diffuse gliomas who underwent multiple b-value DWI. Apparent diffusion coefficient (ADC) values including ADCb500/b1000, ADCb500/b2000, ADCb500/b3000, ADCb500/b4000, ADCb500/b6000, ADCb500/b8000, and ADCb500/b10000 in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated. The ADC ratios of TP/NAWM were assessed for correlations with IDH genotypes, tumor subtypes, and Ki-67 status; diagnostic performances were compared. All ADCs were significantly higher in IDHmutant gliomas than in IDH wild-type gliomas (p < 0.01 for all); ADCb500/b8000 had the highest area under the curve (AUC) of 0.866. All ADCs were significantly lower in glioblastoma than in astrocytoma (p < 0.01 for all). ADCs other than ADCb500/b1000 were significantly lower in glioblastoma than in oligodendroglioma (p < 0.05 for all). ADCb500/b8000 and ADCb500/b10000 were significantly higher in oligodendroglioma than in astrocytoma (p = 0.034 and 0.023). The highest AUCs were 0.818 for ADCb500/b6000 when distinguishing glioblastoma from astrocytoma, 0.979 for ADCb500/b8000 and ADCb500/b10000 when distinguishing glioblastoma from oligodendroglioma, and 0.773 for ADCb500/b10000 when distinguishing astrocytoma from oligodendroglioma. Additionally, all ADCs were negatively correlated with Ki-67 status (p < 0.05 for all). Ultra-high b-value DWI can reliably separate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using WHO CNS5 criteria. Ultra-high b-value diffusion-weighted imaging can accurately distinguish isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas, which may facilitate personalized treatment and prognostic assessment for patients with glioma. • Ultra-high b-value diffusion-weighted imaging can accurately distinguish subtle differences in water diffusion among biological tissues. • Ultra-high b-value diffusion-weighted imaging can reliably separate isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas. • Compared with standard b-value diffusion-weighted imaging, high and ultra-high b-value diffusion-weighted imaging demonstrate better diagnostic performances.

Predictive Value of T2-weighted Perfusion and T1-weighted Permeability MRI Parameters in Determining IDH Mutational Status and Grade of Gliomas.

To assess the performance metrics of perfusion and permeability magnetic resonance imaging (MRI) parameters with optimal cut-offs in differentiating isocitrate dehydrogenase (IDH) genotype and tumor grade in patients with grade 2-4 gliomas. This retrospective study included 36 patients surgically diagnosed with grade 2-4 glioma (six grade 2, seven grade 3, and 23 grade 4) with known IDH genotypes (23 IDH wild-type, 13 IDH mutant) between November 2021 and August 2023. All patients underwent preoperative perfusion and permeability MRI examinations with a 3.0 Tesla scanner. Parameters were calculated on colored map images. Using the intraclass correlation coefficient, intra- and inter-observer agreement was assessed. Following multiple testing correction, the perfusion parameters with statistically significant differences were subjected to receiver operating characteristic (ROC) analysis. Five MRI parameters (rCBV and rCBF from perfusion; Ktrans, Ve, and Vp from permeability) showed a significant difference between groups in terms of IDH genotype (p < 0.001). In ROC analysis, the best parameters in differentiating IDH genotype included rCBV and Ktrans; rCBV with a cut-off of 5.58 achieved an area under the ROC curve (AUC), sensitivity, specificity, and accuracy of 0.883, 95.7%, 76.9%, and 88.8%, respectively. For Ktrans, with a cut-off of 0.0727 min-1, these values were 0.893, 100%, 69.2%, and 88.8%, respectively. In ROC analysis, these two parameters with rCBF and Ve also showed good performance in differentiating low- and high-grade gliomas with an AUC, sensitivity, and accuracy exceeding 0.940, 86%, and 88%, respectively. Perfusion and permeability MRI may provide useful parameters in differentiating the IDH genotype and grade of gliomas.

Development and validation of clinical-radiomics analysis for preoperative prediction of IDH mutation status and WHO grade in diffuse gliomas: a consecutive L-[methyl-11C] methionine cohort study with two PET scanners.

Determination of isocitrate dehydrogenase (IDH) genotype is crucial in the stratification of diagnosis and prognostication in diffuse gliomas. We sought to build and validate radiomics models and clinical features incorporated nomogram for preoperative prediction of IDH mutation status and WHO grade of diffuse gliomas with L-[methyl-11C] methionine ([11C]MET) PET/CT imaging according to the 2016 WHO classification of tumors of the central nervous system. Consecutive 178 preoperative [11C]MET PET/CT images were retrospectively studied for radiomics analysis. One hundred six patients from PET scanner 1 were used as training dataset, and 72 patients from PET scanner 2 were used for validation dataset. [11C]MET PET and integrated CT radiomics features were extracted, respectively; three independent predictive models were built based on PET features, CT features, and combined PET/CT features, respectively. The SelectKBest method, Spearman correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and machine learning algorithms were applied for feature selection and model building. After filtering the satisfactory predictive model, key clinical features were incorporated for the nomogram establishment. The combined [11C]MET PET/CT radiomics model, which consisted of four PET features and eight integrated CT features, was significantly associated with IDH genotype (p < 0.0001 for both training and validation datasets). Nomogram based on the [11C]MET PET/CT radiomics score, patients' age, and dichotomous tumor location status showed satisfactory discrimination capacity, and the AUC was 0.880 (95% CI, 0.726-0.998) in the training dataset and 0.866 (95% CI, 0.777-0.956) in the validation dataset. In IDH stratified WHO grade prediction, the final radiomics model consists of four PET features and two CT features had reasonable and stable differential efficacy of WHO grade II and III patients from grade IV patients in IDH-wildtype patients, and the AUC was 0.820 (95% CI, 0.541-1.000) in the training dataset and 0.766 (95% CI, 0.612-0.921) in the validation dataset. [11C]MET PET radiomics features could benefit non-invasive IDH genotype prediction, and integrated CT radiomics features could enhance the efficacy. Radiomics and clinical features incorporation could establish satisfactory nomogram for clinical application. This non-invasive predictive investigation based on our consecutive cohort from two PET scanners could provide the perspective to observe the differential efficacy and the stability of radiomics-based investigation in untreated diffuse gliomas.

Histogram analysis of quantitative susceptibility mapping and apparent diffusion coefficient for identifying isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas.

Accurate preoperative identification of isocitrate dehydrogenase (IDH) genotypes and tumor subtypes is highly important for proper treatment planning and prognosis evaluation in patients with glioma. This study aimed to differentiate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using histogram features of quantitative susceptibility mapping (QSM) and apparent diffusion coefficient (ADC). This prospective study enrolled patients with suspected gliomas between March 2019 and January 2022 in a random series. Histogram features of QSM and ADC were extracted from the tumor parenchyma. The Mann-Whitney U test was used to compare the difference in histogram features between different IDH genotypes and among tumor subtypes. Receiver operating characteristic (ROC) curves were constructed to assess the corresponding diagnostic performance. This study included 47 patients with histopathologically confirmed adult-type diffuse gliomas. Totals of seven QSM features including 10th percentile (P10), 90th percentile (P90), interquartile range (IQR), maximum, mean absolute deviation (MAD), root mean squared (RMS), and variance, and five ADC features including P10, mean, median, RMS, and skewness exhibited significant differences between different IDH genotypes (P<0.05 for all), with the IQR of QSM demonstrating the highest area under curve (AUC) of 0.774 [95% confidence interval (CI): 0.635-0.913]. For separating tumor subtypes, the IQR of QSM also showed the highest AUC of 0.745 (95% CI: 0.566-0.924) for glioblastoma (GBM) versus astrocytoma and 0.848 (95% CI: 0.706-0.989) for GBM versus oligodendroglioma, but none of the features could discriminate astrocytoma from oligodendroglioma. The combination of the IQR of QSM, P10 of ADC, and age achieved the highest AUC of 0.910 (95% CI: 0.826-0.994) for IDH genotypes, and 0.939 (95% CI: 0.859-1.000) and 0.967 (95% CI: 0.904-1.000) for GBM versus astrocytoma and GBM versus oligodendroglioma, respectively. QSM and ADC histogram features may serve as potential imaging markers for noninvasively assessing IDH genotypes and tumor subtypes of adult-type diffuse gliomas. Combining significant features may enhance the diagnostic performance substantially.

Predictive IDH Genotyping Based on the Evaluation of Spatial Metabolic Heterogeneity by Compartmental Uptake Characteristics in Preoperative Glioma Using 18F-FET PET.

Molecular markers are of increasing importance for classifying, treating, and determining the prognosis for central nervous system tumors. Isocitrate dehydrogenase (IDH) is a critical regulator of glucose and amino acid metabolism. Our objective was to investigate metabolic reprogramming of glioma using compartmental uptake (CU) characteristics in O-(2-18F-fluoroethyl)-l-tyrosine (FET) PET and to evaluate its diagnostic potential for IDH genotyping. Methods: Between 2017 and 2022, patients with confirmed glioma were preoperatively investigated using static 18F-FET PET. Metabolic tumor volume (MTV), MTV for 60%-100% uptake (MTV60), and T2-weighted and contrast-enhancing lesion volumes were automatically segmented using U-Net neural architecture and isocontouring. Volume intersections were determined using the Dice coefficient. Uptake characteristics were determined for metabolically defined compartments (central [80%-100%] and peripheral [60%-75%] areas of 18F-FET uptake). CU ratio was defined as the fraction between the peripheral and central compartments. Mean target-to-background ratio was calculated. Comparisons were performed using parametric and nonparametric tests. Receiver-operating-characteristic curves, regression, and correlation were used for statistical analysis. Results: In total, 52 participants (male, 27, female, 25; mean age ± SD, 51 ± 16 y) were evaluated. MTV60 was greater and distinct from contrast-enhancing lesion volume (P = 0.046). IDH-mutated tumors presented a greater volumetric CU ratio and SUV CU ratio than IDH wild-type tumors (P < 0.05). Volumetric CU ratio determined IDH genotype with excellent diagnostic performance (area under the curve [AUC], 0.88; P < 0.001) at more than 5.49 (sensitivity, 86%, specificity, 90%), because IDH-mutated tumors presented a greater peripheral metabolic compartment than IDH wild-type tumors (P = 0.045). MTV60 and MTV were not suitable for IDH classification (P > 0.05). SUV CU ratio (AUC, 0.72; P = 0.005) and target-to-background ratio (AUC, 0.68; P = 0.016) achieved modest diagnostic performance-inferior to the volumetric CU ratio. Furthermore, the classification of loss of heterozygosity of chromosomes 1p and 19q (AUC, 0.75; P = 0.019), MGMT promoter methylation (AUC, 0.70; P = 0.011), and ATRX loss (AUC, 0.73; P = 0.004) by amino acid PET was evaluated. Conclusion: We proposed parametric 18F-FET PET as a noninvasive metabolic biomarker for the evaluation of CU characteristics, which differentiated IDH genotype with excellent diagnostic performance, establishing a critical association between spatial metabolic heterogeneity, mitochondrial tricarboxylic acid cycle, and genomic features with critical implications for clinical management and the diagnostic workup of patients with central nervous system cancer.

A Dual-Aware deep learning framework for identification of glioma isocitrate dehydrogenase genotype using magnetic resonance amide proton transfer modalities

To propose a Dual-Aware deep learning framework for genotyping of isocitrate dehydrogenase (IDH) in gliomas based on magnetic resonance amide proton transfer (APT) modality data as a means to assist non-invasive diagnosis of gliomas. We collected multimodal magnetic resonance imaging (MRI) imaging data of the brain from 118 cases of gliomas, including 68 wild-type and 50 mutant type cases. The delineation of the ROI of brain glioma was completed in all the cases. APT modality imaging does not require contrast agents, and its signal intensity on tumors is positively correlated with tumor malignancy, and the signal intensity on wild-type IDH is higher than that on mutant IDH. For APT modalities, tumor imaging and derived areas are morphologically variable and lack prominent edge contour characteristics compared with other modalities. Based on these characteristics, we propose the Dual-Aware framework, which introduces the Multi-Aware framework to mine multi-scale features, and the Edge Aware module mines the edge features for automatic genotype identification. The introduction of two types of Aware mechanisms effectively improved the identification rate of the model for glioma IDH genotyping. The accuracy and AUC for each modality data were enhanced, and the best performance was achieved on the APT modality with a prediction accuracy of 83.1% and an AUC of 0.822, suggesting its advantages and effectiveness for identifying glioma IDH genotypes. The proposed deep learning algorithm model constructed based on the image characteristics of the APT modality is effective for glioma IDH genotyping and identification task and may potentially replace the commonly used T1CE modality to avoid contrast agent injection and achieve non- invasive IDH genotyping.

Predicting Isocitrate Dehydrogenase Mutation Status of Grade 2–4 Gliomas with Diffusion Tensor Imaging (DTI) Parameters Derived from Model-Based DTI and Model-Free Q-Sampling Imaging Reconstructions

To determine whether diffusion tensor imaging (DTI) parameters acquired with model-based DTI and model-free generalized Q-sampling imaging (GQI) reconstructions may noninvasively predict isocitrate dehydrogenase (IDH) mutational status in patients with grade 2-4 gliomas. Forty patients with known IDH genotype (28 IDH wild-type; 12 IDH mutant) who underwent preoperative DTI evaluation on a 3-Tesla magnetic resonance imaging scanner were analyzed retrospectively. Absolute values obtained from model-based and model-free reconstructions were compared. Using the intraclass correlation coefficient, interobserver agreement was assessed for various sampling techniques. Variables having statistically significant distributions between IDH groups were subjected to a receiver operating characteristic (ROC) analysis. Using multivariable logistic regression analysis, independent predictors, if present, were identified and a model was developed. Six imaging parameters (3 from model-based DTI and 3 from model-free GQI reconstructions) showed statistically significant differences between groups (P < 0.001, power >0.97), with very high correlation to each other (P < 0.001). Age difference between the groups was statistically significant (P < 0.001). The optimal logistic regression model comprised a GQI-based parameter and age, which were independent predictors as well, producing an area under the ROC curve, accuracy, sensitivity, and specificity of 0.926, 85%, 75%, and 89.3%, respectively. Using the GQI reconstruction feature alone with a cut-off of 1.60, an 85% of accuracy was also achieved with ROC analysis. The imaging parameters acquired from model-based DTI and model-free GQI reconstructions, combined with the clinical variable age, may have the ability to noninvasively predict the IDH genotype in gliomas, either alone or in particular combinations.

Predicting isocitrate dehydrogenase genotype, histological phenotype, and Ki-67 expression level in diffuse gliomas with an advanced contrast analysis of magnetic resonance imaging sequences.

The present study aimed to establish a robust predictive model based on a machine learning (ML) algorithm providing preoperative noninvasive diagnosis and to further explore the contribution of each magnetic resonance imaging (MRI) sequence to the classification to help select images for future model development. This was a retrospective cross-sectional study, and consecutive patients with histologically confirmed diffuse gliomas in our hospital from November 2015 to October 2019 were recruited. The participants were grouped into a training and testing set based on a ratio of 8:2. Five MRI sequences were employed to develop the support vector machine (SVM) classification model. An advanced contrast analysis of single-sequence-based classifiers was performed, according to which different sequence combinations were tested, and the best one was selected to form an ultimate classifier. Patients whose MRIs were acquired with other types of scanners formed an additional, independent validation set. A total of 150 patients with gliomas were used in the present study. Contrast analysis revealed that the contribution of the apparent diffusion coefficient (ADC) was the most significant [accuracies were as follows: histological phenotype, 0.640; isocitrate dehydrogenase (IDH) status, 0.656; and Ki-67 expression, 0.699] and that of T1 weighted imaging was limited (accuracies were as follows: histological phenotype, 0.521; IDH status, 0.492; and Ki-67 expression, 0.556). The ultimate classifiers for IDH status, histological phenotype, and Ki-67 expression achieved promising performances with area under the curve (AUC) values of 0.88, 0.93, and 0.93, respectively. The classifiers for the histological phenotype, IDH status, and Ki-67 expression correctly predicted 3 of 5 subjects, 6 of 7 subjects, and 9 of 13 subjects in the additional validation set, respectively. The present study showed satisfactory performance in predicting the IDH genotype, histological phenotype, and Ki-67 expression level. The contrast analysis revealed the contribution of different MRI sequences and suggested that the combination of all the acquired sequences was not the optimal strategy to build the radiogenomics-based classifier.

Intra-operative desorption-electrospray ionization mass-spectrometry for real-time diagnosis, margin assessment, and maximization of brain cancer resection.

2071 Background: Despite advances in our understanding of brain cancer, the ability to discern between infiltrated and non-infiltrated tissue with current adjuncts, remains elusive. Isocitrate dehydrogenase (IDH) mutation is unique to IDH-mut glioma cells and can be used as a surrogate for tumor infiltration. Further, emerging evidence has shown unique signatures in brain cancer lipidomic profiling, with lipid metabolism playing a key role in brain cancer biology. Here, we aimed to evaluate the impact of desorption electrospray ionization-mass spectrometry (DESI-MS) as a near real-time method to determine infiltration status and the use of DESI-MS to perform intra-operative histological diagnosis. Methods: This is a prospective multi-institution cohort study using intra-operative DESI-MS on freshly obtained tumor samples to evaluate 2-hydroxyglutarate (2HG), with post-hoc validation by review by a board-certified neuropathologist. For tumor cell estimation and tissue diagnosis, high-throughput DESI-MS was used for rapid analysis of high-density arrays using lipidomic profiling. Analysis of the data was performed using a pre-written algorithm employing a combination of Python- and MATLAB-based custom software allowing results in less than a second per sample. Results: A total of 496 tumor biopsies from 72 patients have been analyzed. Earlier iterations focused on absolute quantitation of 2HG in 247 biopsies from 49 patients to determine IDH genotype yielding sensitivity, specificity, and accuracy values of 89, 100, and 94% for core biopsies (71). Improvement in accuracy was obtained with relative measurement of 2HG in comparison to glutamate allowing for detection of IDH-mut infiltration at the margin, resulting in 93% sensitivity, 100% specificity, and 98% accuracy with turnover times averaging 3 minutes in 183 biopsies from 23 patients. Margin assessment beyond the limits of neuro-navigation and surgeon assessment showed tumor infiltration in 88% of margin biopsies from IDH mutant tumors (30 of 34 biopsies) with 100% accuracy. Intra-operative tissue diagnosis was pursued in 66 unique tumor samples using DESI-MS lipidomic profiling, including samples of normal brain parenchyma, gliomas, meningiomas and pituitary adenomas, split into two microarray sets (36 and 40 samples). Using estimated principal components as input features, high accuracy ( &gt; 90%; ROC AUCs ≥ 0.89) was obtained for supervised tissue classification using simple machine learning algorithms. Conclusions: We present a novel system for intra-operative evaluation of tumor infiltration at the margin in gliomas and for intra-operative tissue diagnosis of brain cancer. Our method identified tumor infiltration beyond the surgical margins and serves as a foundation for future uses employing DESI-MS to allow for molecularly guided surgery in IDH-mut gliomas.

PERK-Mediated Cholesterol Excretion from IDH Mutant Glioma Determines Anti-Tumoral Polarization of Microglia.

Isocitrate dehydrogenase (IDH) mutation, a known pathologic classifier, initiates metabolic reprogramming in glioma cells and has been linked to the reaction status of glioma-associated microglia/macrophages (GAMs). However, it remains unclear how IDH genotypes contribute to GAM phenotypes. Here, it is demonstrated that gliomas expressing mutant IDH determine M1-like polarization of GAMs, while archetypal IDH induces M2-like polarization. Intriguingly, IDH-mutant gliomas secrete excess cholesterol, resulting in cholesterol-rich, pro-inflammatory GAMs without altering their cholesterol biosynthesis, and simultaneously exhibiting low levels of tumoral cholesterol due to expression remodeling of cholesterol transport molecules, particularly upregulation of ABCA1 and downregulation of LDLR. Mechanistically, a miR-19a/LDLR axis-mediated novel post-transcriptional regulation of cholesterol uptake is identified, modulated by IDH mutation, and influencing tumor cell proliferation and invasion. IDH mutation-induced PERK activation enhances cholesterol export from glioma cells via the miR-19a/LDLR axis and ABCA1/APOE upregulation. Further, a synthetic PERK activator, CCT020312 is introduced, which markedly stimulates cholesterol efflux from IDH wild-type glioma cells, induces M1-like polarization of GAMs, and consequently suppresses glioma cell invasion. The findings reveal an essential role of the PERK/miR-19a/LDLR signaling pathway in orchestrating gliomal cholesterol transport and the subsequent phenotypes of GAMs, thereby highlighting a novel potential target pathway for glioma therapy.

Metabolic-related gene pairs signature analysis identifies ABCA1 expression levels on tumor-associated macrophages as a prognostic biomarker in primary IDHWT glioblastoma

BackgroundAlthough isocitrate dehydrogenase (IDH) mutation serves as a prognostic signature for routine clinical management of glioma, nearly 90% of glioblastomas (GBM) patients have a wild-type IDH genotype (IDHWT) and lack reliable signatures to identify distinct entities.MethodsTo develop a robust prognostic signature for IDHWT GBM patients, we retrospectively analyzed 4 public datasets of 377 primary frozen tumor tissue transcriptome profiling and clinical follow-up data. Samples were divided into a training dataset (204 samples) and a validation (173 samples) dataset. A prognostic signature consisting of 21 metabolism-related gene pairs (MRGPs) was developed based on the relative ranking of single-sample gene expression levels. GSEA and immune subtype analyses were performed to reveal differences in biological processes between MRGP risk groups. The single-cell RNA-seq dataset was used to examine the expression distribution of each MRG constituting the signature in tumor tissue subsets. Finally, the association of MRGs with tumor progression was biologically validated in orthotopic GBM models.ResultsThe metabolic signature remained an independent prognostic factor (hazard ratio, 5.71 [3.542-9.218], P < 0.001) for stratifying patients into high- and low-risk levels in terms of overall survival across subgroups with MGMTp methylation statuses, expression subtypes, and chemo/ratio therapies. Immune-related biological processes were significantly different between MRGP risk groups. Compared with the low-risk group, the high-risk group was significantly enriched in humoral immune responses and phagocytosis processes, and had more monocyte infiltration and less activated DC, NK, and γδ T cell infiltration. scRNA-seq dataset analysis identified that the expression levels of 5 MRGs (ABCA1, HMOX1, MTHFD2, PIM1, and PTPRE) in TAMs increased with metabolic risk. With tumor progression, the expression level of ABCA1 in TAMs was positively correlated with the population of TAMs in tumor tissue. Downregulation of ABCA1 levels can promote TAM polarization towards an inflammatory phenotype and control tumor growth.ConclusionsThe metabolic signature is expected to be used in the individualized management of primary IDHWT GBM patients.

Diagnostic performance of gliomas grading and IDH status decoding A comparison between 3D amide proton transfer APT and four diffusion-weighted MRI models.

The focus of neuro-oncology research has changed from histopathologic grading to molecular characteristics, and medical imaging routinely follows this change. To compare the diagnostic performance of amide proton transfer (APT) and four diffusion models in gliomas grading and isocitrate dehydrogenase (IDH) genotype. Prospective. A total of 62 participants (37 males, 25 females; mean age, 52 ± 13 years) whose IDH genotypes were mutant in 6 of 14 grade II gliomas, 8 of 20 of grade III gliomas, and 4 of 28 grade IV gliomas. APT imaging using sampling perfection with application optimized contrasts by using different flip angle evolutions (SPACE) and DWI with q-space Cartesian grid sampling were acquired at 3T. The ability of diffusion kurtosis imaging, diffusion kurtosis imaging, neurite orientation dispersion and density imaging (NODDI), mean apparent propagator (MAP), and APT imaging for glioma grade and IDH status were assessed, with histopathological grade and genetic testing used as a reference standard. Regions of interest (ROIs) were drawn by two neuroradiologists after consensus. T-test and Mann-Whitney U test; one-way analysis of variance (ANOVA); receiver operating curve (ROC) and area under the curve (AUC); DeLong test. P value < 0.05 was considered statistically significant. Compared with IDH-mutant gliomas, IDH-wildtype gliomas showed a significantly higher mean, 5th-percentile (APT5 ), and 95th-percentile from APTw, the 95th-percentile value of axial, mean, and radial diffusivity from DKI, and 95th-percentile value of isotropic volume fraction from NODDI, and no significantly different parameters from DTI and MAP (P = 0.075-0.998). The combined APT model showed a significantly wider area under the curve (AUC 0.870) for IDH status, when compared with DKI and NODDI. APT5 was significantly different between two of the three groups (glioma II vs. glioma III vs. glioma IV: 1.35 ± 0.75 vs. 2.09 ± 0.93 vs. 2.71 ± 0.81). APT has higher diagnostic accuracy than DTI, DKI, MAP, and NODDI in glioma IDH genotype. APT5 can effectively identify both tumor grading and IDH genotyping, making it a promising biomarker for glioma classification. 1 TECHNICAL EFFICACY: Stage 2.

Whole-Tumor Histogram Analysis of Multiple Diffusion Metrics for Glioma Genotyping.

Background The isocitrate dehydrogenase (IDH) genotype and 1p/19q codeletion status are key molecular markers included in glioma pathologic diagnosis. Advanced diffusion models provide additional microstructural information. Purpose To compare the diagnostic performance of histogram features of multiple diffusion metrics in predicting glioma IDH and 1p/19q genotyping. Materials and Methods In this prospective study, participants were enrolled from December 2018 to December 2020. Diffusion-weighted imaging was performed by using a spin-echo echo-planar imaging sequence with five b values (500, 1000, 1500, 2000, and 2500 sec/mm2) in 30 directions for every b value and one b value of 0. Diffusion metrics of diffusion-tensor imaging (DTI), diffusion-kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) were calculated, and their histogram features were analyzed in regions that included the entire tumor and peritumoral edema. Comparisons between groups were performed according to IDH genotype and 1p/19q codeletion status. Logistic regression analysis was used to predict the IDH and 1p/19q genotypes. Results A total of 215 participants (115 men, 100 women; mean age, 48 years ± 13 [standard deviation]) with grade II (n = 68), grade III (n = 35), and grade IV (n = 112) glioma were included. Among the DTI, DKI, NODDI, MAP, and total diffusion models, there were no significant differences in the areas under the receiver operating characteristic curve (AUCs) for predicting IDH mutations (AUC, 0.76, 0.82, 0.78, 0.81, and 0.82, respectively; P > .05) and 1p/19q codeletion in gliomas with IDH mutations (AUC, 0.83, 0.81, 0.82, 0.83, and 0.88, respectively; P > .05). A regression model with an R2 value of 0.84 was used for the Ki-67 labeling index and histogram features of the diffusion metrics. Conclusion Whole-tumor histogram analysis of multiple diffusion metrics is a promising approach for glioma isocitrate dehydrogenase and 1p/19q genotyping, and the performance of diffusion-tensor imaging is similar to that of advanced diffusion models. Clinical trial registration no. ChiCTR2100048119 © RSNA, 2021 Online supplemental material is available for this article. An earlier incorrect version appeared online. This article was corrected on December 14, 2021.

Integrated CT Radiomics Features Could Enhance the Efficacy of 18F-FET PET for Non-Invasive Isocitrate Dehydrogenase Genotype Prediction in Adult Untreated Gliomas: A Retrospective Cohort Study.

PurposeWe aimed to investigate the predictive models based on O-[2-(18F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography (18F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas.MethodsFifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment 18F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUVSD), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models.ResultsThe AUC of the simple predictive model consists of semi-quantitative parameter SUVSD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three 18F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined 18F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model (p = 0.048) and simple predictive model (p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973).ConclusionThe predictive model combining 18F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment.

Selected Articles from This Issue

Medulloblastoma is a common and deadly pediatric malignancy whose lethality is compounded by a lack of targeted therapies, partially owing to the differential biologies of the four major molecular subtypes. The most prevalent subtype, Sonic Hedgehog medulloblastoma (SHH-MB), is driven by hyperactivation of the eponymous SHH pathway within MATH-1-expressing granule cell precursors in the developing brain. Therefore, efforts to develop molecular targeted therapies for SHH-MB have focused largely on downstream targets and effectors of the SHH pathway such as the proto-oncogene SOX9, which has been shown to be specifically overexpressed in SHH-MB but whose functional role in disease progression has yet to be defined. Here, Adolphe and colleagues demonstrate that loss of SOX9 does not significantly impact the course of SHH-MB disease development and progression, indicating a dispensable role for this proto-oncogene in SHH-MB despite its elevated expression in most cases. This finding may have significant implications for ongoing preclinical and translational work aimed at identifying suitable molecular drivers of SHH-MB for therapeutic development.Lymphangioleiomyomatosis is a rare neoplastic disease of unknown origin that is nearly exclusive to women, with therapies targeting mTOR as the recognized standard of care. However, there is substantial variability in disease presentation, progression, and therap. response. Here, Espin, Baiges and colleagues perform multi-omics characterization of patient-derived lymphangioleiomyomatosis cells and tissues to identify the molecular features and potential causes of disease heterogeneity. The data suggest that differential expression of certain transcription factors—such as YB1, RUNX1, IRF1, estrogen receptor, and progesterone receptor—as well as certain immunity factors may account for some of these features. Based on the molecular features identified herein, the authors also nominate a breast mesenchymal cell line and certain pleural mesothelioma cell lines as potential surrogate models of lymphangioleiomyomatosis, expanding on the biological basis of the disease. Taken together, the data provide a high-value resource for further exploration of the molecular drivers of lymphangioleiomyomatosis progression.Glioma patients are frequently treated with temozolomide, a DNA alkylating chemotherapeutic that is known to cross the blood-brain barrier. The isocitrate dehydrogenase (IDH) R132H mutation is a common molecular feature of gliomas and is generally associated with gliomagenesis, but also with a more robust response to temozolomide therapy. However, the molecular underpinnings of this observation are not defined. In this study, Zhan, Ma, and colleagues use a heterozygous knock-in approach to model the effects of IDH R132H/wild-type allele balance on glioma cell biology. They find that the heterozygous IDH genotype in tandem with glioma driver gene alterations in p53, RB, and ATRX predisposes glioma cells to pro-senescent features. This combination of molecular features was associated with increased DNA damage markers and potentiated the effects of temozolomide in this model. However, heterozygous IDH R132H mutation also predisposed astroglial cells toward a senescence-associated secretory phenotype, which is known to exert a pro-tumorigenic effect within a tumor-permissive microenvironment. These data offer a potential mechanistic explanation for how a single mutation within IDH may provide a pro-tumorigenic stimulus while also sensitizing glioma cells to alkylating chemotherapy with temozolomide.Potentiating the effects of genotoxic interventions such as chemo- or radiotherapy requires the suppression of DNA damage response pathways, such as non-homologous end joining (NHEJ). Blocking NHEJ with chemical inhibitors is one strategy, but none have been clinically approved for this application. Building upon prior efforts to develop chemical screening strategies for NHEJ inhibitors, Du and colleagues report a miniaturized screening protocol that can be carried out in 96-well plates. By assessing an FDA-approved drug library in this format, the authors identify several potential candidates that function to inhibit NHEJ at concentrations below their cytotoxic dose. One of these drugs, ompalisib, was shown to suppress the phosphorylation of the DNA-dependent protein kinase catalytic subunit (DNA-PKcs) in response to chemo- or radiotherapy, a key step in the initiation of NHEJ to mediate their genotoxic effects. Overall, the authors advance a novel screening strategy to examine the effects of pharmacological interventions on DNA damage responses via NHEJ, and further nominate a candidate FDA-approved drug for development as a chemo- and radiosensitizer.

Multi-parametric Z-spectral MRI may have a good performance for glioma stratification in clinical patients

To comprehensively and noninvasively risk-stratify glioma grade, isocitrate dehydrogenase (IDH) genotype, and 1p/19q codeletion status using multi-contrast Z-spectral magnetic resonance imaging (MRI). One hundred and thirteen patients with glioma were retrospectively included. Multiple contrasts contributing to Z-spectra, including direct saturation of water (DSW), semi-solid magnetization transfer contrast (MTC), amide proton transfer (APT) effect, aliphatic nuclear Overhauser effect, and the 2-ppm chemical exchange saturation transfer peak (CEST@2ppm), were fitted with five individual Lorentzian functions. Z-spectral contrasts were compared according to the three most important risk stratifications: tumor grade, IDH genotype, and 1p/19q codeletion status. We further investigated the differentiation of 1p/19q codeletion status within IDH mutant gliomas. The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic (ROC) analyses. DSW was significantly different within grade, IDH genotypes, and 1p/19q codeletion status. APT was significantly different with grade and IDH mutation, but not with 1p/19q subtypes. CEST@2ppm was only significantly different with 1p/19q codeletion subtypes. DSW and CEST@2ppm were the two Z-spectral contrasts able to differentiate 1p/19q codeletion subtypes within IDH mutant gliomas. For differentiating glioma grades using ROC analyses, DSW achieved the largest AUC. For differentiating IDH genotypes, DSW and APT achieved comparable AUCs. DSW was the best metric for differentiating 1p/19q codeletion status within all patients and within the IDH mutant patients. Combining all Z-spectral contrasts improved sensitivity and specificity for all risk stratifications. Multi-parametric Z-spectral MRI serves as a useful, comprehensive, and noninvasive imaging technique for glioma stratification in clinical patients. • Multiple contrasts contributing to Z-spectra were separately fitted with Lorentzian functions. • Z-spectral contrasts were compared within the three most important and common tumor risk stratifications for gliomas: tumor grade, IDH genotype, and 1p/19q codeletion status. • The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic analyses, which found Z-spectral MRI to be a useful and comprehensive imaging biomarker for glioma stratification.

Systemic Immune Bias Delineates Malignant Astrocytoma Survival Cohorts.

Patients with grade III anaplastic astrocytomas (AA) separate into survival cohorts based on the presence or absence of mutations in isocitrate dehydrogenase (IDH). Progression to glioblastoma (GBM), morphologically distinguishable by elevated microvascular proliferation, necrosis, and cell division in tumor tissues, is considerably more rapid in IDH wild-type tumors such that their diagnosis as AA is relatively rare. More often initially presenting as GBM, these contain higher numbers of tumor-associated macrophages (TAMs) than most AA, and GBM patients also have higher levels of circulating M2 monocytes. TAM and M2 monocytes share functional properties inhibitory for antitumor immunity. Yet, although there is a wealth of data implicating TAM in tumor-immune evasion, there has been limited analysis of the impact of the circulating M2 monocytes. In the current study, immune parameters in sera, circulating cells, and tumor tissues from patients with primary gliomas morphologically diagnosed as AA were assessed. Profound differences in serum cytokines, glioma extracellular vesicle cross-reactive Abs, and gene expression by circulating cells identified two distinct patient cohorts. Evidence of type 2-immune bias was most often seen in patients with IDH wild-type AA, whereas a type 1 bias was common in patients with tumors expressing the IDH1R132H mutation. Nevertheless, a patient's immune profile was better correlated with the extent of tumor vascular enhancement on magnetic resonance imaging than IDH mutational status. Regardless of IDH genotype, AA progression appears to be associated with a switch in systemic immune bias from type 1 to type 2 and the loss of tumor vasculature integrity.

Evaluation of FET PET Radiomics Feature Repeatability in Glioma Patients.

Simple SummaryInitial studies suggested the additional diagnostic value of amino acid positron emission tomography (PET) radiomics using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET) in brain tumor patient management. However, to ensure the reliable performance of the generated FET PET radiomics models for clinical diagnostics, repeatability of radiomics features is essential. Hence, we assessed the impact of brain tumor volumes and key molecular alterations such as an isocitrate dehydrogenase (IDH) mutation on the repeatability of FET PET radiomics features in 50 newly diagnosed glioma patients. In a test–retest approach based on routinely acquired FET PET scans, we identified 297 repeatable features. The IDH genotype did not affect feature repeatability. Moreover, these robust features were able to differentiate patients with IDH-wildtype glioma from those with an IDH mutation. Our results suggest that robust radiomics features can be obtained from routinely acquired FET PET scans, which are valuable for further standardization of radiomics analyses in neurooncology.Amino acid PET using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET) has attracted considerable interest in neurooncology. Furthermore, initial studies suggested the additional diagnostic value of FET PET radiomics in brain tumor patient management. However, the conclusiveness of radiomics models strongly depends on feature generalizability. We here evaluated the repeatability of feature-based FET PET radiomics. A test–retest analysis based on equivalent but statistically independent subsamples of FET PET images was performed in 50 newly diagnosed and histomolecularly characterized glioma patients. A total of 1,302 radiomics features were calculated from semi-automatically segmented tumor volumes-of-interest (VOIs). Furthermore, to investigate the influence of the spatial resolution of PET on repeatability, spherical VOIs of different sizes were positioned in the tumor and healthy brain tissue. Feature repeatability was assessed by calculating the intraclass correlation coefficient (ICC). To further investigate the influence of the isocitrate dehydrogenase (IDH) genotype on feature repeatability, a hierarchical cluster analysis was performed. For tumor VOIs, 73% of first-order features and 71% of features extracted from the gray level co-occurrence matrix showed high repeatability (ICC 95% confidence interval, 0.91–1.00). In the largest spherical tumor VOIs, 67% of features showed high repeatability, significantly decreasing towards smaller VOIs. The IDH genotype did not affect feature repeatability. Based on 297 repeatable features, two clusters were identified separating patients with IDH-wildtype glioma from those with an IDH mutation. Our results suggest that robust features can be obtained from routinely acquired FET PET scans, which are valuable for further standardization of radiomics analyses in neurooncology.