Cystathionine as a marker for 1p/19q codeleted gliomas by in vivo magnetic resonance spectroscopy

Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy

Molecular genetics is highly related with prognosis of high-grade glioma. Accordingly, the latest WHO guideline recommends that molecular subgroups of the genes, including IDH, 1p/19q, MGMT, TERT, EGFR, Chromosome 7/10, CDKN2A/B, need to be detected to better classify glioma and guide surgery and treatment. Unfortunately, there is no preoperative or intraoperative technology available for accurate and comprehensive molecular subgrouping of glioma. Here, we develop a deep learning-assisted fiber-optic Raman diagnostic platform for accurate and rapid molecular subgrouping of high-grade glioma. Specifically, a total of 2,354 fingerprint Raman spectra was obtained from 743 tissue sites (astrocytoma: 151; oligodendroglioma: 150; glioblastoma (GBM): 442) of 44 high-grade glioma patients. The convolutional neural networks (ResNet) model was then established and optimized for molecular subgrouping. The mean area under receiver operating characteristic curves (AUC) for identifying the molecular subgroups of high-grade glioma reached 0.904, with mean sensitivity of 83.3%, mean specificity of 85.0%, mean accuracy of 83.3%, and mean time expense of 10.6 s. The diagnosis performance using ResNet model was shown to be superior to PCA-SVM and UMAP models, suggesting that high dimensional information from Raman spectra would be helpful. In addition, for the molecular subgroups of GBM, the mean AUC reached 0.932, with mean sensitivity of 87.8%, mean specificity of 83.6%, and mean accuracy of 84.1%. Furthermore, according to saliency maps, the specific Raman features corresponding to tumor-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen) were found to contribute to the accurate molecular subgrouping. Collectively, this study opens up new opportunities for accurate and rapid molecular subgrouping of high-grade glioma, which would assist optimal surgical resection and instant post-operative decision-making.

Use of a Commercial 7-T MRI Scanner for Clinical Brain Imaging: Indications, Protocols, Challenges, and Solutions-A Single-Center Experience

The first commercially available 7-T MRI scanner (Magnetom Terra) was approved by the FDA in 2017 for clinical imaging of the brain and knee. After initial protocol development and sequence optimization efforts in volunteers, the 7-T system, in combination with an FDA-approved 1-channel transmit/32-channel receive array head coil, can now be routinely used for clinical brain MRI examinations. The ultrahigh field strength of 7-T MRI has the advantages of improved spatial resolution, increased SNR, and increased CNR but also introduces an array of new technical challenges. The purpose of this article is to describe an institutional experience with the use of the commercially available 7-T MRI scanner for routine clinical brain imaging. Specific clinical indications for which 7-T MRI may be useful for brain imaging include brain tumor evaluation with possible perfusion imaging and/or spectroscopy, radiotherapy planning; evaluation of multiple sclerosis and other demyelinating diseases, evaluation of Parkinson disease and guidance of deep brain stimulator placement, high-detail intracranial MRA and vessel wall imaging, evaluation of pituitary pathology, and evaluation of epilepsy. Detailed protocols, including sequence parameters, for these various indications are presented, and implementation challenges (including artifacts, safety, and side effects) and potential solutions are explored.

Neurochemical Differences between 1p/19q Codeleted and Noncodeleted IDH-mutant Gliomas by in Vivo MR Spectroscopy

Background Noninvasive identification of glioma subtypes is important for optimizing treatment strategies. Purpose To compare the in vivo neurochemical profiles between isocitrate dehydrogenase (IDH) 1-mutant 1p/19q codeleted gliomas and their noncodeleted counterparts measured by MR spectroscopy at 3.0 T with a point-resolved spectroscopy (PRESS) sequence optimized for D-2-hydroxyglutarate (2HG) detection. Materials and Methods Adults with IDH1-mutant gliomas were retrospectively included for this study from two university hospitals (inclusion period: January 2015 to July 2016 and September 2019 to June 2021, respectively) based on availability of 1p/19q codeletion status and a PRESS acquisition optimized for 2HG detection (echo time, 97 msec) at 3.0 T before any treatment. Spectral analysis was performed using LCModel and a simulated basis set. Metabolite quantification was performed using the water signal as a reference and correcting for water and metabolite longitudinal and transverse relaxation time constants. Concentration ratios were computed using total creatine (tCr) and total choline. A two-tailed unpaired t test was used to compare metabolite concentrations obtained in codeleted versus noncodeleted gliomas, accounting for multiple comparisons. Results Thirty-one adults (mean age, 39 years ± 8 [SD]; 19 male) were included, and 19 metabolites were quantified. Cystathionine concentration was higher in codeleted (n = 13) than noncodeleted (n = 18) gliomas when quantification was performed using the water signal or tCr as references (2.33 mM ± 0.98 vs 0.93 mM ± 0.94, and 0.34 mM ± 0.14 vs 0.14 mM ± 0.14, respectively; both P < .001). The sensitivity and specificity of PRESS to detect codeletion by means of cystathionine quantification were 92% and 61%, respectively. Other metabolites did not show evidence of a difference between groups (P > .05). Conclusion Higher cystathionine levels were detected in IDH1-mutant 1p/19q codeleted gliomas than in their noncodeleted counterparts with use of a PRESS sequence optimized for 2HG detection. Of 19 metabolites quantified, only cystathionine showed evidence of a difference in concentration between groups. Clinical trial registry no. NCT01703962 © RSNA, 2023 See also the editorial by Lin in this issue.

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Cuproptosis-related gene expression is associated with immune infiltration and CD47/CD24 expression in glioblastoma, and a risk score based on these genes can predict the survival and prognosis of patients

Introduction

Glioblastoma (GBM) is the most invasive type of glioma, is insensitive to radiotherapy and chemotherapy, and has high proliferation and invasive ability, with a 5-year survival rate of <5%. Cuproptosis-related genes (CRGs) have been successfully used to predict the prognosis of many types of tumors. However, the relationship between cuproptosis and GBM remains unclear.

Methods

Here, we sought to identify CRGs in GBM and elucidate their role in the tumor immune microenvironment and prognosis. To that aim, changes in CRGs in The Cancer Genome Atlas (TCGA) transcriptional and Gene Expression Omnibus (GEO) datasets (GEO4290 and GEO15824) were characterized, and the expression patterns of these genes were analyzed.

Results

A risk score based on CRG expression characteristics could predict the survival and prognosis of patients with GBM and was significantly associated with immune infiltration levels and the expression of CD47 and CD24, which are immune checkpoints of the "don't eat me "signal. Furthermore, we found that the CDKN2A gene may predict GBM sensitivity and resistance to drugs.

Discussion

Our findings suggest that CRGs play a crucial role in GBM outcomes and provide new insights into CRG-related target drugs/molecules for cancer prevention and treatment.

Advances in molecular and imaging biomarkers in lower-grade gliomas

INTRODUCTION

Lower-grade (grade 2-3) gliomas (LGGs) constitutes a group of primary brain tumors with variable clinical behaviors and treatment responses. Recent advancements in molecular biology have redefined their classification, and novel imaging modalities emerged for the noninvasive diagnosis and follow-up.

AREAS COVERED

This review comprehensively analyses the current knowledge on molecular and imaging biomarkers in LGGs. Key molecular alterations, such as IDH mutations and 1p/19q codeletion, are discussed for their prognostic and predictive implications in guiding treatment decisions. Moreover, the authors explore theranostic biomarkers for the potential of tailored therapies. Additionally, they also describe the utility of advanced imaging modalities, including widely available techniques, as dynamic susceptibility contrast perfusion-weighted imaging and less validated, emerging approaches, for the noninvasive LGGs characterization and follow-up.

EXPERT OPINION

The integration of molecular markers enhanced the stratification of LGGs, leading to the new concept of integrated histomolecular classification. While the IDH mutation is an established key prognostic and predictive marker, recent results from IDH inhibitors trials showed its potential value as a theranostic marker. In this setting, advanced MRI techniques such as 2-D-hydroxyglutarate spectroscopy are very promising for the noninvasive diagnosis and monitoring of LGGs. This progress offers exciting prospects for personalized medicine and improved treatment outcomes in LGGs.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Association between altered metabolism and genetic mutations in human glioma

BACKGROUND

Molecular markers for classification of gliomas include isocitrate dehydrogenase (IDH) mutations and codeletion of chromosomal arms 1p and 19q (1p/19q). While mutations in IDH enzymes result in the well-characterized production of oncometabolite 2-hydroxyglutarate, dysregulation of other metabolites in IDH tumors is less characterized. Similarly, the effects of 1p/19q codeletion on cellular metabolism are also unclear.

AIM

This study aimed to quantify changes in tumor metabolites in human glioma tissue as a function of both IDH mutation and 1p/19q codeletion.

METHODS AND RESULTS

Deidentified human glioma tissue and associated clinical data were obtained from the Emory University Winship Cancer Institute tissue biobank from 14 patients (WHO grades II, III, and IV; seven female and seven male). Proton (¹ H) high-resolution magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy data were acquired using a 600 MHz Bruker AVANCE III NMR spectrometer. Metabolite concentrations were calculated using LCModel. Differences in metabolite concentrations as a function of IDH mutation, 1p/19q codeletion, and survival status were determined using Mann-Whitney U tests. Concentrations of alanine, glutamine, and glutamate were significantly lower in glioma tissue with IDH mutations compared to tissue with IDH wildtype. Additionally, glutamate concentration was significantly lower in glioma tissue with 1p/19q codeletion compared to intact 1p/19q. Exploratory analysis revealed alanine concentration varied significantly as a function of survival status.

CONCLUSIONS

Given the emerging landscape of glioma treatments that target metabolic dysregulation, an improved understanding of altered metabolism in molecular sub-types of gliomas, including those with IDH mutation and 1p/19q codeletion, is an important consideration for treatment stratification and personalized medicine.

Identification of a copper metabolism-related gene signature for predicting prognosis and immune response in glioma

BACKGROUND

Gliomas are highly refractory intracranial cancers characterized by genetic and transcriptional heterogeneity. However, therapeutic options are limited. In the last years, copper-induced cell death is becoming a prospective treatment strategy for gliomas and other solid tumors, but copper metabolism-related genes associated with cancer development remain unclear.

METHODS

We first collected gene expression data from The Cancer Genome Atlas (TCGA) to identify significantly differentially expressed copper metabolism-related genes in gliomas. Using these genes, we performed COX regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct the prognostic model. The prognostic value of the model was further validated by CGGA testing set. Subsequently, functional analyses were carried out, including gene set enrichment analysis (GSEA), immune infiltration analysis, and mutation analysis. Finally, the expression levels of these genes were verified by immunohistochemical analysis.

RESULTS

The prognostic model consisted of 7 genes: CDK1, LOXL2, LOXL3, NFE2L2, SLC31A1, SUMF1 and FDX1. According to this prognosis model, glioma patients could be split into the high-risk group or low-risk group, and the low-risk group showed significantly better prognostic survival (p < 0.001). Moreover, the high-risk group had higher levels of immune cell infiltration, immune checkpoint genes expression, and higher tumor mutational burden (TMB), which indicates that they might benefit more from immunotherapy. Finally, we confirmed the expression level of FDX1, SUMF1, and SLC31A1 protein as significantly different in glioblastoma, lower-grade glioma, and non-tumor brain tissues by immunohistochemical analysis, and the high expression of FDX1 and SLC31A1 protein was related to poor survival in glioma patients.

CONCLUSIONS

Our study could contribute to the prognosis prediction and decision-making in patients with gliomas.

SDHx mutation and pituitary adenoma: can in vivo 1H-MR spectroscopy unravel the link?

Germline mutations in genes encoding succinate dehydrogenase (SDH) are frequently involved in pheochromocytoma/paraganglioma (PPGL) development and were implicated in patients with the '3PAs' syndrome (associating pituitary adenoma (PA) and PPGL) or isolated PA. However, the causality link between SDHx mutation and PA remains difficult to establish, and in vivo tools for detecting hallmarks of SDH deficiency are scarce. Proton magnetic resonance spectroscopy (1H-MRS) can detect succinate in vivo as a biomarker of SDHx mutations in PGL. The objective of this study was to demonstrate the causality link between PA and SDH deficiency in vivo using 1H-MRS as a novel noninvasive tool for succinate detection in PA. Three SDHx-mutated patients suffering from a PPGL and a macroprolactinoma and one patient with an apparently sporadic non-functioning pituitary macroadenoma underwent MRI examination at 3 T. An optimized 1H-MRS semi-LASER sequence (TR = 2500 ms, TE = 144 ms) was employed for the detection of succinate in vivo. Succinate and choline-containing compounds were identified in the MR spectra as single resonances at 2.44 and 3.2 ppm, respectively. Choline compounds were detected in all the tumors (three PGL and four PAs), while a succinate peak was only observed in the three macroprolactinomas and the three PGL of SDHx-mutated patients, demonstrating SDH deficiency in these tumors. In conclusion, the detection of succinate by 1H-MRS as a hallmark of SDH deficiency in vivo is feasible in PA, laying the groundwork for a better understanding of the biological link between SDHx mutations and the development of these tumors.

Accurate prediction of isocitrate dehydrogenase -mutation status of gliomas using SLOW-editing magnetic resonance spectroscopic imaging at 7 T MR

Background

2-hydroxy-glutarate (2HG) is a metabolite that accumulates in isocitrate dehydrogenase (IDH)-mutated gliomas and can be detected noninvasively using MR spectroscopy. However, due to the low concentration of 2HG, established magnetic resonance spectroscopic imaging (MRSI) techniques at the low field have limitations with respect to signal-to-noise and to the spatial resolution that can be obtained within clinically acceptable measurement times. Recently a tailored editing method for 2HG detection at 7 Tesla (7 T) named SLOW-EPSI was developed. The underlying prospective study aimed to compare SLOW-EPSI to established techniques at 7 T and 3 T for IDH-mutation status determination.

Methods

The applied sequences were MEGA-SVS and MEGA-CSI at both field strengths and SLOW-EPSI at 7 T only. Measurements were performed on a MAGNETOM-Terra 7 T MR-scanner in clinical mode using a Nova 1Tx32Rx head coil and on a 3 T MAGNETOM-Prisma scanner with a standard 32-channel head coil.

Results

Fourteen patients with suspected glioma were enrolled. Histopathological confirmation was available in 12 patients. IDH mutation was confirmed in 9 out of 12 cases and 3 cases were characterized as IDH wildtype. SLOW-EPSI at 7 T showed the highest accuracy for IDH-status prediction (91.7% accuracy, 11 of the 12 predictions correct with 1 false negative case). At 7 T, MEGA-CSI had an accuracy of 58.3% and MEGA-SVS had an accuracy of 75%. At 3 T, MEGA-CSI showed an accuracy of 63.6% and MEGA-SVS of 33.3%. The co-edited cystathionine was detected in 2 out of 3 oligodendroglioma cases with 1p/19q codeletion.

Conclusions

Depending on the pulse sequence, spectral editing can be a powerful tool for the noninvasive determination of the IDH status. SLOW-editing EPSI sequence is the preferable pulse sequence when used at 7 T for IDH-status characterization.

Structural and functional imaging of brains

Analyzing the complex structures and functions of brain is the key issue to understanding the physiological and pathological processes. Although neuronal morphology and local distribution of neurons/blood vessels in the brain have been known, the subcellular structures of cells remain challenging, especially in the live brain. In addition, the complicated brain functions involve numerous functional molecules, but the concentrations, distributions and interactions of these molecules in the brain are still poorly understood. In this review, frontier techniques available for multiscale structure imaging from organelles to the whole brain are first overviewed, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), serial-section electron microscopy (ssEM), light microscopy (LM) and synchrotron-based X-ray microscopy (XRM). Specially, XRM for three-dimensional (3D) imaging of large-scale brain tissue with high resolution and fast imaging speed is highlighted. Additionally, the development of elegant methods for acquisition of brain functions from electrical/chemical signals in the brain is outlined. In particular, the new electrophysiology technologies for neural recordings at the single-neuron level and in the brain are also summarized. We also focus on the construction of electrochemical probes based on dual-recognition strategy and surface/interface chemistry for determination of chemical species in the brain with high selectivity and long-term stability, as well as electrochemophysiological microarray for simultaneously recording of electrochemical and electrophysiological signals in the brain. Moreover, the recent development of brain MRI probes with high contrast-to-noise ratio (CNR) and sensitivity based on hyperpolarized techniques and multi-nuclear chemistry is introduced. Furthermore, multiple optical probes and instruments, especially the optophysiological Raman probes and fiber Raman photometry, for imaging and biosensing in live brain are emphasized. Finally, a brief perspective on existing challenges and further research development is provided.

MRSCloud: A cloud-based MRS tool for basis set simulation

PURPOSE

The purpose of this study is to present a cloud-based spectral simulation tool "MRSCloud," which allows MRS users to simulate a vendor-specific and sequence-specific basis set online in a convenient and time-efficient manner. This tool can simulate basis sets for GE, Philips, and Siemens MR scanners, including conventional acquisitions and spectral editing schemes with PRESS and semi-LASER localization at 3 T.

METHODS

The MRSCloud tool was built on the spectral simulation functionality in the FID-A software package. We added three extensions to accelerate computation (ie, one-dimensional projection method, coherence pathways filters, and precalculation of propagators). The RF waveforms were generated based on vendors' generic pulse shapes and timings. Simulations were compared within MRSCloud using different numbers of spatial resolution (21 × 21, 41 × 41, and 101 × 101). Simulated metabolite basis functions from MRSCloud were compared with those generated by the generic FID-A and MARSS, and a phantom-acquired basis set from LCModel. Intraclass correlation coefficients were calculated to measure the agreement between individual metabolite basis functions. Statistical analysis was performed using R in RStudio.

RESULTS

Simulation time for a full PRESS basis set is approximately 11 min on the server. The interclass correlation coefficients ICCs were at least 0.98 between MRSCloud and FID-A and were at least 0.96 between MRSCloud and MARSS. The interclass correlation coefficients between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for glutamine at 0.68 and highest for N-acetylaspartate at 0.96.

CONCLUSIONS

Substantial reductions in runtime have been achieved. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets.

SWI by 7T MR Imaging for the Microscopic Imaging Diagnosis of Astrocytic and Oligodendroglial Tumors

BACKGROUND AND PURPOSE

Despite advances in molecular imaging, preoperative diagnosis of astrocytomas and oligodendrogliomas can be challenging. In the present study, we assessed whether 7T SWI can be used to distinguish astrocytomas and oligodendrogliomas and whether malignant grading of gliomas is possible.

MATERIALS AND METHODS

7T SWI was performed on 21 patients with gliomas before surgery with optimization for sharp visualization of the corticomedullary junction. Scoring for cortical thickening and displacement of medullary vessels, characteristic of oligodendroglial tumors, and cortical tapering, characteristic of astrocytic tumors, was performed. Additionally, characteristics of malignancy, including thickening of the medullary veins, the presence of microbleeds, and/or necrosis were scored.

RESULTS

Scoring for oligodendroglial (highest possible score, +3) and astrocytic (lowest score possible, -3) characteristics yielded a significant difference between astrocytomas and oligodendrogliomas (mean, -1.93 versus +1.71, P < .01). Scoring for malignancy was significantly different among the World Health Organization grade II (n = 10), grade III (n = 4), and grade IV (n = 7) tumors (mean, 0.20 versus 1.38 versus 2.79). Cortical thickening was observed significantly more frequently in oligodendrogliomas (P < .02), with a sensitivity of 71.4% and specificity of 85.7%; observation of tapering of the cortex was higher in astrocytomas (P < .01) with a sensitivity of 85.7% and specificity of 100%.

CONCLUSIONS

Visualization of the corticomedullary junction by 7T SWI was useful in distinguishing astrocytomas and oligodendrogliomas. Observation of tapering of the cortex was most sensitive and specific for diagnosing astrocytomas. Reliably predicting malignant grade was also possible by 7T SWI.

Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning

Simple Summary

The enzyme isocitrate dehydrogenase (IDH) affects glioma cell metabolism in multiple ways. Mutation of IDH is not only indicative of the presence of astrocytoma or oligodendroglioma but it also comes with a better prognosis and constitutes a promising therapeutic target. Therefore, determination of IDH mutation status is essential in clinical practice. In most patients, tissue can be obtained by resection or biopsy to determine IDH status histologically. However, in some cases, this is not possible for technical reasons. We recently showed in a small cohort of patients that non-invasive determination of IDH mutation status using proton magnetic resonance spectroscopy (¹H-MRS) at 3.0 Tesla (T) together with machine learning techniques is feasible in a standard clinical setting and with acceptable effort. Here, we demonstrate that our approach showed comparably good results in sensitivity (82.6%) and specificity (72.7%) in a larger validation cohort employing ¹H-MRS at 1.5 T in a retrospective, distinct setting. We concluded that our method works well regardless of the magnetic field strength and scanner used, and thus, may improve patient care.

Abstract

The isocitrate dehydrogenase (IDH) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for this purpose. In clinical routine, however, non-invasive determination of IDH mutation status is desirable in cases where tumor biopsy is not possible and for monitoring neuro-oncological therapies. In a previous publication, we presented reliable prediction of IDH mutation status employing proton magnetic resonance spectroscopy (¹H-MRS) on a 3.0 Tesla (T) scanner and machine learning in a prospective cohort of 34 glioma patients. Here, we validated this approach in an independent cohort of 67 patients, for which ¹H-MR spectra were acquired at 1.5 T between 2002 and 2007, using the same data analysis approach. Despite different technical conditions, a sensitivity of 82.6% (95% CI, 61.2–95.1%) and a specificity of 72.7% (95% CI, 57.2–85.0%) could be achieved. We concluded that our ¹H-MRS based approach can be established in a routine clinical setting with affordable effort and time, independent of technical conditions employed. Therefore, the method provides a non-invasive tool for determining IDH status that is well-applicable in an everyday clinical setting.

In vivo brain MR spectroscopy in gliomas: clinical and pre-clinical chances

Purpose

Gliomas, the most common primary brain tumours, have recently been re-classified incorporating molecular aspects with important clinical, prognostic, and predictive implications. Concurrently, the reprogramming of metabolism, altering intracellular and extracellular metabolites affecting gene expression, differentiation, and the tumour microenvironment, is increasingly being studied, and alterations in metabolic pathways are becoming hallmarks of cancer. Magnetic resonance spectroscopy (MRS) is a complementary, non-invasive technique capable of quantifying multiple metabolites. The aim of this review focuses on the methodology and analysis techniques in proton MRS (1H MRS), including a brief look at X-nuclei MRS, and on its perspectives for diagnostic and prognostic biomarkers in gliomas in both clinical practice and preclinical research.

Methods

PubMed literature research was performed cross-linking the following key words: glioma, MRS, brain, in-vivo, human, animal model, clinical, pre-clinical, techniques, sequences, 1H, X-nuclei, Artificial Intelligence (AI), hyperpolarization.

Results

We selected clinical works (n = 51), preclinical studies (n = 35) and AI MRS application papers (n = 15) published within the last two decades. The methodological papers (n = 62) were taken into account since the technique first description.

Conclusions

Given the development of treatments targeting specific cancer metabolic pathways, MRS could play a key role in allowing non-invasive assessment for patient diagnosis and stratification, predicting and monitoring treatment responses and prognosis. The characterization of gliomas through MRS will benefit of a wide synergy among scientists and clinicians of different specialties within the context of new translational competences. Head coils, MRI hardware and post-processing analysis progress, advances in research, experts’ consensus recommendations and specific professionalizing programs will make the technique increasingly trustworthy, responsive, accessible.

The influence of cystathionine on neurochemical quantification in brain tumor in vivo MR spectroscopy

PURPOSE

To evaluate the ability of the PRESS sequence (T_E  = 97 ms, optimized for 2-hydroxyglutarate detection) to detect cystathionine in gliomas and the effect of the omission of cystathionine on the quantification of the full neurochemical profile.

METHODS

Twenty-three subjects with a glioma were retrospectively included based on the availability of both MEGA-PRESS and PRESS acquisitions at 3T, and the presence of the cystathionine signal in the edited MR spectrum. In eight subjects, the PRESS acquisition was performed also in normal tissue. Metabolite quantification was performed using LCModel and simulated basis sets. The LCModel analysis for the PRESS data was performed with and without cystathionine.

RESULTS

All subjects with glioma had detectable cystathionine levels >1 mM with Cramér-Rao lower bounds (CRLB) <15%. The mean cystathionine concentrations were 3.49 ± 1.17 mM for MEGA-PRESS and 2.20 ± 0.80 mM for PRESS data. Cystathionine concentrations showed a significant correlation between the two MRS methods (r = 0.58, p = .004), and it was not detectable in normal tissue. Using PRESS, 19 metabolites were quantified with CRLB <50% for more than half of the subjects. The metabolites that were significantly (p < .0028) and mostly affected by the omission of cystathionine were aspartate, betaine, citrate, γ-aminobutyric acid (GABA), and serine.

CONCLUSIONS

Cystathionine was detectable by PRESS in all the selected gliomas, while it was not detectable in normal tissue. The omission from the spectral analysis of cystathionine led to severe biases in the quantification of other neurochemicals that may play key roles in cancer metabolism.

Proteomics and metabolomics approach in adult and pediatric glioma diagnostics

The diagnosis of glioma is mainly based on imaging methods that do not distinguish between stage and subtype prior to histopathological analysis. Patients with gliomas are generally diagnosed in the symptomatic stage of the disease. Additionally, healing scar tissue may be mistakenly identified based on magnetic resonance imaging (MRI) as a false positive tumor recurrence in postoperative patients. Current knowledge of molecular alterations underlying gliomagenesis and identification of tumoral biomarkers allow for their use as discriminators of the state of the organism. Moreover, a multiomics approach provides the greatest spectrum and the ability to track physiological changes and can serve as a minimally invasive method for diagnosing asymptomatic gliomas, preceding surgery and allowing for the initiation of prophylactic treatment. It is important to create a vast biomarker library for adults and pediatric patients due to their metabolic differences. This review focuses on the most promising proteomic, metabolomic and lipidomic glioma biomarkers, their pathways, the interactions, and correlations that can be considered characteristic of tumor grade or specific subtype.

Overexpression of SKA Complex Is Associated With Poor Prognosis in Gliomas

The spindle and kinetochore-associated complex is composed of three members: SKA1, SKA2, and SKA3. It is necessary for stabilizing spindle microtubules attaching to kinetochore (KT) in the middle stage of mitosis. The SKA complex is associated with poor prognosis in several human cancers. However, the role of SKA complex in rare malignant diseases, such as gliomas, has not been fully investigated. We investigated several databases, including Oncomine, UALCAN, and cBioPortal to explore the expression profile and prognostic significance of SKA complex in patients with gliomas. Gene ontology and Kyoto Encyclopedia of Genes and Genome pathways were used to analyze the potential enriched pathways. The genes co-expressed with SKA complex were identified and used for developing a protein-protein interaction (PPI) network using the STRING database. We found a significant overexpression of the mRNA levels of SKA1, SKA2, and SKA3 in patients with glioma patients. Higher expression of SKA1 and SKA3, but not SKA2, was significantly correlated with shorter overall survival of patients with glioma. In glioma, SKA complex was found to be involved in nuclear division, chromosome segregation, and DNA replication. The results of PPI network identified 10 hub genes (CCNB2, UBE2C, BUB1B, TPX2, CCNA2, CCNB1, MELK, TOP2A, PBK, and KIF11), all of which were overexpressed and negatively associated with prognosis of patients with glioma. In conclusion, our study sheds new insights into the biological role and prognostic significance of SKA complex in glioma.

Metabolomic Phenotyping of Gliomas: What Can We Get with Simplified Protocol for Intact Tissue Analysis?

Glioblastoma multiforme is one of the most malignant neoplasms among humans in their third and fourth decades of life, which is evidenced by short patient survival times and rapid tumor-cell proliferation after radiation and chemotherapy. At present, the diagnosis of gliomas and decisions related to therapeutic strategies are based on genetic testing and histological analysis of the tumor, with molecular biomarkers still being sought to complement the diagnostic panel. This work aims to enable the metabolomic characterization of cancer tissue and the discovery of potential biomarkers via high-resolution mass spectrometry coupled to liquid chromatography and a solvent-free sampling protocol that uses a microprobe to extract metabolites directly from intact tumors. The metabolomic analyses were performed independently from genetic and histological testing and at a later time. Despite the small cohort analyzed in this study, the results indicated that the proposed method is able to identify metabolites associated with different malignancy grades of glioma, as well as IDH and 1p19q codeletion mutations. A comparison of the constellation of identified metabolites and the results of standard tests indicated the validity of using the characterization of one comprehensive tumor phenotype as a reflection of all diagnostically meaningful information. Due to its simplicity, the proposed analytical approach was verified as being compatible with a surgical environment and applicable for large-scale studies.

Tissue metabolites in diffuse glioma and their modulations by IDH1 mutation, histology and treatment

The discovery of the oncometabolite 2-hydroxyglutarate in isocitrate dehydrogenase 1–mutated (IDH1-mutated) tumor entities affirmed the role of metabolism in cancer. However, large databases with tissue metabolites that are modulated by IDH1 mutation remain an area of development. Here, we present an unprecedented and valuable resource for tissue metabolites in diffuse glioma and their modulations by IDH1 mutation, histology, and tumor treatments in 101 tissue samples from 73 diffuse glioma patients (24 astrocytoma, 17 oligodendroglioma, 32 glioblastoma), investigated by NMR-based metabolomics and supported by RNA-Seq. We discovered comparison-specific metabolites and pathways modulated by IDH1 (IDH1 mutation status cohort) and tumor entity. The Longitudinal investigation cohort provides metabolic profiles of untreated and corresponding treated glioma samples at first progression. Most interestingly, univariate and multivariate cox regressions and Kaplan-Meier analyses revealed that tissue metabolites correlate with progression-free and overall survival. Thus, this study introduces potentially novel candidate prognostic and surrogate metabolite biomarkers for future prospective clinical studies, aiming at further refining patient stratification in diffuse glioma. Furthermore, our data will facilitate the generation of so-far–unanticipated hypotheses for experimental studies to advance our molecular understanding of glioma biology.

Imaging Advances for Central Nervous System Tumors

This article reviews recent advances in the use of standard and advanced imaging techniques for diagnosis and treatment of central nervous system (CNS) tumors, including glioma and brain metastasis. Following the recent transition from a histology-based approach in classifying CNS tumors to one that integrates histology with the molecular information of tumor, the approaches for imaging CNS tumors have also been adapted to this new framework. Some challenges related to the diagnosis and treatment of CNS tumors, such as differentiating tumor from treatment-related imaging changes, require further progress to implement advanced imaging for clinical use.

Comparison of 2-Hydroxyglutarate Detection With sLASER and MEGA-sLASER at 7T

The onco-metabolite 2-hydroxyglutarate (2HG), a biomarker of IDH-mutant gliomas, can be detected with ¹H MR spectroscopy (¹H-MRS). Recent studies showed measurements of 2HG at 7T with substantial gain in signal to noise ratio (SNR) and spectral resolution, offering higher specificity and sensitivity for 2HG detection. In this study, we assessed the sensitivity of semi-localized by adiabatic selective refocusing (sLASER) and J-difference MEsher-GArwood-semi-LASER (MEGA-sLASER) for 2HG detection at 7T. We performed spectral editing at long TE using a TE-optimized sLASER sequence (110 ms) and J-difference spectroscopy using MEGA-sLASER (TE = 74ms) in phantoms with different 2HG concentrations to assess the sensitivity of 2HG detection. The robustness of the methods against B₀ inhomogeneity was investigated. Moreover, the performance of these two techniques was evaluated in four patients with IDH1-mutated glioma. In contrary to MEGA-sLASER, sLASER was able to detect 2HG concentration as low as 0.5 mM. In case of a composite phantom containing 2HG with overlapping metabolites, MEGA-sLASER provided a clean 2HG signal with higher fitting reliability (lower %CRLB). The results demonstrate that sLASER is more robust against field inhomogeneities and experimental or motion-related artifacts which promotes to adopt sLASER in clinical implementations.

Hyperpolarized MRI, functional MRI, MR spectroscopy and CEST to provide metabolic information in vivo

Access to metabolic information in vivo using magnetic resonance (MR) technologies has generally been the niche of MR spectroscopy (MRS) and spectroscopic imaging (MRSI). Metabolic fluxes can be studied using the infusion of substrates labeled with magnetic isotopes, with the use of hyperpolarization especially powerful. Unfortunately, these promising methods are not yet accepted clinically, where fast, simple, and reliable measurement and diagnosis are key. Recent advances in functional MRI and chemical exchange saturation transfer (CEST) MRI allow the use of water imaging to study oxygen metabolism and tissue metabolite levels. These, together with the use of novel data analysis approaches such as machine learning for all of these metabolic MR approaches, are increasing the likelihood of their clinical translation.

MRI biomarkers in neuro-oncology

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.

Spectral editing in 1 H magnetic resonance spectroscopy: Experts' consensus recommendations

Spectral editing in in vivo ¹ H-MRS provides an effective means to measure low-concentration metabolite signals that cannot be reliably measured by conventional MRS techniques due to signal overlap, for example, γ-aminobutyric acid, glutathione and D-2-hydroxyglutarate. Spectral editing strategies utilize known J-coupling relationships within the metabolite of interest to discriminate their resonances from overlying signals. This consensus recommendation paper provides a brief overview of commonly used homonuclear editing techniques and considerations for data acquisition, processing and quantification. Also, we have listed the experts' recommendations for minimum requirements to achieve adequate spectral editing and reliable quantification. These include selecting the right editing sequence, dealing with frequency drift, handling unwanted coedited resonances, spectral fitting of edited spectra, setting up multicenter clinical trials and recommending sequence parameters to be reported in publications.

The role of 2-hydroxyglutarate magnetic resonance spectroscopy for the determination of isocitrate dehydrogenase status in lower grade gliomas versus glioblastoma: a systematic review and meta-analysis of diagnostic test accuracy

PURPOSE

Magnetic resonance spectroscopy (MRS) provides a non-invasive means of determining isocitrate dehydrogenase (IDH) status. Determination of 2-hydroxyglutarate (2-HG) presence through MRS is a means of determining IDH status; however, differences may be seen by grade. The goal of this paper is to perform a diagnostic test accuracy (DTA) meta-analysis on 2-HG MRS for IDH status in both lower-grade glioma (LGG) and glioblastoma (GBM) in preoperative patients.

METHODS

A systematic review and meta-analysis were performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Diagnostic Test Accuracy guidelines. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies 2. The search was up to date as of 09/02/2021. Nine English-language journal articles were included.

RESULTS

The meta-analysis found a pooled sensitivity of 93% (95% CI 58-99%) and specificity of 84% (95% CI 51-96%) for LGG (n= 181). For GBM (n= 77), the pooled sensitivity was 84% (95% CI 25.0-99%) and the specificity was 97% (95% CI 43-100%).

CONCLUSION

2-HG MRS shows promise as a non-invasive means of determining IDH status, with specificity higher for GBM and sensitivity higher for LGG. The wide confidence intervals are notable, however, in particular related to the small number of IDH-mutant GBM studied. Diagnostic heterogeneity was particularly present for LGG, and the hierarchical summary receiver operator curves showed poor predictive accuracy in both groups. For more conclusive results, diagnostic test accuracy statistics need to be quantified with larger studies and more deliberate study design.

A retrospective overview of PHGDH and its inhibitors for regulating cancer metabolism

Emerging evidence suggests that cancer metabolism is closely associated to the serine biosynthesis pathway (SSP), in which glycolytic intermediate 3-phosphoglycerate is converted to serine through a three-step enzymatic transformation. As the rate-limiting enzyme in the first step of SSP, phosphoglycerate dehydrogenase (PHGDH) is overexpressed in various diseases, especially in cancer. Genetic knockdown or silencing of PHGDH exhibits obvious anti-tumor response both in vitro and in vivo, demonstrating that PHGDH is a promising drug target for cancer therapy. So far, several types of PHGDH inhibitors have been identified as a significant and newly emerging option for anticancer treatment. Herein, this comprehensive review summarizes the recent achievements of PHGDH, especially its critical role in cancer and the development of PHGDH inhibitors in drug discovery.

Magnetic resonance spectroscopy for the study of cns malignancies

Despite intensive research, brain tumors are amongst the malignancies with the worst prognosis; therefore, a prompt diagnosis and thoughtful assessment of the disease is required. The resistance of brain tumors to most forms of conventional therapy has led researchers to explore the underlying biology in search of new vulnerabilities and biomarkers. The unique metabolism of brain tumors represents one potential vulnerability and the basis for a system of classification. Profiling this aberrant metabolism requires a method to accurately measure and report differences in metabolite concentrations. Magnetic resonance-based techniques provide a framework for examining tumor tissue and the evolution of disease. Nuclear Magnetic Resonance (NMR) analysis of biofluids collected from patients suffering from brain cancer can provide biological information about disease status. In particular, urine and plasma can serve to monitor the evolution of disease through the changes observed in the metabolic profiles. Moreover, cerebrospinal fluid can be utilized as a direct reporter of cerebral activity since it carries the chemicals exchanged with the brain tissue and the tumor mass. Metabolic reprogramming has recently been included as one of the hallmarks of cancer. Accordingly, the metabolic rewiring experienced by these tumors to sustain rapid growth and proliferation can also serve as a potential therapeutic target. The combination of 13C tracing approaches with the utilization of different NMR spectral modalities has allowed investigations of the upregulation of glycolysis in the aggressive forms of brain tumors, including glioblastomas, and the discovery of the utilization of acetate as an alternative cellular fuel in brain metastasis and gliomas. One of the major contributions of magnetic resonance to the assessment of brain tumors has been the non-invasive determination of 2-hydroxyglutarate (2HG) in tumors harboring a mutation in isocitrate dehydrogenase 1 (IDH1). The mutational status of this enzyme already serves as a key feature in the clinical classification of brain neoplasia in routine clinical practice and pilot studies have established the use of in vivo magnetic resonance spectroscopy (MRS) for monitoring disease progression and treatment response in IDH mutant gliomas. However, the development of bespoke methods for 2HG detection by MRS has been required, and this has prevented the wider implementation of MRS methodology into the clinic. One of the main challenges for improving the management of the disease is to obtain an accurate insight into the response to treatment, so that the patient can be promptly diverted into a new therapy if resistant or maintained on the original therapy if responsive. The implementation of 13C hyperpolarized magnetic resonance spectroscopic imaging (MRSI) has allowed detection of changes in tumor metabolism associated with a treatment, and as such has been revealed as a remarkable tool for monitoring response to therapeutic strategies. In summary, the application of magnetic resonance-based methodologies to the diagnosis and management of brain tumor patients, in addition to its utilization in the investigation of its tumor-associated metabolic rewiring, is helping to unravel the biological basis of malignancies of the central nervous system.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Non-invasive assessment of telomere maintenance mechanisms in brain tumors

Telomere maintenance is a universal hallmark of cancer. Most tumors including low-grade oligodendrogliomas use telomerase reverse transcriptase (TERT) expression for telomere maintenance while astrocytomas use the alternative lengthening of telomeres (ALT) pathway. Although TERT and ALT are hallmarks of tumor proliferation and attractive therapeutic targets, translational methods of imaging TERT and ALT are lacking. Here we show that TERT and ALT are associated with unique 1H-magnetic resonance spectroscopy (MRS)-detectable metabolic signatures in genetically-engineered and patient-derived glioma models and patient biopsies. Importantly, we have leveraged this information to mechanistically validate hyperpolarized [1-13C]-alanine flux to pyruvate as an imaging biomarker of ALT status and hyperpolarized [1-13C]-alanine flux to lactate as an imaging biomarker of TERT status in low-grade gliomas. Collectively, we have identified metabolic biomarkers of TERT and ALT status that provide a way of integrating critical oncogenic information into non-invasive imaging modalities that can improve tumor diagnosis and treatment response monitoring.

Cystathionine-γ-lyase drives antioxidant defense in cysteine-restricted IDH1-mutant astrocytomas

BACKGROUND

Mutations in isocitrate dehydrogenase 1 or 2 (IDH1/2) define glioma subtypes and are considered primary events in gliomagenesis, impacting tumor epigenetics and metabolism. IDH enzyme activity is crucial for the generation of reducing potential in normal cells, yet the impact of the mutation on the cellular antioxidant system in glioma is not understood. The aim of this study was to determine how glutathione (GSH), the main antioxidant in the brain, is maintained in IDH1-mutant gliomas, despite an altered NADPH/NADP balance.

METHODS

Proteomics, metabolomics, metabolic tracer studies, genetic silencing, and drug targeting approaches in vitro and in vivo were applied. Analyses were done in clinical specimen of different glioma subtypes, in glioma patient-derived cell lines carrying the endogenous IDH1 mutation and corresponding orthotopic xenografts in mice.

RESULTS

We find that cystathionine-γ-lyase (CSE), the enzyme responsible for cysteine production upstream of GSH biosynthesis, is specifically upregulated in IDH1-mutant astrocytomas. CSE inhibition sensitized these cells to cysteine depletion, an effect not observed in IDH1 wild-type gliomas. This correlated with an increase in reactive oxygen species and reduced GSH synthesis. Propargylglycine (PAG), a brain-penetrant drug specifically targeting CSE, led to delayed tumor growth in mice.

CONCLUSIONS

We show that IDH1-mutant astrocytic gliomas critically rely on NADPH-independent de novo GSH synthesis via CSE to maintain the antioxidant defense, which highlights a novel metabolic vulnerability that may be therapeutically exploited.

Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer

In vivo magnetic resonance spectroscopy (MRS) can provide clinically valuable metabolic information from brain tumors that can be used for prognosis and monitoring response to treatment. Unfortunately, this technique has not been widely adopted in clinical practice or even clinical trials due to the difficulty in acquiring and analyzing the data. In this work we propose a computational approach to solve one of the most critical technical challenges: the problem of quickly and accurately positioning an MRS volume of interest (a cuboid voxel) inside a tumor using MR images for guidance. The proposed automated method comprises a convolutional neural network to segment the lesion, followed by a discrete optimization to position an MRS voxel optimally within the lesion. In a retrospective comparison, the novel automated method is shown to provide improved lesion coverage compared to manual voxel placement.

Cerebrospinal fluid ctDNA and metabolites are informative biomarkers for the evaluation of CNS germ cell tumors

Serum and cerebrospinal fluid (CSF) levels of α-fetoprotein and β-subunit of human chorionic gonadotropin are used as biomarkers for the management of central nervous system (CNS) germ cell tumors (GCTs). However, additional discriminating biomarkers are required. Especially, biomarkers to differentiate non-germinomatous germ cell tumors (NGGCTs) from germinomas are critical, as these have a distinct prognosis. We investigated CSF samples from 12 patients with CNS-GCT patients (8 germinomas and 4 NGGCTs). We analyzed circulating tumor DNA (ctDNA) in CSF to detect mutated genes. We also used liquid chromatography-mass spectrometry to characterize metabolites in CSF. We detected KIT and/or NRAS mutation, known as frequently mutated genes in GCTs, in 3/12 (25%) patients. We also found significant differences in the abundance of 15 metabolites between control and GCT, with unsupervised hierarchical clustering analysis. Metabolites related to the TCA cycle were increased in GCTs. Urea, ornithine, and short-chain acylcarnitines were decreased in GCTs. Moreover, we also detected several metabolites (e.g., betaine, guanidine acetic acid, and 2-aminoheptanoic acid) that displayed significant differences in abundance in patients with germinomas and NGGCTs. Our results suggest that ctDNA and metabolites in CSF can serve as novel biomarkers for CNS-GCTs and can be useful to differentiate germinomas from NGGCTs.

Magnetic resonance spectroscopy of isocitrate dehydrogenase mutated gliomas: current knowledge on the neurochemical profile

PURPOSE OF REVIEW

Magnetic resonance spectroscopy (MRS) may play a key role for the management of patients with glioma. We highlighted the utility of MRS in the noninvasive diagnosis of gliomas with mutations in isocitrate dehydrogenase (IDH) genes, by providing an overview of the neurochemical alterations observed in different glioma subtypes, as well as during treatment and progression, both in vivo and ex vivo.

RECENT FINDINGS

D-2-hydroxyglutarate (2HG) decrease during anticancer treatments was recently shown to be associated with altered levels of other metabolites, including lactate, glutamate and glutathione, suggesting that tumour treatment leads to a metabolic reprogramming beyond 2HG depletion. In combination with 2HG quantification, cystathionine and glycine seem to be the most promising candidates for higher specific identification of glioma subtypes and follow-up of disease progression and response to treatment.

SUMMARY

The implementation of advanced MRS methods in the routine clinical practice will allow the quantification of metabolites that are not detectable with conventional methods and may enable immediate, accurate diagnosis of gliomas, which is crucial for planning optimal therapeutic strategies and follow-up examinations. The role of different metabolites as predictors of patient outcome still needs to be elucidated.

Human IDH mutant 1p/19q co-deleted gliomas have low tumor acidity as evidenced by molecular MRI and PET: a retrospective study

Co-deletion of 1p/19q is a hallmark of oligodendroglioma and predicts better survival. However, little is understood about its metabolic characteristics. In this study, we aimed to explore the extracellular acidity of WHO grade II and III gliomas associated with 1p/19q co-deletion. We included 76 glioma patients who received amine chemical exchange saturation transfer (CEST) imaging at 3 T. Magnetic transfer ratio asymmetry (MTR_asym) at 3.0 ppm was used as the pH-sensitive CEST biomarker, with higher MTR_asym indicating lower pH. To control for the confounder factors, T₂ relaxometry and l-6-¹⁸F-fluoro-3,4-dihydroxyphenylalnine (¹⁸F-FDOPA) PET data were collected in a subset of patients. We found a significantly lower MTR_asym in 1p/19q co-deleted gliomas (co-deleted, 1.17% ± 0.32%; non-co-deleted, 1.72% ± 0.41%, P = 1.13 × 10⁻⁷), while FDOPA (P = 0.92) and T₂ (P = 0.61) were not significantly affected. Receiver operating characteristic analysis confirmed that MTR_asym could discriminate co-deletion status with an area under the curve of 0.85. In analysis of covariance, 1p/19q co-deletion status was the only significant contributor to the variability in MTR_asym when controlling for age and FDOPA (P = 2.91 × 10⁻³) or T₂ (P = 8.03 × 10⁻⁶). In conclusion, 1p/19q co-deleted gliomas were less acidic, which may be related to better prognosis. Amine CEST-MRI may serve as a non-invasive biomarker for identifying 1p/19q co-deletion status.

A metabolomic data fusion approach to support gliomas grading

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T₂ -filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.

Determinants of nutrient limitation in cancer

Proliferation requires that cells accumulate sufficient biomass to grow and divide. Cancer cells within tumors must acquire a variety of nutrients, and tumor growth slows or stops if necessary metabolites are not obtained in sufficient quantities. Importantly, the metabolic demands of cancer cells can be different from those of untransformed cells, and nutrient accessibility in tumors is different than in many normal tissues. Thus, cancer cell survival and proliferation may be limited by different metabolic factors than those that are necessary to maintain noncancerous cells. Understanding the variables that dictate which nutrients are critical to sustain tumor growth may identify vulnerabilities that could be used to treat cancer. This review examines the various cell-autonomous, local, and systemic factors that determine which nutrients are limiting for tumor growth.

In vivo 1 H MRS detection of cystathionine in human brain tumors

PURPOSE

To report the technical aspects of noninvasive detection of cystathionine in human brain glioma with edited MRS, and to investigate possible further acquisition improvements for robust quantification of this metabolite.

METHODS

In vivo ¹ H MR spectra were acquired at 3 T in 15 participants with an isocitrate dehydrogenase-mutated glioma using a MEGA-PRESS (MEscher GArwood point resolved spectroscopy) sequence previously employed for 2-hydroxyglutarate detection (T_R = 2 s, T_E = 68 ms). The editing pulse was applied at 1.9 ppm for the edit-on condition and at 7.5 ppm for the edit-off condition. To evaluate the editing efficiency, spectra were acquired in 1 participant by placing the editing pulse for the edit-on condition at 1.9, 2.03, and 2.16 ppm. Cystathionine concentration was quantified using LCModel and a simulated basis set. To confirm chemical shifts and J-coupling values of cystathionine, the ¹ H NMR cystathionine spectrum was measured using a high-resolution 500 MHz spectrometer.

RESULTS

In 12 gliomas, cystathionine was observed in the in vivo edited MR spectra at 2.72 and 3.85 ppm and quantified. The signal intensity of the cystathionine resonance at 2.72 ppm increased 1.7 and 2.13 times when the editing pulse was moved to 2.03 and 2.16 ppm, respectively. Cystathionine was not detectable in normal brain tissue.

CONCLUSION

Cystathionine can be detected in vivo by edited MRS using the same protocol as for 2-hydroxyglutarate detection. This finding may enable a more accurate, noninvasive investigation of cellular metabolism in glioma.

Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy

BACKGROUND

The molecular genetic classification of gliomas, particularly the identification of isocitrate dehydrogenase (IDH) mutations, is critical for clinical and surgical decision-making. Raman spectroscopy probes the unique molecular vibrations of a sample to accurately characterize its molecular composition. No sample processing is required allowing for rapid analysis of tissue. The aim of this study was to evaluate the ability of Raman spectroscopy to rapidly identify the common molecular genetic subtypes of diffuse glioma in the neurosurgical setting using fresh biopsy tissue. In addition, classification models were built using cryosections, formalin-fixed paraffin-embedded (FFPE) sections and LN-18 (IDH-mutated and wild-type parental cell) glioma cell lines.

METHODS

Fresh tissue, straight from neurosurgical theatres, underwent Raman analysis and classification into astrocytoma, IDH-wild-type; astrocytoma, IDH-mutant; or oligodendroglioma. The genetic subtype was confirmed on a parallel section using immunohistochemistry and targeted genetic sequencing.

RESULTS

Fresh tissue samples from 62 patients were collected (36 astrocytoma, IDH-wild-type; 21 astrocytoma, IDH-mutated; 5 oligodendroglioma). A principal component analysis fed linear discriminant analysis classification model demonstrated 79%-94% sensitivity and 90%-100% specificity for predicting the 3 glioma genetic subtypes. For the prediction of IDH mutation alone, the model gave 91% sensitivity and 95% specificity. Seventy-nine cryosections, 120 FFPE samples, and LN18 cells were also successfully classified. Meantime for Raman data collection was 9.5 min in the fresh tissue samples, with the process from intraoperative biopsy to genetic classification taking under 15 min.

CONCLUSION

These data demonstrate that Raman spectroscopy can be used for the rapid, intraoperative, classification of gliomas into common genetic subtypes.