Mutations in the isocitrate dehydrogenase genes (IDH1/2) occur often in diffuse gliomas, where they are associated with abnormal accumulation of the oncometabolite 2-hydroxyglutarate (2-HG). Monitoring 2-HG levels could provide prognostic information in this disease, but detection strategies that are noninvasive and sufficiently quantitative have yet to be developed. In this study, we address this need by presenting a proton magnetic resonance spectroscopy (1H-MRS) acquisition scheme that uses an ultrahigh magnetic field (≥7T) capable of noninvasively detecting 2-HG with quantitative measurements sufficient to differentiate mutant cytosolic IDH1 and mitochondrial IDH2 in human brain tumors. Untargeted metabolomics analysis of in vivo1H-MRS spectra discriminated between IDH-mutant tumors and healthy tissue, and separated IDH1 from IDH2 mutations. High-quality spectra enabled the quantification of neurochemical profiles consisting of at least eight metabolites, including 2-HG, glutamate, lactate, and glutathione in both tumor and healthy tissue voxels. Notably, IDH2 mutation produced more 2-HG than IDH1 mutation, consistent with previous findings in cell culture. By offering enhanced sensitivity and specificity, this scheme can quantitatively detect 2-HG and associated metabolites that may accumulate during tumor progression, with implications to better monitor patient responses to therapy. Cancer Res; 76(1); 43–49. ©2015 AACR.

Mutations in isocitrate dehydrogenase (IDH) 1 and 2 occur in over 80% of low-grade gliomas and secondary glioblastomas (1). Wild-type IDH catalyzes the conversion of isocitrate to α-ketoglutarate (α-KG); IDH1 (cytosolic) and IDH2 (mitochondrial)-mutant tumors accumulate 2-hydroxyglutarate (2-HG) as a result of a neomorphic IDH activity, which additionally catalyses reduction of α-KG to give 2-HG (Fig. 1A; refs. 2, 3). The role of 2-HG in gliomagenesis is uncertain, but 2-HG is recognized as a tumor-specific biomarker and a potential target for pharmacologic intervention (4). It is proposed that different subtypes of IDH mutations might be distinguished on the basis of their characteristic neurochemical profiles.

Figure 1.

General layout of 2-HG accumulations and its detection by semi-LASER 1H MRS at 7T. A, tumors with IDH1/2 mutations (mIDH1/2) produce 2-HG in mitochondria and the cytosol. B, a diagram of the “in vivo1H-MRS pulse sequence using adiabatic slice selective refocusing pulses (semi-LASER; ref. 11) optimized for 2-HG signal detection. The semi-LASER sequence consists of a 90° excitation pulse (6 ms) followed by two couples of adiabatic refocusing pulses (each 6 ms). Gx, Gy, and Gz are gradients in read, phase encoding, and slice selection directions, respectively. B1, RF pulses. C, by using GAMMA/PyGAMMA simulation library of VESPA (14) to carry out the density matrix formalism, time delays between the RF pulses are tuned to optimum 2-HG detection. At the echo time (TE) = 110 ms (TE1 = 11 ms, TE2 = 65 ms, and TE3 = 34 ms), one of the multiplets of 2-HG at approximately 2.25 ppm (H4, H4′) was fully absorptive with a negative spectral pattern. D, phantom spectra of 2-HG, Gln, Glu, NAA, and Gly obtained with semi-LASER at TE = 36 (TE1 = 11 ms, TE2 = 15 ms, and TE3 = 10 ms) and 110 ms, together with LCModel (16) fitting and corresponding CRLBs. (TR = 5,000 ms; number of transients, 128; VOI, 8 mL). Phantom spectra were line broadened to match line widths encountered in vivo.

Figure 1.

General layout of 2-HG accumulations and its detection by semi-LASER 1H MRS at 7T. A, tumors with IDH1/2 mutations (mIDH1/2) produce 2-HG in mitochondria and the cytosol. B, a diagram of the “in vivo1H-MRS pulse sequence using adiabatic slice selective refocusing pulses (semi-LASER; ref. 11) optimized for 2-HG signal detection. The semi-LASER sequence consists of a 90° excitation pulse (6 ms) followed by two couples of adiabatic refocusing pulses (each 6 ms). Gx, Gy, and Gz are gradients in read, phase encoding, and slice selection directions, respectively. B1, RF pulses. C, by using GAMMA/PyGAMMA simulation library of VESPA (14) to carry out the density matrix formalism, time delays between the RF pulses are tuned to optimum 2-HG detection. At the echo time (TE) = 110 ms (TE1 = 11 ms, TE2 = 65 ms, and TE3 = 34 ms), one of the multiplets of 2-HG at approximately 2.25 ppm (H4, H4′) was fully absorptive with a negative spectral pattern. D, phantom spectra of 2-HG, Gln, Glu, NAA, and Gly obtained with semi-LASER at TE = 36 (TE1 = 11 ms, TE2 = 15 ms, and TE3 = 10 ms) and 110 ms, together with LCModel (16) fitting and corresponding CRLBs. (TR = 5,000 ms; number of transients, 128; VOI, 8 mL). Phantom spectra were line broadened to match line widths encountered in vivo.

Close modal

To date, IHC and molecular pathologic analysis of surgically obtained tumor tissue is required to diagnose an IDH-mutated glioma. Recently, the detection of 2-HG with high-resolution magic angle spinning proton magnetic resonance spectroscopy (1H-MRS) was demonstrated, followed by in vivo detection of 1H-MRS at 3T (5, 6). However, because of overlapping multiplets from glutamate (Glu), glutamine (Gln), glutathione (GSH), and γ-aminobutyric acid (GABA), reliable measurement of 2-HG at field strengths of 3T and below is difficult and cannot attribute the 2-HG signal to either the activity of IDH1 or IDH2 mutations. At ultrahigh magnetic fields (UHF, ≥7T), in vivo1H-MRS detection of metabolites benefits from substantial gains in signal-to-noise ratio (SNR) and spectral resolution, enabling the detection of subtle changes in metabolite levels from small volumes-of-interest (VOI) and higher specificity than at 3T (7). Thus, in vivo1H-MRS of 2-HG and associated metabolites at UHF offers the possibility to make important contributions not only in the early differential diagnosis of brain tumors, but also more importantly in assisting the study of disease progression and treatment response that cannot be obtained with other methods.

In this study, we show a proton 1H-MRS acquisition scheme enabling discernible 2-HG in the spectra of IDH-mutant patients within 20 seconds and quantify metabolic changes associated with the IDH mutation. Because of the increased sensitivity and specificity of this scheme at UHF, we demonstrate elevated 2-HG accumulation in IDH2 R172K (mitochondrial) compared with the IDH1 R132H (cytosolic)-mutant tumors in human brains noninvasively.

Subject inclusion

Fourteen glioma patients (8 men, 45 ± 13-year-old, mean ± SD) and 8 healthy volunteers (6 men, 42 ± 11-year-old) participated in the study after giving written informed consent (Table 1). One patient (P005) was excluded because of poor placement of a dielectric pad resulting in high measurement noise and insufficient transmit field. The Oxfordshire B National Research Ethics Committee approved the study.

Table 1.

Demographic and clinical characteristics of patients and healthy volunteer participants who were scanned by in vivo1H-MRS at 7T

Subject IDAge/genderDiagnosisIHCDNA sequencingNumber of healthy tissue VOIsNumber of tumor tissue VOIs
P001 31/M Anaplastic astrocytoma (WHO grade 3) +ve NA 
P002 36/F Anaplastic oligoastrocytoma (WHO grade 3) +ve NA 
P003 34/M Anaplastic astrocytoma (WHO grade 3) +ve NA 
P004 68/M Astrocytoma (WHO grade 2) −ve W/T 
P005 47/M Anaplastic astrocytoma (WHO grade 3) +ve IDH1(R132H) 
P006 27/F Oligodendroglioma (WHO grade 2) −ve IDH2(R172K) 
P007 52/M Glioblastoma (WHO grade 4) −ve W/T — 
P008 54/M Glioblastoma (WHO grade 4) −ve W/T — 
P009 51/M Anaplastic astrocytoma (WHO grade 3) +ve NA 
P010 60/F Glioblastoma (WHO grade 4) −ve W/T 
P011 53/F Astrocytoma (WHO grade 2) +ve N/A 
P012 29/F Oligodendroglioma (WHO grade 2) −ve IDH2(R172K) 
P013 45/M Astrocytoma (WHO grade 2) +ve NA 
P014 33/F Oligodendroglioma (WHO grade 2) −ve IDH2(R172K) 
C001 47/M Healthy volunteer NA NA NA 
C002 38/F Healthy volunteer NA NA NA 
C003 54/F Healthy volunteer NA NA NA 
C004 51/M Healthy volunteer NA NA NA 
C005 24/M Healthy volunteer NA NA NA 
C006 37/M Healthy volunteer NA NA NA 
C007 55/M Healthy volunteer NA NA NA 
C008 37/M Healthy volunteer NA NA NA 
Subject IDAge/genderDiagnosisIHCDNA sequencingNumber of healthy tissue VOIsNumber of tumor tissue VOIs
P001 31/M Anaplastic astrocytoma (WHO grade 3) +ve NA 
P002 36/F Anaplastic oligoastrocytoma (WHO grade 3) +ve NA 
P003 34/M Anaplastic astrocytoma (WHO grade 3) +ve NA 
P004 68/M Astrocytoma (WHO grade 2) −ve W/T 
P005 47/M Anaplastic astrocytoma (WHO grade 3) +ve IDH1(R132H) 
P006 27/F Oligodendroglioma (WHO grade 2) −ve IDH2(R172K) 
P007 52/M Glioblastoma (WHO grade 4) −ve W/T — 
P008 54/M Glioblastoma (WHO grade 4) −ve W/T — 
P009 51/M Anaplastic astrocytoma (WHO grade 3) +ve NA 
P010 60/F Glioblastoma (WHO grade 4) −ve W/T 
P011 53/F Astrocytoma (WHO grade 2) +ve N/A 
P012 29/F Oligodendroglioma (WHO grade 2) −ve IDH2(R172K) 
P013 45/M Astrocytoma (WHO grade 2) +ve NA 
P014 33/F Oligodendroglioma (WHO grade 2) −ve IDH2(R172K) 
C001 47/M Healthy volunteer NA NA NA 
C002 38/F Healthy volunteer NA NA NA 
C003 54/F Healthy volunteer NA NA NA 
C004 51/M Healthy volunteer NA NA NA 
C005 24/M Healthy volunteer NA NA NA 
C006 37/M Healthy volunteer NA NA NA 
C007 55/M Healthy volunteer NA NA NA 
C008 37/M Healthy volunteer NA NA NA 

NOTE: In cases where the immunohistochemistry did not detect IDH1 R132H mutations, regions containing IDH1 codon 132 and IDH2 codon 172 were PCR amplified and subsequently sequenced to establish whether a mutation was present at either locus. The number of VOIs for tumor and healthy tissue is listed for each participant.

Abbreviations: +ve, immunopositive; −ve, immunonegative; NA, not applicable; W/T, wild type; WHO, World Health Organization.

Immunohistochemistry and DNA sequencing

Immunohistochemistry and DNA sequencing are detailed in Supplementary Methods.

MR imaging and spectroscopy

Each volunteer participated in a 1 hour MR scan. MR experiments were performed using a 7T whole body MR system (Siemens) with a Nova Medical 32-channel receive array head-coil. VOIs were defined on each participant's anatomical scan (1-mm isotropic resolution MPRAGE sequence: repetition time TR = 2.3 s, inversion time TI = 1.05 s, echo time TE = 2.8 ms, total acquisition time = 3 min). First- and second-order shims were first adjusted by gradient-echo shimming (8). The second step involved only fine adjustment of first order shims using FASTMAP (9). Barium titanate pads were used to increase the extent of the effective transmit field (B1+; ref. 10). Spectra were measured with a semi-localization by adiabatic selective refocusing (semi-LASER; ref. 11) pulse sequence (TE = 110 ms, TR = 5–6 s, number of transients NT = 128, spectral bandwidth = 6 kHz, data points = 2048) with VAPOR (variable power and optimized relaxation delays) water suppression and outer volume suppression (OVS). The distance between the voxel edge and each OVS saturation band was set to 8 mm, thus ensuring no signal loss due to OVS bands. For patients, volumes of 8 mL (20 × 20 × 20 mm3) were acquired from the tumor and, where time allowed, contralateral healthy tissue regions. Tumor voxel positioning aimed to exclude very heterogeneous tissue and minimize inclusion of healthy-appearing tissue [except in one previously-operated patient (P002), a 2 mL (20 × 10 × 10 mm3) volume was measured]. For healthy volunteers, an 8 mL voxel was placed in regions similar to the patient tumor locations.

Simulations

The model spectra of 2-HG and other metabolites were generated on the basis of the previously reported chemical shifts and coupling constants (12, 13) using the GAMMA/PyGAMMA simulation library of VESPA (14) for carrying out the density matrix formalism. 2-HG contains five C-H protons that are detectable by MRS and that have the following chemical shifts (δHn) and scalar coupling constants in H2O (JHn-Cm; ref. 13): δH2 = 4.022, δH3 = 1.825, δH3′ = 1.977, δH4 = 2.221, δH4′ = 2.272, JH2-H3 = 7.6, JH2-H3′ = 4.1, JH2-H4 = 0, JH2-H4′ = 0, JH3-H3′ = −14.0, JH3-H4 = 5.3, JH3-H4′ = 10.4, JH3′-H4 = 10.6, JH3′-H4′ = 6.0, JH4-H4′ = −15.0. Simulations were performed with the same RF pulses and sequence timings as that on the 7T system in use. The echo time and timing between RF pulses influences the lineshape of 2-HG, and therefore simulations were performed to obtain the best interpulse delays for an optimal 2-HG detection at 7T. Thus, 2-HG was simulated for varied interpulse delays using 20 equally spaced steps between 8–46 ms, 14–109 ms, and 8–103 ms for TE1, TE2, and TE3, respectively.

Spectral processing

Spectral processing steps are detailed in Supplementary Methods.

Untargeted metabolomics analysis

After transforming the pre-processed signals to the frequency domain, the baseline offset was subtracted from the spectrum. The normalization of the spectral data vector to the L2-norm was performed on the basis of the data points in the region 1.6 to 4.2 ppm. Finally, a spectral range restricted to 1.6 to 3.1 ppm was used as an input to SpectraClassifier 3.1, an automated MRS-based classifier-development system (15). Feature selection was performed with Correlation-based Feature Subset Forward Selection and the resulting features were used as an input to a Fisher Linear Discriminant Analysis (LDA). The number of spectral features selected using correlation analysis was set to 2 (<n/3, where n is the number of cases in the smallest group).

Spectral quantification

LCModel (16) fitting using a basis set simulated at TE of 110 ms was performed over the spectral range from 0.5 to 4.2 ppm for pre-processed signals (Supplementary Fig. S1). The metabolite concentrations were estimated with respect to a water reference. Only the transverse (T2) relaxation effects of the water signal were corrected for tumor and healthy tissue using published water T2 values for healthy tissue voxels (T2 = 50 ms), and assuming that the T2 of water in tumor tissue is 2× longer than in healthy tissue (17). The relaxation effects of metabolites and fraction of CSF in the voxel were neglected. Cramér-Rao lower bounds (CRLB; estimated error of the metabolite quantification) of LCModel analysis were used to evaluate the sensitivity of metabolite quantification at 7T. Metabolites quantified with CRLB above 30% were classified as not reliably detected. Only metabolites quantified with CRLB ≤30% in at least half of the spectra from a tissue were included in the final neurochemical profile. If the correlation between two metabolites was consistently high (correlation coefficient ≤0.5), their sum was reported, such as Glc + Tau, NAA + NAAG (tNAA, total NAA), Cr + PCr (tCr, total creatine), and GPC + PCho (tCho, total choline).

Because of its minimal chemical shift displacement error and insensitivity to transmit field (B1+) inhomogeneities at UHF, we investigated the semilocalization by adiabatic selective refocusing sequence (semi-LASER; ref. 11) for in vivo 2-HG detection (Fig. 1B). We conducted density matrix simulations to establish the optimal interpulse delays of the semi-LASER sequence for 2-HG detection (Fig. 1C). The simulations indicate that the 2-HG multiplets at 2.25 ppm (H4, H4′) lead to a maximum absorptive negative (inverted) multiplet at a total echo time of 100 to 120 ms (Fig. 1C). A TE of 110 ms was chosen, because simulations showed a near fully absorptive negative 2-HG (Supplementary Fig. S2) and lactate (Lac) spectral pattern at 2.25 ppm and 1.35 ppm with timings TE1 = 11 ms, TE2 = 65 ms and TE3 = 34 ms (total TE = 110 ms). The accuracy of simulation and specificity of the proposed acquisition scheme was tested on three “phantoms,” which contained 2-HG with glycine (Gly), Lac with acetate (Ace) and 2-HG (4 mmol/L) with Glu (4 mmol/L), Gln (4 mmol/L), NAA (10 mmol/L), and Gly (10 mmol/L). The spectral shape of 2-HG and Lac at TE = 110 ms obtained from these phantom experiments closely resembled the simulated 2-HG and Lac shape determined by LCModel (16) fitting (Supplementary Fig. S3). In comparison with the shortest achievable TE of 36 ms, a TE of 110 ms resulted in 2.9 (simulation) and 1.5 (phantom) fold higher 2-HG signal at 2.25 ppm, respectively (Supplementary Fig. S4). In addition, Fig. 1D illustrates phantom spectra of 2-HG, Gln, Glu, and NAA obtained with semi-LASER at TE = 36 (TE1 = 11 ms, TE2 = 15 ms and TE3 = 10 ms) and TE = 110 ms, together with LCModel fits. The LCModel analysis of a phantom consisting of 2-HG, Gln, Glu, NAA and Gly at TE = 110 ms resulted in CRLBs of 4%, 4%, 9%, 1%, and 1%, respectively, whereas at TE 36 ms the CLRBs were 5%, 3%, 3%, 1%, and 1%, respectively. Quantitative comparison of short and long TEs using the ratio of 2-HG with the sum of Glu+Gln resulted in values of 0.56 and 0.44, respectively, which was similar to the prepared concentration ratio of 0.5. Although spectral overlap with the adjacent resonances of Glu, Gln, and 2-HG was more prominent at TE = 36 ms, the CLRB values of 2-HG at 36 ms was similar that of at 110 ms. This was due to the H2 proton of 2-HG at 4.01 ppm, which was hard to detect under in vivo conditions due to overlapping peaks of myo-inositol (myo-Ins) at 4.05 ppm, Lac at 4.09 ppm and tCr 3.91 ppm. To determine the effect of the H4 proton of 2-HG on the CRLBs, an additional LCModel analysis between 3.9 and 0.5 ppm resulted in an increased CRLB of 2-HG at TE = 36 ms (CRLB, 8%) compared with that of TE = 110 ms (CRLB, 4%; Supplementary Fig. S5).

Ten of 14 patients studied with in vivo MRS were shown to have mutations of IDH in tumor tissue subsequently obtained at surgery (Fig. 2A and Table 1). Tissue samples underwent IHC analysis for the common IDH1 R132H mutation. Cases that were IDH1 R132H immunonegative were subjected to DNA sequencing. Three immunonegative cases (P006, P012, and P014) harbored a less common IDH2 R172K mutation detectable in the sequencing electropherogram.

Figure 2.

Histology and in vivo1H-MRS at 7T. A, hematoxylin and eosin stained (left) and immunohistochemistry with anti-IDH1 R132H antibody (middle; inset, P53). PCR and direct sequencing of codon 132 of IDH1 (top trace) and R172 of IDH2 (bottom trace) was performed for immunonegative cases (right). All scale bars, 100 μm. B, representative of contralateral healthy and of tumor tissue voxel placement and respective in vivo1H-MRS spectra for immunopositive (top), immunonegative with rare mutation (middle), and a wild-type tumor patient (bottom). C, mean (solid line) and ± SD (shade) of L2-normalized 1H-MRS spectra from all subjects. Vertical dashed line indicates the identified spectral feature (2.25 ppm) by untargeted metabolomics analysis. D, LDA latent space for the discrimination of the spectra from tumor tissue voxel of IDH-mutant glioma patients and healthy tissue voxel.

Figure 2.

Histology and in vivo1H-MRS at 7T. A, hematoxylin and eosin stained (left) and immunohistochemistry with anti-IDH1 R132H antibody (middle; inset, P53). PCR and direct sequencing of codon 132 of IDH1 (top trace) and R172 of IDH2 (bottom trace) was performed for immunonegative cases (right). All scale bars, 100 μm. B, representative of contralateral healthy and of tumor tissue voxel placement and respective in vivo1H-MRS spectra for immunopositive (top), immunonegative with rare mutation (middle), and a wild-type tumor patient (bottom). C, mean (solid line) and ± SD (shade) of L2-normalized 1H-MRS spectra from all subjects. Vertical dashed line indicates the identified spectral feature (2.25 ppm) by untargeted metabolomics analysis. D, LDA latent space for the discrimination of the spectra from tumor tissue voxel of IDH-mutant glioma patients and healthy tissue voxel.

Close modal

Figure 2B shows representative spectra from three different patients (P006, P010, and P011) obtained from contralateral healthy tissue and tumor voxels at 7T. In all cases, the residual water signal was smaller than the major metabolite peaks tCho and tNAA for tumor and healthy tissue voxels, respectively). In addition, the double localization accomplished by semi-LASER and OVS eliminated signals from outside the VOI, such as lipid signals from the subcutaneous tissue, resulting in artefact-free spectra with a flat baseline in the spectral range of 1.6 to 4.2 ppm for all subjects (Fig. 2C).

Given the phantom and in vivo measurements, we then characterized the spectral pattern changes induced by the IDH mutations, particularly any visually discernible 2-HG signal. Thus, untargeted metabolomics analysis was performed for the spectral range restricted to 1.6 to 3.1 ppm. The untargeted feature extraction of in vivo spectra from healthy and tumor voxels resulted in a spectral pattern deviation at 2.25 ppm, where the 2-HG peak is located (Fig. 2C). The feature identified was used for LDA to separate data into IDH-mutant or healthy subjects. The LDA classifier projection space plot identified distinct clustering patterns, not only distinguishing 7T MRS spectra between IDH-mutant tumors and healthy tissue but, furthermore, separating IDH1 R132H from IDH2 R172K mutations (Fig. 2D).

To characterize this difference in more detail, we quantified 2-HG and related metabolite concentrations using LCModel (16), which uses an a priori established basis set for a selected group of metabolites (Supplementary Fig. S1). The high spectral quality enabled the quantification of a neurochemical profile consisting of eight metabolites in both tumor and healthy tissue voxels (Fig. 3A). A 2-HG signal was only detected in patients exhibiting IDH1 R132H and IDH2 R172K mutations. We demonstrated that mitochondrial IDH2 R172K mutations lead to higher levels of 2-HG than cytosolic IDH1 R132H mutations (9.06 ± 0.87 and 2.53 ± 0.75 μmol/g, respectively; Fig. 3B), in agreement with a previous cell culture findings (2). In addition, high Lac, myo-Ins, tCho, and Glc+Tau concentrations were observed in tumor voxels, whereas Glu and tNAA were decreased (Fig. 3A). 2-HG concentrations were also evaluated relative to the tCr and tCho, MRS markers for cellular bioenergetics and proliferation (18), respectively (Fig. 3C). The 2-HG ratios (2-HG:tCr and 2-HG:tCho) appeared to be higher in IDH2 R172K compared with the IDH1 132H due to increased 2-HG levels in IDH2 172K. However, for 1 IDH2 R172K patient, the increase in 2-HG:tCr resulted not only from increased 2-HG but also decreased tCr.

Figure 3.

LCModel analysis. A, neurochemical profiles determined by LCModel fitting. Only metabolites quantified with CRLBs ≤30% in at least half of the spectra from a brain region were included in the profiles. B, 2-HG concentrations in μmol/g. Only healthy tissue voxels of two patients resulted in 2-HG detection with CLRBs of 25 and 26%, respectively. C and D, 2-HG concentrations (C) relative to the tCr and tCho CRLBs (D) of 2-HG detection by LCModel fitting together with means (boxes) and SDs (error bars) as a function of the number of transients. Error bars, intersubject SD. Glu, glutamate; GSH, glutathione; myo-Ins, myo-inositol; scyllo-Ins, scyllo-inositol; tNAA, total N-acetylaspartate; tCho, total choline; tCr, total creatine; Glc, glucose; Tau, taurine; Lac, lactate.

Figure 3.

LCModel analysis. A, neurochemical profiles determined by LCModel fitting. Only metabolites quantified with CRLBs ≤30% in at least half of the spectra from a brain region were included in the profiles. B, 2-HG concentrations in μmol/g. Only healthy tissue voxels of two patients resulted in 2-HG detection with CLRBs of 25 and 26%, respectively. C and D, 2-HG concentrations (C) relative to the tCr and tCho CRLBs (D) of 2-HG detection by LCModel fitting together with means (boxes) and SDs (error bars) as a function of the number of transients. Error bars, intersubject SD. Glu, glutamate; GSH, glutathione; myo-Ins, myo-inositol; scyllo-Ins, scyllo-inositol; tNAA, total N-acetylaspartate; tCho, total choline; tCr, total creatine; Glc, glucose; Tau, taurine; Lac, lactate.

Close modal

Analysis of CRLBs as a function of the number of signal averages clearly showed that the estimated quantification error per number of transients was always less than for previously published data at 3T (Fig. 3D). Importantly, because of the improved SNR and sensitivity at 7T, the proposed method enabled us to quantify 2-HG in the tumor VOI with a mean CRLB of 16 ± 8.4% following only four transient averages (∼20 s experimental duration).

We acknowledge that our study is limited by a small sample size, particularly concerning the IDH2 mutations. However, distinction between canonical IDH1 and IDH2 mutation appeared robust even when correcting for tCr and tCho values, and the low frequency of IDH2 mutations in our single-centre study was expected as only 3% of diffuse gliomas of WHO grade 2 or 3 carry IDH2 mutations (19). A multicenter study is required for robust comparisons between canonical and noncanonical IDH1 mutations and IDH2 mutations. One potential limitation of the methodology of this study is the semiquantification of metabolite levels by using an internal reference method within the same voxel as the effects of tumor heterogeneity, regional differences in absolute and relative metabolite concentrations of water, and metabolite relaxation times are not practical to assess in patient studies. In particular, differences in water and metabolite T2s with tumor type and grade have the potential to complicate quantification of metabolite concentrations (20). Finally, the use of a single TE of 110 ms, at which overlapping peaks of Glu and Gln are decreased relative to 2-HG at 2.25 ppm, could lead to the underestimation of Glu and Gln concentrations.

A number of studies have demonstrated in vivo detection of 2-HG in IDH-mutant tumors at 3T (5, 6), commonly used in the clinical setting. However, the assignment of 2-HG resonances at 3T is an important technical challenge not only because of the complex spin-coupling features of overlapping resonances but also due to the lack of SNR and spectral resolution. As we demonstrate, the increased sensitivity and spectral resolution at 7T substantially improves the precision of in vivo detection of 2-HG and other metabolite changes. Finally, noninvasive discrimination between IDH1 and IDH2 mutations with the acquisition scheme should be extended to larger sample sizes to explore new diagnostic and therapeutic approaches and associated metabolite biomarkers.

No potential conflicts of interest were disclosed.

The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health.

Conception and design: U.E. Emir, P. Plaha, C.J. Schofield, T. Cadoux-Hudson, O. Ansorge

Development of methodology: U.E. Emir, K. Al-Qahtani, C.J. Schofield, S. Clare, P. Jezzard, T. Cadoux-Hudson

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): U.E. Emir, S.J. Larkin, N. de Pennington, N. Voets, P. Plaha, R. Stacey, J. Mccullagh, T. Cadoux-Hudson, O. Ansorge

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): U.E. Emir, S.J. Larkin, C.J. Schofield, O. Ansorge

Writing, review, and/or revision of the manuscript: U.E. Emir, S.J. Larkin, N. de Pennington, N. Voets, P. Plaha, R. Stacey, K. Al-Qahtani, J. Mccullagh, C.J. Schofield, P. Jezzard, T. Cadoux-Hudson, O. Ansorge

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): U.E. Emir, N. de Pennington, P. Plaha, O. Ansorge

Study supervision: U.E. Emir, P. Plaha, J. Mccullagh, P. Jezzard, T. Cadoux-Hudson, O. Ansorge

The authors acknowledge the Oxford Brain Bank, supported by the Medical Research Council (MRC), Brains for Dementia Research (BDR), the Welcome Trust (U.E. Emir), the Dunhill Medical Trust (P. Jezzard), and the NIHR Oxford Biomedical Research Centre.

The research was funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Center based at Oxford University Hospitals NHS Trust and the University of Oxford (S.J. Larkin and O. Ansorge).

1.
Parsons
DW
,
Jones
S
,
Zhang
X
,
Lin
JC
,
Leary
RJ
,
Angenendt
P
, et al
An integrated genomic analysis of human glioblastoma multiforme
.
Science
2008
;
321
:
1807
12
.
2.
Ward
PS
,
Lu
C
,
Cross
JR
,
Abdel-Wahab
O
,
Levine
RL
,
Schwartz
GK
, et al
The potential for isocitrate dehydrogenase mutations to produce 2-hydroxyglutarate depends on allele specificity and subcellular compartmentalization
.
J Biol Chem
2013
;
288
:
3804
15
.
3.
Dang
L
,
White
DW
,
Gross
S
,
Bennett
BD
,
Bittinger
MA
,
Driggers
EM
, et al
Cancer-associated IDH1 mutations produce 2-hydroxyglutarate
.
Nature
2009
;
462
:
739
44
.
4.
Yen
KE
,
Bittinger
MA
,
Su
SM
,
Fantin
VR
. 
Cancer-associated IDH mutations: biomarker and therapeutic opportunities
.
Oncogene
2010
;
29
:
6409
17
.
5.
Choi
C
,
Ganji
SK
,
DeBerardinis
RJ
,
Hatanpaa
KJ
,
Rakheja
D
,
Kovacs
Z
, et al
2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas
.
Nat Med
2012
;
18
:
624
9
.
6.
Andronesi
OC
,
Kim
GS
,
Gerstner
E
,
Batchelor
T
,
Tzika
AA
,
Fantin
VR
, et al
Detection of 2-hydroxyglutarate in IDH-mutated glioma patients by in vivo spectral-editing and 2D correlation magnetic resonance spectroscopy
.
Sci Transl Med
2012
;
4
:
116ra4
.
7.
Mekle
R
,
Mlynarik
V
,
Gambarota
G
,
Hergt
M
,
Krueger
G
,
Gruetter
R
. 
MR spectroscopy of the human brain with enhanced signal intensity at ultrashort echo times on a clinical platform at 3T and 7T
.
Magn Reson Med
2009
;
61
:
1279
85
.
8.
Shah
S
,
Kellman
P
,
Greiser
A
,
Weale
P
,
Zuehlsdorff
S
,
Jerecic
R
. 
Rapid Fieldmap Estimation for Cardiac Shimming
. In:
Proceedings of the 17th Scientific Meeting, International Society for Magnetic Resonance in Medicine
.
Honolulu
:
ISMRM
; 
2009
.
Abstract nr 565
.
9.
Gruetter
R
,
Tkac
I
. 
Field mapping without reference scan using asymmetric echo-planar techniques
.
Magn Reson Med
2000
;
43
:
319
23
.
10.
Teeuwisse
WM
,
Brink
WM
,
Haines
KN
,
Webb
AG
. 
Simulations of high permittivity materials for 7 T neuroimaging and evaluation of a new barium titanate-based dielectric
.
Magn Reson Med
2012
;
67
:
912
8
.
11.
van de Bank
BL
,
Emir
UE
,
Boer
VO
,
van Asten
JJ
,
Maas
MC
,
Wijnen
JP
, et al
Multi-center reproducibility of neurochemical profiles in the human brain at 7 T
.
NMR Biomed
2015
;
28
:
306
16
.
12.
Govindaraju
V
,
Young
K
,
Maudsley
AA
. 
Proton NMR chemical shifts and coupling constants for brain metabolites
.
NMR Biomed
2000
;
13
:
129
53
.
13.
Bal
D
,
Gryff-Keller
A
. 
1H and 13C NMR study of 2-hydroxyglutaric acid and its lactone
.
Magn Reson Chem
2002
;
40
:
533
36
.
14.
Soher
BJ
,
Semanchuk
P
,
Todd
D
,
Steinberg
J
,
Young
K
. 
Vespa: integrated applications for RF pulse design, spectral simulation and MRS data analysis
. In:
Proceedings of the 19th Scientific Meeting, International Society for Magnetic Resonance in Medicine
.
Quebec, Canada
:
ISMRM
; 
2011
.
Abstract nr 1410
.
15.
Ortega-Martorell
S
,
Olier
I
,
Julia-Sape
M
,
Arus
C
. 
SpectraClassifier 1.0: a user friendly, automated MRS-based classifier-development system
.
BMC Bioinformatics
2010
;
11
:
106
.
16.
Provencher
SW
. 
Automatic quantitation of localized in vivo 1H spectra with LCModel
.
NMR Biomed
2001
;
14
:
260
4
.
17.
Isobe
T
,
Matsumura
A
,
Anno
I
,
Yoshizawa
T
,
Nagatomo
Y
,
Itai
Y
, et al
Quantification of cerebral metabolites in glioma patients with proton MR spectroscopy using T2 relaxation time correction
.
Magn Reson Imaging
2002
;
20
:
343
9
.
18.
Oz
G
,
Alger
JR
,
Barker
PB
,
Bartha
R
,
Bizzi
A
,
Boesch
C
, et al
Clinical proton MR spectroscopy in central nervous system disorders
.
Radiology
2014
;
270
:
658
79
.
19.
Hartmann
C
,
Meyer
J
,
Balss
J
,
Capper
D
,
Mueller
W
,
Christians
A
, et al
Type and frequency of IDH1 and IDH2 mutations are related to astrocytic and oligodendroglial differentiation and age: a study of 1,010 diffuse gliomas
.
Acta Neuropathol
2009
;
118
:
469
74
.
20.
Li
Y
,
Srinivasan
R
,
Ratiney
H
,
Lu
Y
,
Chang
SM
,
Nelson
SJ
. 
Comparison of T(1) and T(2) metabolite relaxation times in glioma and normal brain at 3T
.
J Magn Reson Imaging
2008
;
28
:
342
50
.