2.2. GBM and Oligodendroglioma Show Different Metabolic Patterns

Comparing metabolic profiles from tissue between GBM and oligodendrogliomas revealed 12 significantly differentiating features (w*_average ± 2 standard deviations (SD)). In serum, 13 metabolic features were significantly differentially expressed. Investigation of the significance of the detected metabolic patterns by means of orthogonal partial least squares-discriminant analysis (OPLS-DA) showed that it was possible to distinguish between glioblastomas and oligodendrogliomas in both tumor and serum (A = 1 + 0 + 0, R2X = 0.39, R2Y = 0.379, Q2 = 0.341, p = 2.46 × 10⁻⁸ and A = 1 + 0 + 0, R2X = 0.25 R2Y = 0.251 Q2 = 0.223, p = 1.3 × 10⁻⁵) (Figure 1). Detected features with a suggested identity from spectral library comparison (fragmentation pattern and retention index) responsible for discriminating between the diagnoses in tumor and serum are listed in Table 1. In summary, higher levels of mannitol and phenylalanine where found in GBMs compared to oligodendrogliomas in tissue while 2-hydroxyglutaric acid, 4-Aminobutyric acid (GABA), creatinine, glycerol-2-phosphate, glycerol-3-phosphate, ribitol and myo-inositol showed higher levels in oligodendrogliomas as compared to GBM. In serum, cysteine was found at higher levels in GBMs, while lysine and 2-oxoisocaproic acid showed higher levels in oligodendrogliomas.

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Figure 1

Receiver operating characteristic (ROC) curves and scatter plots of orthogonal partial least squares-discriminant analysis (OPLS-DA) scores following a seven-fold cross-validation procedure showing differences associated with diagnosis and tumor grade. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of glioblastoma and oligodendroglioma in tissue (blue line) and serum (red line) with area under the ROC curve (AUROCC) values of 0.881 (0.791–0.970) and 0.826 (0.722–0.929), respectively (left). The scatter plots show the class differences between glioblastoma and oligodendroglioma based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right). (Lower panel) ROC curves based on the cross-validated score values from the final OPLS-DA model discriminating between World Health Organization (WHO) grade II and III in oligodendroglioma in tissue (blue line) and serum (red line) with AUROCC values of 0.833 (0.557–1.000) and 0.946 (0.858–1.000), respectively (left). The scatter plots show the class differences between grade II and grade III based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).

2.3. Metabolic Differences between Oligodendroglioma WHO Grade II and III

For the comparison between oligodendroglioma grade II and grade III, 10 resolved features fulfilled the significance criteria in tissue and provided a separation between the two sample classes in an OPLS-DA model (A = 1 + 0 + 0, R2X = 0.579, R2Y = 0.505, Q2 = 0.434, p = 0.01) (Figure 1). Serine, threonic acid and mannitol were all elevated in in oligodendroglioma grade III, while oxalic acid was elevated in oligodendroglioma grade II. In serum, 12 resolved features as a pattern in an OPLS-DA model provided a significant difference between oligodendroglioma grade II and grade III (A = 1 + 0 + 0, R2X = 0.405, R2Y = 0.589, Q2 = 0.589, p = 0.0008) (Figure 1). Mannitol, maltose, threonic acid-1,4-lactone and alanine were all higher in relative concentration in grade III oligodendrogliomas, while lysine and 1-monohexadecanoylglycerol were found in lower relative concentrations in grade III as compared to grade II oligodendrogliomas (Table 1).

2.4. Metabolic Profiles Associated with Survival

In GBM, the metabolic profiles of tumor samples from patients surviving long after diagnosis (≥3 years) were compared to patients that died shortly after diagnosis (≤4 months). Based on seven resolved features passing the significance criteria, OPLS-DA provided a significant separation associated with time of survival (A = 1 + 0 + 0, R2X = 0.669, R2Y = 0.474, Q2 = 0.427, p = 0.006) (Figure 2). Interpretation of the model revealed that glycerol-3-phoshate, myo-inositol, ribitol and fructose increased in level with long survival. Furthermore, we detected a significant association with survival time in serum samples from the same patients in an OPLS-DA model based on five resolved features (A = 1 + 0 + 0, R2X = 0.536, R2Y = 0.572, Q2 = 0.478, p = 0.004; Figure 2). Unfortunately, it was not possible to obtain a suggested identity for any of the affected features found in serum. The same comparison was carried out in patients with oligodendroglioma. In tumor tissue, eight resolved features passed the significance criteria and provided a significant metabolic pattern (OPLS-DA model) in relation to time of survival (A = 1 + 0 + 0, R2X = 0.56, R2Y = 0.796, Q2 = 0.767, p = 0.01; Figure 2). High levels of ribitol, myo-inositol and spermidine were associated with long survival, while high levels of glycine, aminomalonic acid and highly likely an unidentified sterol were associated with short survival time. A complete separation with respect to survival time could also be seen in serum (Figure 2). The final OPLS-DA model was based on 13 resolved features, which together formed a significant metabolic pattern (A = 1 + 0 + 0, R2X = 0.521, R2Y = 0.909, Q2 = 0.855, p = 0.001). In this model, myo-inositol and hexadecenoic acid were associated with long survival time. All features with a putative identity significant in oligodendroglioma survival can be viewed in Table 1. AUCROCC analyses of the extracted metabolic patterns visualized as the OPLS-DA score values following a seven-fold cross-validation procedure for GBM in tumor and serum gave ROC values of 0.846 (0.647–1.000) and 0.958 (0.870–1.000), respectively (Figure 2). In oligodendroglioma, the corresponding ROC values reached the value 1 (1.000–1.000) in both tumor and serum.

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Figure 2

Receiver operating characteristic (ROC) curves and scatter plots of orthogonal partial least squares-discriminant analysis (OPLS-DA) scores following a seven-fold cross-validation procedure showing differences between long and short survival time. (Upper panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of short survival time compared to long survival time in patients with glioblastomas in tissue (blue line) and serum (red line) with ROC values of 0.846 (0.647–1.000) and 0.958 (0.870–1.000), respectively (left). The scatter plots show class differences between short survival time and long survival time based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right). (Lower panel) ROC curves based on the cross-validated score values from the final OPLS-DA model for the discrimination of short survival time compared to long survival time in patients with oligodendrogliomas in tissue (blue line) and serum (red line). AUROCC values for survival in oligodendroglioma were calculated to 1 (1.000–1.000) for both tissue and serum (left). The scatter plots show class differences between short survival time and long survival time based on cross-validated predictive OPLS-DA scores (tcv[1]p) for tissue (center) and serum (right).

3.2. The Metabolome as Prognostic Factor

Grading of the tumors should according to the aims of the WHO classification be related to the prognosis [2]. However, for many histopathological entities there are large variations in survival. For example, in GBM the survival can vary between 0.4 to 142 months in larger series [4] and for low-grade gliomas from 0.2 to 16 years [5]. In a clinical setting, this is unsatisfactory. Quon et al. have by repeated MRS in glioma patients undergoing surgery and radiotherapy demonstrated that changes in choline could provide prognostic information [24]. In a microdialysis study, our group has previously investigated the metabolomic alterations in GBM during the early course of radiotherapy [25]. In that study, significant changes in metabolic patterns were disclosed. Majos et al. analyzed MRS spectra in patients with high-grade astrocytomas (grade III and GBM) and found prognostic information [26]. Although they did not identify any specific metabolites, their study points to the possibility of utilizing a metabolic signature or pattern for prognostication. Our analysis showed that were associations between the metabolome and time of survival in tumor and serum of both GBM and oligodendrogliomas and that there was a break-point for a high prognostic value based on changes in the metabolome for patients with GBM living shorter than four months, and longer than three years. A prognosis of less than four months survival might indicate that an aggressive treatment should be avoided, while a prognosis better than three years may support a more active approach. For oligodendrogliomas, the corresponding break-points were two and three years, respectively. If further verifications of these findings are successful in separate sample cohorts, this methodology might possess a potential means to extract latent variables holding prognostic information regarding glioma survival.

3.3. Metabolic Pathways and Specific Metabolites of Interest

In general, we have found it hard to corroborate our findings to the existing literature due to the lack of metabolomics data regarding gliomas. Although there are an increasing number of publications based on MRS and NMR reporting on single or a few metabolites, there are few studies on metabolomics utilizing mass spectrometric methods in brain tumors. By utilizing the IPA^® pathway analysis platform, we could identify a number of potential pathways, including amino and fatty acid metabolism, or metabolic entities that the detected metabolites of interest are involved in. Since the high grade of complexity in tumor metabolism possesses a major challenge when it comes to mechanistic interpretations, we found it difficult to draw any major mechanistic conclusions based on the pathway analysis in this limited material Nevertheless, some of the putatively identified metabolites found in our study can be interpreted in a biological context and are discussed below.

Mannitol was found at higher levels in GBM compared to oligodendroglioma and in oligodendroglioma grade III compared to oligodendroglioma grade II. In clinical practice mannitol is used temporarily during surgery to reduce brain edema. Almost all patients, except for a few with low-grade tumors, received 200–300 mL mannitol approximately 1–2 h before resection of the tumor. Mannitol is a large molecule that normally does not pass the blood–brain barrier, therefore, it is not surprising that we found higher levels of mannitol in the high-grade tumors where a more or less defect blood–brain barrier is to be expected. Peeling et al. have previously demonstrated that mannitol was found in glioma tissue only when the tumors analyzed showed a contrast enhancement on MR indicating a disruption of the blood-brain barrier [27].

Creatinine was also found as significantly altered between GBM and oligodendroglioma. It is a breakdown product of creatine phosphate, which can be used as a reserve of high-energy phosphates for the brain, anaerobically phosphorylating ADP to ATP [28]. Creatine phosphate is likely to be consumed to a higher extent by the more aggressive tumors leading to lower levels of the creatine phosphate breakdown product, creatinine. This could explain why we see lower levels of creatinine in tissue from GBM tumors as compared to oligodendroglioma.

We also found that levels of GABA in tumor were lower in GBM compared to oligodendroglioma. GABA is an important inhibitory neurotransmitter. The number of publications reporting on GABA in glioma is sparse and provides conflicting information. One study by Faria et al. showed that GABA was detectable in low-grade astrocytomas and normal brain, however they failed to detect GABA in high grade tumors [29], while another study by Biachi et al. found increased levels of GABA in GBM as compared to normal brain using microdialysis [30]. Another neurotransmitter, glutamate, is a substrate for GABA and an intermediate in the glutamine conversion to oxaloacetate in the amino acid, nucleotide and lipid synthesis [31]. Glutamate takes part in the energy supply, and has an important role as an excitotoxic substance promoting glioma invasiveness [32,33]. Several studies have demonstrated, by NMS in tissue or in the extracellular space by microdialysis, that the glutamate concentration is higher in more malignant gliomas, or in gliomas compared to normal brain [34,35,36]. In this study, we did not find any significant differences in glutamate levels, however, we had no possibility to compare low-grade astrocytomas with GBM.

Glycerol-3-phosphate is the backbone of triglycerides and glycerophospholipids and is also involved in the fatty acid oxidation cycle generating NADH. Accordingly, we found that the glycerol-3-phosphate level was lower in GBM compared to oligodendrogliomas as well as lower in short surviving patients, which would be expected in highly proliferating tumors.

Myo-inositol is an interesting metabolite. Previous studies have reported that low levels of myo-inositol are associated with higher aggressiveness of the glioma phenotype [37,38]. Kinoshita et al. and Faria et al. also found that inositol was lower in GBM as compared to astrocytoma grade II and III, while Wright and fellows found that the myo-inositol level in GBM where similar to the level in astrocytoma grade III but lower than in grade II [29,36,39]. We can confirm those findings in our study where we found that the level of myo-inositol was significantly lower in GBM as compared to oligodendrogliomas and that long survival patients from both tumor types have higher levels of myo-inositol than patients that die shortly after diagnosis. Myo-inositol is an activator of protein C kinase [37]. The activation of PKC contribute to tumor cell survival and proliferation and has shown to be involved in the progression of malignant gliomas [40].

When comparing metabolic profiles in patients that lived long after diagnosis compared to patients that died shortly after diagnosis we found glycine to be of interest. Glycine has been detected by MRS at higher concentrations in high grade gliomas and therefore been suggested as a possible diagnostic marker [41]. In our study, glycine were higher in patients that died shortly after diagnosed with oligodendroglioma supporting that higher concentrations of glycine is associated with a worse prognosis. Unfortunately, no correlation of glycine with survival in GBM could be found in our material.

4.2. Sample Preparation and GC-TOFMS Analysis

One milliliter extraction solution consisting of chloroform (20%), methanol (60%) and water (20%) with 11 IS (7 ng/μL) was added to 15 mg of tissue. Tissue samples were kept frozen on dry ice until the extraction solutions were added. Two tungsten beads were placed in each sample tube and the tissue samples were milled for 2 min, 30 Hz using a MM301 vibration Mill (Retsch GmbH & Co. KG, Haan, Germany) before centrifuged for 15 min, 4 °C, 14 000 rpm. 200 μL of the collected supernatant was transferred to GC vials and evaporated to dryness. The serum samples were thawed in room temperature for 30 min before addition of 900 μL extraction solution consisting of methanol (90%) and water (10%) with 11 IS (7 ng/μL) to 100 μL serum. The serum samples were extracted using the same approach as for the tissue samples, except for the tungsten beads. The samples were then methoxymated with 30 μL of methoxyamine solution in pyridine (15 μg/μL) and left standing at room temperature for 16 h before trimethylsilylation with 30 μL of MSTFA. After 1 h, 30 μL of heptane (containing 0.5 μg of methyl stearate) was added. One microliter of derivatized sample was injected splitless by an Agilent 7683 Series autosampler (Agilent, Atlanta, GA, USA) in randomized order into an Agilent 6980 GC equipped with a 10 m × 0.18 mm i.d. fused-silica capillary column chemically bonded with 0.18 μm DB5-MS stationary phase (J&W Scientific, Folsom, CA, USA). The injector temperature was set to 270 °C. Helium was used as carrier gas at a constant flow rate of 1 mL/min through the column. The purge time was set to 60 s at a purge flow rate of 20 mL/min and an equilibration time of 1 min for every analysis. Initially, the column temperature was kept to 70 °C for 2 min and then increased to 320 °C at 30 °C/min, where it was kept for 2 min. The column effluent was introduced into the ion source of a Pegasus III TOFMS (Leco Corp., St Joseph, MI, USA). The transfer line temperature was set to 250 °C and the ion source temperature to 200 °C. Ions were generated by a 70 eV electron beam at a current of 2.0 mA. Masses were acquired from m/z 50 to 800 at a rate of 30 spectra/s, and the acceleration voltage was turned on after a solvent delay of 165 s. The stable isotope-labeled internal standard compounds (IS) [¹³C₅]-proline, [²H₄]-succinic acid, [¹³C₅,¹⁵N]-glutamic acid, [1,2,3-¹³C₃]-myristic acid, [²H₇]-cholesterol and [¹³C₄]-disodium α-ketoglutarate were purchased from Cambridge Isotope Laboratories (Andover, MA). [¹³C₁₂]-sucrose, [¹³C₄]-palmitic acid and [²H₄]- butanediamine 2HCl were from Campro (Veenendaal, The Netherlands). [¹³C₆]-glucose was from Aldrich (Steinheim, Germany) and [²H₆]-salicylic acid was from Icon (Summit, NJ, USA). Stock solutions of the IS were prepared either in purified and deionized water (Milli-Q, Millipore, Billerica, MA, USA) or in methanol (J.T. Baker, Deventer, The Netherlands) at a concentration, 0.5 μg/μL. Methyl stearate was purchased from Sigma (St. Louis, USA). N-Methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS) and pyridine (silylation grade) were purchased from Pierce Chemical Co. Heptane was purchased from Fischer Scientific (Loughborough, UK).

4.3. Data Processing

To be able to relatively quantify and provide a putative identity for all detected individual features, the acquired GC-TOFMS data were processed by applying HMCR [42,43]. HMCR uses a multivariate approach for generating pure chromatographic signatures together with corresponding mass spectra for all detected feature peaks. In this way multiple sample comparisons based on the whole metabolic profile will be greatly facilitated. For this, NetCDF files of the raw acquired GC-TOFMS data were exported to MATLAB 7.11.0 (R2010b) (Mathworks, Natick, MA, USA) where baseline correction, alignment, time-window settings were carried out before applying HMCR time-window wise to resolve the individual feature peaks. All data processing including HMCR was done using in-house developed scripts. For the tissue data, the chromatograms were divided into 64 time windows from which 197 chromatographic peaks (features) were resolved resulting in a data matrix (X) were each row represents one patient and each column represents one feature, e.g. metabolite. Similarly, for the serum data, the chromatograms were divided into 67 time windows from which 230 chromatographic peaks were resolved. For each feature in each sample the area under the chromatographic peak, the relative concentration, was calculated. All peak areas were normalized using the peak areas from the 11 internal standards. The detected features’ mass spectral profile and retention indices were compared to spectra in an in-house spectral library of metabolite standards and the NIST library 2.0 (as of 31 January 2001) to provide a putative identity for each individual feature. This was followed by a manual inspection and curation of the data to further resolve co-eluting compounds and to correct for split peaks.

4.4. Pattern Recognition and Statistical Analysis

Pattern recognition utilizes multivariate projection methods to extract and verify co-varying patterns or signatures of variables that are significant for explaining systematic variation in experimental data. In metabolomics, pattern recognition works to compress the variable space, i.e., the detected and relatively quantified metabolic features, into a few latent variables, e.g., principal components, explaining the majority of the systematic variation in the data. In this way interpretation of changes in metabolic signatures as well as detection of robust and relevant sample patterns caused by those signature changes are largely facilitated. In this work, processed metabolomics data from tissue and serum samples were analyzed separately using different pattern recognition approaches. In a first step, principal component analysis (PCA) [44] was applied to get an unsupervised overview of the variation in the data and to detect deviating samples, so-called outliers. For further multivariate sample comparisons with the aim to look for differences between pre-defined sample classes, orthogonal partial least squares-discriminant analysis (OPLS-DA) [45] was used. OPLS is a supervised multivariate regression method allowing a separation of the variation into predictive variation (related to the response(s) of interest) and orthogonal variation (variation unrelated to the response(s) of interest). This has been shown to facilitate the interpretation of complex multivariate data and the interactions therein. The combined data processing and pattern recognition procedure can be overviewed in Figure 3. Initially, the diagnostic potential of the metabolic profiles in terms of discriminating between GBMs and oligodendrogliomas was evaluated. The low grade astrocytomas (grade II and III) were excluded from further evaluation due to the low number of samples making statistical analysis unfeasible. Then differences in tumor grade were investigated comparing samples from oligodendroglioma patients with grade II and grade III tumors, respectively. Finally, metabolic patterns associated with survival time were investigated in glioblastomas and oligodendrogliomas separately. From the calculated OPLS-DA models, model weight values (w*), i.e., variable contribution values for the pre-defined sample class separations, were extracted and only variables related to the class separation were included in the final OPLS-DA models (w*_average ± 2 SD). Furthermore, a Mann–Whitney U-test was used to calculate a probability value (p-value) for each included metabolite in relation to the class separations of interest. All models were validated using cross-validation and p-values for the cross-validated model were calculated using CV-ANOVA [46]. A seven-fold cross-validation procedure was applied using 1/7 of the data as the test set, while the remaining 6/7 of the data were modeled and then repeating this seven times. Furthermore, for all OPLS-DA models the number of latent variables (OPLS components) (A), the variation described in the metabolite data (R2X), the between class variation described (R2Y) and the between class variation predicted based on cross-validation (Q2) were reported. When comparing time of survival in GBM, patients that died shortly after diagnosis (≤4 months) were compared to patients that lived long after diagnosis (≥3 years). For oligodendrogliomas, patients that died within 2 years after diagnosis (short survival) were compared to patients that lived for more than 3 years (long survival). Survival groups were selected based on retrospective data from our institution with an expected 4 months survival in GBM of 65% and 3 year survival of only 8%. In oligodendroglioma grade II, 2- and 3-year survival is expected to be 78% and 75%, respectively. For patients with oligodendroglioma grade III expected survival of 2 and 3 years is 50% and 50%, respectively. However, in our consecutively collected material, we did not have enough patients within any clinically relevant time limits. Instead, we had two almost equally sized groups of patients; one group that died within two years of diagnosis and the other group that lived longer than 3 years, which is why those limits were the most inherent to get reliable statistics. All pattern recognition analysis, including cross-validation and CV-ANOVA, was performed in SIMCA (version SIMCA-P + 13.0; Umetrics, Umeå, Sweden). Model plots were created using SIMCA or GraphPad Prism (5.04; GraphPad Software Inc., La Jolla, CA, USA) in combination with Adobe Illustrator CS5 (15.0.0; Adobe Systems Inc., San Jose, CA, USA). To summarize the results, Receiver Operating Characteristic (ROC) curves were calculated for the detected significant metabolic patterns associated with survival. This way of utilizing ROC curves for metabolic patterns as compared to the conventional way using single markers is novel and makes it possible to evaluate the diagnostic and prognostic potential of metabolic patterns or signatures in a way that is familiar to the clinical community. The ROC calculations were performed in ROCCET: ROC Curve Explorer & Tester (www.roccet.ca) [47].

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Figure 3

Overview of the metabolomic workflow. (Upper panel. Left) Raw gas chromatography-time of flight mass spectrometry (GC-TOFMS) data for the analyzed samples makes up a three dimensional matrix with the Time axis being retention time or index for each metabolite linked to the elution from the chromatographic system, the mass to charge (m/z) axis being the mass over charge ration for the molecular fragments detected by the mass spectrometer and the Samples axis being the analyzed samples. (Middle) To obtain pure chromatographic and spectral profiles for relative quantification and identification of metabolites the raw GC-TOFMS data was processed by hierarchical multivariate curve resolution (HMCR), which is a multivariate deconvolution technique especially developed to resolve complex GC-MS based metabolomics data from multiple samples to make it suitable for multiple sample comparisons by means of e.g. pattern recognition approaches. (Right) The area under each resolved metabolite peak makes up the variables of the resulting data matrix (X) used as input for further pattern recognition and statistical analysis. Each column of X represents one resolved metabolite peak over all samples (rows of X). Chemometric bioinformatics based pattern recognition is applied to, X.; e.g. for investigating the difference between two sample classes (turquoise and grey in X). (Lower panel. Left) The sample variation of X is projected in the model scores allowing interpretation of sample distribution patterns. Each symbol in the scores plot represents one sample described by all variables/metabolites (columns of X). As an example, the pink sample symbol relates to the pink row of X. (Right) The variable/metabolite variation is projected in the model loadings allowing interpretation of sample distribution patterns and explanation of variable contribution to patterns in seen in scores. Each symbol in the loading plot represents on variable/metabolite. As an example, the blue symbol relates to the blue column of X as well as the blue resolved metabolite profile in the upper middle frame.

This investigation was supported by grants from the Swedish Cancer Society (H.A., A.T.B.), the Swedish Research Council (H.A.) and the Research foundation of Clinical Neuroscience, Umeå University (A.T.B.).