Metabolomic patterns in glioblastoma and changes during ...wibom/data/paper II.pdf · Manuscript...

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Metabolomic patterns in glioblastoma and changes during radiotherapy – a clinical microdialysis study Carl Wibom, Izabella Surowiec, Lina Mörén, Per Bergström, Mikael Johansson, Henrik Antti, A Tommy Bergenheim Department of Oncology, University Hospital, SE 901 85 Umeå, Sweden (C.W., P.B., M.J.) Department of Chemistry, Umeå University, SE 901 87 Umeå, Sweden (C.W., I.S., L.M., H.A.) Department of Neurosurgery, University Hospital, SE 901 85 Umeå, Sweden (C.W., A.T.B) Correspondence: Carl Wibom, Department of Radiation Sciences, Oncology, Umeå University, S-901 85 UMEÅ, Sweden. E-mail: [email protected] We have employed stereotactic microdialysis to sample extracellular fluid intracranially from glioblastoma patients, before and during the first five days of conventional radiotherapy treatment. Microdialysis catheters were implanted in the contrast enhancing tumour as well as in the brain adjacent to tumour (BAT). Reference samples were collected subcutaneously from the patients’ abdomen. The samples were extracted and analyzed by gas chromatography – time of flight – mass spectrometry (GC-TOF-MS), and the acquired data was processed by hierarchical multivariate curve resolution (H-MCR) and analysed with orthogonal partial least squares (OPLS). To enable efficient analysis of treatment induced alterations to the metabolome, the data was further processed by individual treatment over time (ITOT) normalization. 151 metabolites were reliably detected, of which 67 were identified. We found distinct metabolic differences between the intracranially collected tumour samples and the BAT samples. There was also a marked difference between both tumour and BAT and the subcutaneously collected reference samples. We also observed systematic metabolic changes induced by radiotherapy treatment among both tumour and BAT samples. The patterns of metabolites affected by treatment were different between tumour and BAT, and both contained highly discriminating information, as indicated by ROC values of 0.896 and 0.821, respectively. Our findings contribute to increased molecular knowledge of basic glioblastoma pathophysiology and point to the possibility of detecting metabolic marker patterns associated to early treatment response. Keywords: glioblastoma; radiotherapy; treatment response; predictive metabolomics Introduction

In the search for new treatments for malignant glioma there is an imminent need for an improved understanding of the basic tumour pathophysiology as well as for identification of predictive bio-markers for assessment of therapeutic response. Both the transcriptome and the proteome hold the promise to harbour candidate biomarkers associated to treatment response in malignant glioma (Christensen et al., 2008; Wibom et al., 2006). Lately, also the metabolome has gained interest in this regard. The metabolome is generally defined by all the low molecular weight metabolites in a system and is considered to be downstream both gene and protein expression, and as such reflect processes on both transcriptional and translational levels. There are several options to study the metabolism in various types of tumours. Magnetic resonance spectroscopy (MRS) is one clinically used technique to study selected metabolites in vivo. It holds the advantage of being non-invasive and may therefore be used to repeatedly assess a patient throughout the course of the disease and its treatment. Although metabolomic investigations by MRS

in brain tumour have demonstrated promising results related to diagnosis, prognostic markers and potential markers for therapeutic response (Sibtain et al., 2007), no metabolomic marker nor pattern has so far had a major impact on clinical decision making. MRS may be a powerful tool to study single metabolites in vivo but the limited number of metabolites possible to identify does not allow MRS to be used for detailed studies of the metabolome. Using MRS in vitro on cells or tissue extracts may provide an improved resolution compared to in vivo MRS, and thereby also an increased number of detectable metabolites. This approach has for instance been used to demonstrate metabolomic patterns discriminating between different types of brain tumours (Kinoshita et al., 1994). A fundamentally different approach to study treatment effects at a metabolite level is to perform an unbiased global screening of the metabolome. Gas-chromatography hyphenated with mass-spectrometry (GC-MS) is an established method for this purpose (Dunn et al., 2005; Jonsson et al., 2005), and has successfully been used to detect metabolite abnormalities in cancer in general (Dunn et al., 2005; Sreekumar et al., 2009) as well as to discriminate brain tumour tissue from normal brain tissue

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(Jellum et al., 1981). Brain tissue analysis is however not a feasible approach for longitudinal studies of glioma patients, seeing as tissue collection normally only can be performed once in every patient. In this study we employed stereotactic microdialysis for sample collection, which allows for continuous study of metabolic events in the extracellular space of tumours such as high-grade gliomas (Tabatabaei et al., 2008). Microdialysis samples were collected from the contrast enhancing tumour as well as from the brain-adjacent to-tumour (BAT) region in freely mobilised patients, before and during five days of radiotherapy. The main objective of the study was to investigate the metabolome of malignant glioma and asses the metabolic response to radiotherapy by a predictive metabolomics approach (Jonsson et al., 2006).

Methods

Surgery and microdialysis Eleven patients with radiological suspicion of high-grade glioma considered not suitable for surgical resection were included in the study. The patients underwent a stereotactic biopsy to obtain a tissue diagnosis before non-surgical treatment. The biopsy procedure was carried out under general anaesthesia using the Leksell stereotactic frame (Elekta, Stockholm, Sweden). Biopsies, as well as the implantation of microdialysis catheters, were planned via a stereotactic CT investigation (Roslin et al., 2003). The diagnoses were confirmed by a frozen section before implantation of two microdialysis catheters along the biopsy trajectory, one into the contrast enhancing tumour tissue and one into the brain adjacent to tumour (BAT) region (Figure 1). One catheter was also placed in the abdominal subcutaneous tissue as a reference. The catheters had a 10 mm semipermeable membrane with 100 kDa cut-off (CMA 71; CMA Microdialysis, Stockholm, Sweden). The position of the catheters was postoperatively confirmed on the CT performed for dose-planning. The catheters were connected to a 2.5 mL syringe placed in a micro infusion pump with a flow rate of 0.3 μL/min (CMA 106 or CMA 107; CMA Microdialysis). All catheters were perfused with a Ringer solution (Perfusion fluid T1; CMA Microdialysis) mixed with Dextran (30 g Dextran 60 1000mL-1) to prevent microfiltration (Hillman et al., 2005). The samples were collected in microvials every second hour, thereafter frozen and kept at -80°C until analysed. Patient care and radiotherapy The patients were allowed to recover from surgery for approximately 24 hours at the neurointensive care unit before a CT for dose planning was performed. All patients received routinely perioperative bethametasone and the blood glucose was kept under 8 mmol L-1. Eight patients were planned for a standard radiation schedule of 2 Gy x 30. Three elderly patients in poor general condition were planned for a faster fractionation using 3 Gy x 13 (2 cases) or 3.4 Gy x 10 (1 case). Thus, the dose per fraction during the microdialysis sampling was 2 Gy (8

Figure 1 Coronar reconstruction of dose planning CT showing implanted microdialysis catheters. Two catheters were placed within the contrast enhancing tumor and one in the BAT region (see inset panel for preoperative contrast enhanced coronar CT reconstruction).

cases), 3 Gy (2 cases) or 3.4 Gy (1 case). The gross tumour volume was defined as the contrast enhancing part of the tumour and a margin of 2 cm was added for the planning target volume. The radiotherapy was started within two to five days after the biopsy and catheter implantation. Sampling of the microdialysis fluid continued during five days of irradiation including the morning after the fifth fraction. In a few cases, the microdialysis had to be terminated earlier due to malfunctioning catheters (Table 1). Sample selection Samples for analysis on GC/MS were selected relative to each patient’s individual treatment schedule. Depending on when the catheters were implanted in relation to treatment initiation, as well as on how long a given catheter stayed operational, we were able to follow the patients longitudinally for different durations of time (Table 1). Where applicable, we chose to analyse 3 samples collected before the first radiotherapy session (time points 1-3) and 3 samples collected thereafter; specifically the afternoon after the patient received his or hers first (time point 4), third (time point 5) and fifth (time point 6) radiotherapy fraction. The samples analysed were collected between 4 and 8 pm, and were stored in two separate microdialysis vials. In a separate step, the two vials from each time point of interest were thawed on ice and pooled together to yield sufficient sample volumes for analysis. The pooled samples were then once again stored at -80°C prior to extraction. Chemicals The chemicals used for sample preparation were all of analytical grade, except where otherwise stated. The stable isotope-labelled 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). Stock solutions of the IS were prepared either in purified and

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Table 1 Sample overview

Day -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Pat 1 I rt1 rt2 rt3 rt4 rt5

T x x x BAT x x x x x x SC x x x x x x

Pat 2 I rt1 rt2 rt3 rt4 rt5 T x x x x x

BAT x x x x x SC x x x x x x

Pat 3 I rt1 rt2 rt3 rt4 rt5 T x x x x x x

BAT x x x x x x SC x x x x x x


BAT x x x x x SC x x x x x


BAT x x x x SC x x x x x x

Pat 6 I rt1 rt2 rt3 T x x x x

BAT x x x x SC x x x


BAT x x x x SC x x x x x









172 samples in total (T=57, BAT=56, SC=59). I = Catheter Implantation (typically around 12 noon). x = Sample collected for analysis (between 4 and 8 pm).

deionised water (Milli-Q, Millipore, Billerica, MA) or in methanol (J.T. Baker, Deventer, Holland) at the same concentration, 0.5 μg μL-1. 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). Sample preparation Just prior to extraction, the samples were allowed to thaw at 37°C for 15 min. 450 μL of the extraction solution (methanol/water (8:1) with 11 IS, each of the concentration 7 ng μL-1) was then added to 50 μL of the microdialysate. The mixtures were vortexted for approximately 10 seconds thereafter vigorously extracted at a frequency of 30 Hz for 1 min, using a MM301 vibration Mill (Retsch GmbH & Co. KG, Haan, Germany). After 120 min on ice, the samples were centrifuged at 19600 g for 10 min at 4°C. A 200 μL aliquot of supernatant was transferred to a GC vial and evaporated to dryness. Methoxymation with 30 μL of methoxyamine solution in pyridine (15 μg μL-1) was carried out at room temperature for 16 h. Finally, the samples were trimethylsilylated with 30 μL of MSTFA at room temperature for 1 h, after which 30 μL of heptane (containing 0.5 μg of methyl stearate as injection IS) were added.

GC-MS Prior to analysis by GC-MS, the samples were divided into two separate batches, where samples from patients 1-5 constituted batch 1 (82 samples), and samples from patients 6-11 constituted batch 2 (116 samples). The samples in batch 1 were analyzed in the first GC-MS run and the samples in batch 2 in the second GC-MS run, which took place the following day. The run order within each batch was randomized. A 1 μL aliquot of derivatized sample was injected splitless by an Agilent 7683 Series autosampler (Agilent, Atlanta, GA) into an Agilent 6980 GC equipped with a 10 m x 0.18 mm i.d. fused-silica capillary column chemically bonded with 0.18 μm DB5-MS stationary phase (J&W Scientific, Folsom, CA). The injector temperature was set at 270°C. Helium was used as carrier gas at a constant flow rate of 1 mL min-1 through the column. For every analysis, the purge time was set to 60 s at a purge flow rate of 20 mL min-1 and an equilibration time of 1 min. The column temperature was initially kept at 70°C for 2 min and then increased from 70 to 320°C at 30°C min-1, 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). The transfer line temperature was set at 250°C and the ion source temperature at 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-1, and the acceleration voltage was turned on after a solvent delay of 165 s. Files of acquired data were exported to MATLAB 7.3 (R2006b) (Mathworks, Natick, MA) in NetCDF format for further data processing and analysis. Hierarchical multivariate curve resolution All data pre-treatment procedures, such as baseline correction, chromatogram alignment, time-window setting, and hierarchical multivariate curve resolution (H-MCR) (Jonsson et al., 2005) were performed in MATLAB, using in-house scripts. The data acquired from the samples in batch 1 were subjected to H-MCR. Alignment and smoothing using a moving average was performed prior to dividing the chromatograms into 66 time windows from which a total of 183 chromatographic profiles (peaks, i.e. putative derivatized metabolites) with corresponding mass spectra were resolved. The samples from the second batch were then predictively resolved according to the H-MCR parameters obtained from the first batch, meaning that the same metabolites were quantified in the same way using the resolved mass spectra from batch 1. This is a fast and efficient procedure for processing large series of metabolomic GC-MS data, with maintained high data quality, that has proven useful in global screening and for building diagnostic systems based on metabolic patterns (Chorell et al., 2009; Jonsson et al., 2006; Pohjanen et al., 2007). Prior to further multivariate analysis, all peak areas were normalized using the peak areas of the 11 IS that eluted over the whole chromatographic time range. Ultimately, the complete H-MCR process resulted in a data table (X), where samples are represented by rows and metabolites by columns. Each cell in the table corresponds to the relative

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concentration, i.e. the calculated area under the deconvoluted chromatographic peak, of a specific metabolite in a specific sample. Furthermore, each resolved compound is associated with a corresponding mass spectral profile, which can be used for identification of metabolites. For identification mass spectra of all detected compounds were compared with spectra in the NIST library 2.0 (as of January 31, 2001), the in-house mass spectra library database established by Umeå Plant Science Center, or the mass spectra library maintained by the Max Planck Institute in Golm (http://csbdb.mpimp-golm.mpg.de/csbdb/gmd/gmd.html). Orthogonal partial least squares Orthogonal partial least squares (OPLS) is a supervised multivariate data projection method used to relate a set of predictor variables (X) to one or more responses (Y) (Trygg & Wold, 2002). By employing a response matrix (Y) that represents predefined sample classes, this method can be used for discriminant analysis (OPLS-DA) to predict class identity and to extract specific features distinguishing between the predefined sample classes. OPLS operates by dividing the systematic variation in X into two parts: one part that is linearly related to Y, and thus can be used to predict Y, and one part that is uncorrelated (i.e. orthogonal) to Y. In this process, each variable in X is associated with a weight, w*, which represents the variable’s covariation with Y. All calculated OPLS components were validated by seven-fold full cross-validation (Stone, 1974). In addition, the cross-validation process was used to assess the models ability to predict the response variation, expressed by the term Q2. Data pre-treatment and analysis strategies The data matrix (X) produced by H-MCR was scaled to unit variance and screened for inconsistencies, using both principal component analysis (PCA) (Wold et al., 1987) and orthogonal partial least squares discriminant analysis (OPLS-DA) with leave-one-out cross-validation. Whenever evidently deviating samples or metabolites were encountered, the original data was properly scrutinised and appropriate action was taken. Data from samples collected prior to treatment initiation (time point 1-3) was studied for difference between sample types using PCA and OPLS-DA. Thereafter, to enable for analyses focused on treatment induced changes, the data in X was subjected to an additional pre-treatment step. Samples from the same patient and sample type were categorised as either treated or untreated. Within each group of untreated samples, the obtained value from the first collected sample (time point 1 or 2) was subtracted from all samples in the group (including itself). Thereafter, the original value from time point 3 was copied and included as the first value in the group of treated samples, before the treated samples were subjected to the same transformation. The described procedure was carried out individually for each metabolite, and will herein be referred to as Individual Treatment Over Time (ITOT) normalization. Thereby, all

metabolites were set to begin at zero for the first sample in both the treated and the untreated group, for each patient and sample type. The result was stored in a separate matrix (XITOT). XITOT was subsequently modelled by OPLS-DA in a series of steps to extract metabolites affected by treatment. The analyses were performed on tumour samples and BAT samples individually, and consisted of the following steps: (i) Exclusion of variables unaffected by treatment, excluding variables with low model weight values (|w*|<0.05) in an OPLS-DA model against treatment. (ii) Exclusion of variables displaying similar correlation to treatment also in the subcutaneously collected reference samples. This was achieved by means of two different OPLS-DA models with treatment as response, one based on SC samples and one on tumour/BAT samples. In a specific versus unique structures (SUS) plot (Wiklund et al., 2008), the w* vectors from the two models were compared and variables affected by treatment in both models were discharded. The cut-off values used were |w*|>0.05 for correlation in tumour/BAT samples and |w*|>0.1 for correlation in SC samples. (iii) Finally metabolites whose variation in concentration was similar before and after treatment was excluded. The remaining variables were analyzed in two separate OPLS-DA models, where the response variables were treatment and sampling sequence, respectively. By means of the SUS-plot, variables correlated to both treatment and sampling sequence were excluded (using the same cut-off limits as in step ii). Following this series of exclusion steps, the remaining metabolites were considered interesting as descriptors of treatment effect. To visualize the metabolic pattern associated with treatment, a final OPLS-DA model for each of the sample types (tumour and BAT) based on the selected metabolites was calculated and the cross-validated score plots from these models are presented herein for visualization and interpretation. This combination of predictive data processing using H-MCR and multivariate predictions, here by means of cross-validated OPLS-DA scores, is known as predictive metabolomics and has been developed for, and recently successfully applied to, studies of the human metabolome (Chorell et al., 2009; Pohjanen et al., 2007; Wuolikainen et al., 2009). Validation and evaluation The metabolites that were highlighted as interesting in the analyses described above were further evaluated by two different methods for assessing significance. The first was based on a 95% confidence interval (CI) for each variable’s model loading (w*). The CI was estimated by jack knifing, and variables whose CI did not contain the value zero were considered as significant. The second significance test was a standard paired Student’s t-test, where p-values < 0.05 were considered significant. In figure 3, differences in metabolite composition between the investigated sample types are illustrated. For all selected metabolites, the abundance level found in each sample is plotted,

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expressed in terms of standard deviations from the mean. This was calculated by subtracting the mean value of one sample type from each individual value, and dividing the difference by the standard deviation of the same sample type. Ethics All patients participated voluntarily after informed consent. The study was approved by the ethics committee of the Umeå University. Results

Data processing 164 chromatographic profiles (peaks, i.e. putative derivatized metabolites) with corresponding mass spectra were resolved using H-MCR. Careful monitoring of data quality revealed 9 compounds, detected only in a few samples, likely to be artefacts of the derivatization. Another 4 metabolites were not processed correctly, thus appearing in only one of the two analysis batches. These compounds were all removed prior to further analysis. Consequently, 151 metabolites were reliably detected and quantified, whereof 67 were identified (44%). It should be mentioned that the identity assigned to a few sugar isoforms were slightly uncertain (these are marked with an asterisk in tables and figures). Furthermore, in a few instances we found one or a few samples that displayed extreme values for a specific compound, as compared to the values in the other samples. These extreme values were replaced by the mean value of that specific variable, calculated on the same type of sample collected at the same time point from the remaining patients. Finally, two separate tumour samples, from two different patients, each collected as the last sample in the sample series displayed a metabolic profile unlike all the other samples. These samples were both excluded from further analysis. Differences between sample types Unsupervised PCA of the metabolic profiles of samples collected prior to treatment onset (tp1-3), revealed that the largest variation in the data was that between samples from the subcutaneously located catheter and the intracranial catheters (data not shown). These differences were studied in greater detail by means of OPLS-DA. By pair-wise comparisons between

Figure 2 Cross-validated scores from an OPLS-DA model based on 127 metabolites highlighted as possibly differentially expressed between the sample types. The labels represent the number assigned to the patient from whom the sample was taken. The model consisted of 2 predictive and 4 orthogonal components, predicting 73.8% of the variation in Y (Q2 = 0.738).

Table 2 Identified compounds differing between compartments

Arrows denote that a compound was highlighted as interesting by OPLS-DA. The direction of the arrow represents higher or lower abundance within the compartment listed first in the column heading. Sign - Denotes the outcome of the significance tests, according to the criteria P-value<0.05 and C.I. excluding zero; †† = fulfilling both criteria; † = fulfilling one criteria; - = not fulfilling any criteria; Asterisks (*) by the name of sugar compounds denote a degree of uncertainty in the assigned identity.

sample types extracting all variables of interest from each analysis, 127 metabolites were highlighted as interesting. These metabolites were then selected for further analysis by OPLS-DA, using a response matrix (Y) consisting of three dummy variables, each representing one sample type. The resulting, cross-validated scores indicated that there were clear, systematic differences between the metabolic profiles from the different sample types (Figure 2). Figure 3 illustrates the abundance of all metabolites that were selected based on their w*-values in the separate OPLS-DA models, and the value and significance of all identified metabolites are listed in table 2. Changes in the metabolic pattern induced by radiotherapy

The average abundance of a given metabolite within a specific sample type could vary substantially between patients. In addition, the time during which a catheter had been implanted

Average Relative Concentration T vs. BAT

T vs. SC

BAT vs. SC

Compund BAT SC T Δ sign Δ sign Δ sign 2.4-dihydroxybutanoic acid 202 911 272 823 136 267 ↓ † ↓ †† ↓ †† 3.4-dihydroxybutanoic acid 32 736 53 257 44 486 ↑ † 3-chlorobenzoic acid 32 503 67 249 25 371 ↓ †† ↓ †† 3-cyanoalanine 107 788 159 911 214 673 ↑ †† ↑ † ↓ - 3-hydroxybutanoic acid 1 864 220 1 504 622 2 414 777 ↑ †† ↑ † Alanine 168 396 343 432 474 924 ↑ †† ↑ - ↓ † Allothreonine 246 665 366 796 542 763 ↑ †† ↑ † ↓ - Arabinose* 699 892 63 635 287 283 ↓ †† ↑ †† ↑ †† Arabitol 1 680 940 270 862 588 752 ↓ †† ↑ † ↑ †† Beta-Alanine 15 572 62 245 36 472 ↑ †† ↓ - ↓ †† Citric acid 1 109 436 150 103 787 325 ↓ † ↑ †† ↑ †† Creatinine 2 719 845 1 245 290 2 315 804 ↑ †† ↑ †† Cystathionine 29 162 3 077 20 104 ↓ † ↑ †† ↑ †† D-Fructose 2 569 177 1 742 844 1 940 745 ↓ †† ↑ †† D-Galactose* 15 804 160 18 871 934 12 711 829 ↓ † ↓ †† ↓ - D-Glucose 2 762 911 3 310 143 2 334 773 ↓ † Diisooctyl phthalate 130 484 150 796 136 983 ↓ † ↓ †† D-Mannitol 5 964 282 8 088 837 3 853 553 ↓ † ↓ †† ↓ † Glultamate 159 032 22 053 208 585 ↑ †† ↑ - Glutaric acid 15 404 15 525 38 460 ↑ †† ↑ †† Glyceric acid 594 471 223 095 591 968 ↑ †† ↑ †† Glycerol 2 001 252 4 404 486 1 838 296 ↓ †† ↓ †† Glycine 1 645 524 3 783 026 5 057 644 ↑ †† ↓ † Inositol 2 078 189 1 757 466 270 ↓ †† ↑ † ↑ †† Isomaltose 485 501 631 880 315 516 ↓ † ↓ †† ↓ † Itaconic acid 55 812 21 254 42 714 ↑ †† ↑ † Lactulose* 192 908 262 183 118 336 ↓ † ↓ †† ↓ † L-Arginine 203 668 237 221 281 187 ↑ † L-Asparagine 90 080 112 986 144 435 ↑ † L-Cysteine 19 616 66 091 33 953 ↑ † ↓ † ↓ † L-Glutamine 2 461 236 864 958 2 627 885 ↑ †† ↑ †† L-Lysine 2 482 214 3 495 133 4 668 623 ↑ †† ↑ † ↓ - L-Ornithine 589 596 878 991 1 585 840 ↑ †† ↑ † L-Phenylalanine 1 375 970 1 452 279 2 545 271 ↑ †† ↑ † L-Serine 1 684 536 1 324 614 3 384 732 ↑ †† ↑ †† L-Threonine 1 758 235 2 532 323 3 777 324 ↑ †† ↑ † ↓ - L-Tryptophan 98 217 107 716 195 915 ↑ †† ↑ † L-Tyrosine 233 010 437 600 422 984 ↑ † ↓ † L-Valine 686 844 602 974 1 097 056 ↑ †† ↑ †† ↓ † Maltitol* 62 757 76 648 39 838 ↓ †† ↓ †† ↓ † Myo-Inositol 16 955 314 1 434 632 7 014 867 ↓ †† ↑ †† ↑ †† Nigerose* 23 161 27 523 16 232 ↓ † ↓ †† Palatinose 127 131 183 336 77 836 ↓ † ↓ †† ↓ † Palatinose* 253 893 326 475 154 891 ↓ † ↓ †† ↓ † Pentonic acid 916 489 434 468 542 101 ↓ †† ↑ †† Putrescine 36 864 39 370 33 329 ↓ † Pyroglutamic acid 4 512 366 1 766 258 5 044 492 ↑ †† ↑ †† S-Methyl-L-cysteine 37 271 48 624 61 476 ↑ †† Trehalose 88 951 137 495 61 548 ↓ †† ↓ †† ↓ †† UPSC10009 122 198 145 529 194 155 ↑ †† UPSC10013 204 467 101 331 142 968 ↓ †† ↑ †† ↑ †† Urea 5 334 568 4 258 649 5 606 030 ↓ † ↑ † ↑ † Xylose* 181 500 154 449 144 585 ↓ - Xylulose 129 494 44 249 103 477 ↓ † ↑ †† ↑ ††

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Figure 3 All samples collected prior to treatment (time points 1-3) plotted in terms of standard deviations from the mean, for each metabolite highlighted as interesting based on OPLS-DA model weights. The grey areas represent values greater than the extent of the horizontal axes. a) Samples collected in BAT compared to subcutaneous (SC). b) Samples collected in tumour (T) compared to BAT. C) Samples collected in tumour compared to subcutaneous.

could potentially confound the interpretation of a true treatment response. Thus, a new dataset was constructed (XITOT), which allowed the analysis to focus on the differences between samples collected before and after onset of treatment, irrespective of the abundance difference between patients and un-confounded from the linear time effect originating from the catheter implantation. XITOT was modelled by OPLS-DA in several steps to exclude metabolites that (i) were unaffected by treatment, (ii) were systematically affected (i.e. displayed similar correlation to treatment in the subcutaneously collected reference samples), and (iii) showed the same trend before treatment initiation as after. These analyses steps were performed individually for tumour and BAT samples, and highlighted 55 metabolites of interest in the tumour samples as well as 72 in the BAT samples. Based on these numbers, similarities and dissimilarities between tumour and BAT samples are illustrated by the Venn diagram in figure 4, and all highlighted metabolites with assigned identities are listed in table 3. Moreover, to obtain an overview of how the complete patterns, constituted by the metabolites of interest, were altered by

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ctions represent metabolites found positively (Pos.) or negatively (Neg.) rrelated to treatment in tumour (T) or BAT samples.

eatment, they were investigated in greater detail by OPLS-DA. he cross-validated scores from these models are presented in gure 5. Subsequently, the cross-validated score values xcluding the first value for each patient in both the treated and

ntreated group, i.e. those set to zero in the ITOT ormalization) were used in ROC-curve calculations. The btained areas under the ROC-curves for tumour and BAT mples were 0.896 and 0.821, respectively.

OT-normalized data from selected metabolites of interest are lotted in figure 6. For instance, alanine that displayed the rongest positive correlation to treatment among the tumour mples. Although the abundance has increased following eatment in most patients, some patients display a dissimilar attern.

iscussion

this study, utilising a GC-MS-based predictive metabolomics reening approach, we reliably detected a total of 151

s in tumour as mpared to BAT tissue, and both were found highly dissimilar

esults difficult. Our alyses highlighted a total of 79 metabolites whose abundance

patterns in the different analysed compartments. The iscriminating abilities of the patterns were strong, as

demonstrated by ROC values of 0.896 and 0.821 for treatment-

changes in tumour and BAT, respectively. These findings indicate that metabolomic patterns as such may be of

Figure 4 A Venn-diagram representing all 127 metabolites marked as possibly affected by treatment in tumour and/or BAT samples. The different seco

trTfi(eunosa ITpstsatrp D

Inscmetabolites (67 of which were identified) in microdialysate samples obtained from glioblastoma and BAT tissue. We observed distinctly different metabolic patterncoto the subcutaneous tissue that was used as an extracranial control. This is in concordance with previous magnetic resonance spectroscopy (MRS) studies reporting differences in metabolite expression between different brain tumour tissue types ex vivo (Erb et al., 2008) as well as in vivo (Fountas et al., 2000; Likavcanova et al., 2005). However, MRS based investigations typically focus on a few selected metabolites, which make direct comparisons to our ranlevels differed significantly between tumour and BAT samples (52 identified), and 95 that differed significantly between tumour and subcutaneous tissues (41 identified). Furthermore, during the first week of radiotherapy, we found an early metabolic response to treatment both in tumour and in BAT, with different response d

induced pattern

value as biomarkers related to radiotherapy treatment effects in individual patients. Glucose is the main energy source for the brain and brain tumours are considered to utilize glycolysis for their energy metabolism. With regard to changes in glucose metabolism we found that the glucose level was lower in tumour tissue than in BAT and subcutaneous tissue, which is in accordance with previous studies performed both in animal models (Griffin & Kauppinen, 2007; Gyngell et al., 1992) and in patients, as previously reported by our group (Roslin et al., 2003; Tabatabaei et al., 2008). Furthermore, amino acids were generally observed to be more abundant in the tumour compartment compared to that of BAT. All six of the identified essential amino acids (L-Threonine, Allothreonine, L-Tryptophan, L-Arginine, L-Lysine, L-Valine) were found to be more abundant in tumour than in BAT, as were most of the identified non-essential amino acids. Elevated levels of amino acids in high-grade glioma has reported by several other groups (Behrens et al., 2000; Florian et al., 1995; Griffin & Kauppinen, 2007; Gyngell et al., 1992; Maxwell et al., 1998; McKnight, 2004), however the biological reason for these findings is to our knowledge not known. One might however Table 3 Identified compounds affected by treatment Tumor BAT Compound Corr. Δ-fold Sign Corr. Δ-fold Sign 3-chlorobenzoic acid ↑ 0,3 - ↑ 0,4 - Alanine ↑ 1,7 †† Arabinose* ↓ -2 - ↑ 1,1 † Arabitol ↑ 1,7 †† Citric acid ↑ 3 † Creatinine ↑ 103,6 - Cystathionine ↑ 2,5 † D-Fructose ↑ 0,7 - D-Galactose* ↓ -5,7 - D-Glucopyranose ↑ 1,8 † D-Glucose ↑ 12,8 - Ethanolamine ↓ -1,9 - ↑ 0,9 † Glultamate ↑ 0,7 - ↑ 1 - Glutaric acid ↑ 1,6 - Glyceric acid ↑ 0,8 - Glycerol ↑ 0,7 † Inositol ↑ 1,6 † Isomaltose ↑ 1,4 - Itaconic acid ↑ 2,6 - ↑ 4,5 † Lactulose* ↑ 1,5 - L-Glutamine ↑ 366,2 - ↑ 19,5 † L-Ornithine ↓ -1,6 † ↓ -0,7 - L-Tyrosine ↓ -3,5 - Nigerose* ↑ 13,4 † Palatinose ↑ 1,3 - Palatinose* ↑ 1,5 - Pentonic acid ↑ 3,2 †† Putrescine ↓ -0,9 - S-Methyl-L-cysteine ↓ -1,4 - Stearic acid ↑ 2 † ↓ -2 - Succinic acid ↓ -8,4 - Trehalose ↑ 2,7 - UPSC10009 ↓ -0,5 - UPSC10013 ↑ 10,5 - ↑ 1 - Urea ↓ -1,2 - ↑ 8 - Xylulose ↑ 2,6 † Corr = correlation to tr t e atio eg re

a ted o OT-n lized ta, an d t e value, accordin o: (T -CT bs(CT ); Sign Denotes the

he significanc ests, a ding to the crit va 05 a C.I. zero; †† = fulfi g both iteria; fulfil one ria; -

criteria; Aster s (*) the na of su omp de a certainty in th ssigned entity.

eatmen (↑: positiv correl n / ↓: n ative cor lation);Δ-fold = Fold change c lcula n IT orma da d relate o thuntreated g t REAT RL)/a RL - outcome of t e t ccor eria P- lue<0. nd excluding llin cr † = ling crite = notfulfilling any isk by me gar c ounds note degree of un e a id

7

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8

at it is o importance and reflective of a whereby nst ts fo rogression o his h ly

our are supplied.

s a non- entia ino aittor which no ly is restric d to syn tic

synaptic space of glutamateric synapses. It has also been in several aspects of glioma tumour biology,

e invasiv roce d the high frequency of seizures ade gliom pa . Whereas orm stro tes tamate from the acellular space, glioma cells have

onstrated to re glutamate to a concentration to cause w espre excitotoxic death among normal

e & So heime 1999 This roce has n a par f th asiv roces romoting tumour s et ., 20 Sontheimer, 2008; Takano et al.,

released utamain and activate glutamate neuronal torsuce seizu (So mer 008). ken ether t is

rising that we found glutam e to be more abundant in

for the high frequency of

o BAT, in accordance with revious studies (Kinoshita & Yokota, 1997).

d in the formation f diacylglycerol and inositol 1,4,5-triphospate. The resulting

oints, and the asterisk (3*) denotes that me point 3 was used as a reference point in ITOT normalization of the

ositol 1,4,5-trisphosphate releases the Ca2+ thereby making the

could be a decrease in the formation of inositol isphosphate and inositol hexaphosphate leading to poor

speculate th f functionalmechanism co ituen r p f t ighproliferative tum Glutamate i ess l am acid and a m jor excitatory neorutransm rmal te the apand periimplicatedincluding th e p ss anin high-gr a tients n al a cyremove glu extrbeen dem lease sufficient id ad neurons (Y nt r, ). p ss beesuggested to be

(Lyont o e inv e p s p

expansion al 07; 2001). The gl ate is also proposed to diffuse into normal br recep and thereby ind res nthei , 2 Ta tog , inot surp attumour tissue compared to BAT, which also confirms previous findings where glutamate was found considerably more abundant in tumour than in normal brain (Bergenheim et al., 2006). Another amino acid that has been suggested to be epileptogenic and partly responsibleseizures in glioma patients is glycine (Behrens et al., 2000; Sierra-Paredes et al., 2001), whose abundance we also found to be elevated in tumour compared tp Following radiotherapy we found that both glutamate and glutamine levels were increased in intracranially collected samples. To our knowledge this is the first report of this post-radiation effect in patients with high-grade glioma. Considering that glutamine is involved in several major metabolic and proliferative processes, including DNA and protein synthesis, one may hypothesize that this observation is a sign of reduced proliferation in these tissues. Moreover, glutamate has been suggested to be a marker for ischemic and traumatic brain injury (Hillered et al., 2005; Nordmark et al., 2009), hence the radiation-induced glutamate increase may possibly reflect a release from tumour or astrocytic cells damaged by radiation. Myo-inositol is an important intracellular compound and second messenger in intracellular signalling. In this study, we found myo-inositol at greater abundance in BAT than in tumour, however, both BAT and tumour contained considerable higher levels than subcutaneous tissue. This is in agreement with previous studies that report higher levels of myo-inositol in glioblastoma both compared to low-grade astrocytoma (Majos et al., 2003) and compared to normal white matter (Hattingen et al., 2008; Maxwell et al., 1998). Myo-inositol is known to contribute to the formation of a phosphorylated form of phosphatidylinositol, which in turn is involveodiacylglycerol activates protein kinase C and a cascade of proteolytic enzymes, including matrix metalloproteases which

Figure 5 Cross-validated score values (vertical axes) from the OPLS-DA models based on the 55 and 72 most interesting metabolites affected by treatment in tumour (upper panel) and BAT (lower panel) samples, respectively. The horizontal axes represent individual patients. The labels denote the sequential sampling ptitreated samples. The OPLS-DA model of tumour samples consisted of 1 predictive and 1 orthogonal component and predicted 35.5% of the response variation (Q2 = 0.355). The OPLS-DA model of BAT samples consisted of 1 predictive component that predicted 24.4% of the response variation (Q2 = 0.244).

are highly involved in the process of tumour invasion (Uhm et al., 1996). Following radiation, we found an increased level of inositol in both tumour and BAT. Inositol together with its most prominent naturally occurring form, myo-inositol, are the basis for a number of signalling and secondary messenger molecules. One of the hallmarks of cancer is the imbalance of signals that control cell survival and cell death, where apoptosis is one of the regulating processes. Szado et al have demonstrated that stimulation of the inositol 1,4,5-trisphosphate receptor by incells more susceptible to apoptotic stimuli (Szado et al., 2008). Also inositol is a substrate for inositol hexaphosphate which in model systems has been shown to down regulate survival factors such as BIRC-2 and telomerase and upregulate calpain and caspase-3 which stimulates apoptosis thereby reducing the viability of tumour cells in vitro (Karmakar et al., 2007). One possible mechanism for the increase in inositol observed in this study trsensibility for apoptotic stimuli. If this hypothesis is correct it may provide one explanation for the relative resistance for glioma cells to radiation induced apoptotic cell death. Ethanolamine and glycerol are basal parts of cell-membranes and have been suggested to be a markers of membrane degradation (Hillered et al., 1998). Radiation has been shown by MRS to increase phospho-ethanolamine in experimental RIF-1 tumours, which was suggested to reflect a radiation-induced breakdown of membrane-phospholipids (Mahmood et al., 1995). Here, we found the level of ethanolamine to be decreased in tumour, whereas both ethanolamine and glycerol were increased in BAT. This points to a radiation-induced breakdown of

Manuscript

Figure 6 ITOT normalized data from three selected compounds. Samples from the same patient belonging to the same group (treated (gray) or untreated (black)) are connected. The horizontal axes represent the individual patients, and the sequentially collected samples from each patient are plotted from left to right. Untreated samples are plotted in black and treated samples in grey.

membrane that may be induced in BAT but not in tumour. Together with the increase in glucose and urea in BAT it seems that radiotherapy induced a more pronounced katabolic situation in BAT than in tumour. In this study we identified S-methyl-L-cysteine, with a higher

a ethylated-DNA-[protein]-cysteine S-methyltransferase (Foote

f DNA in tumour and BAT. his DNA repair process is similar to the one mediated by O6-

curve resolution method (H-MCR) and the multivariate statistical methods (PCA and OPLS-DA) to allow for a reliable interpretation and validation of detected metabolite patterns based on representative data (Jonsson et al., 2006). Here, we used 82 samples from five individual patients analysed in the first GC-MS batch as a training set for the H-MCR processing, while the

training set parameters. In this way the same metabolites were detected, and quantified in the same way, in the samples from

etabolites. In the present study we used the redictive processing to update the 82 training set samples with

level in tumour than in subcutaneous tissue. S-Methyl-L-cysteine is together with DNA the end product of the demethylation reaction of DNA containing methylguanine catalysed by

remaining 116 samples from six individual patients analysed in the second GC-MS batch were predictively resolved based on the

met al., 1980). Since it is a stochiometric reaction and the S-methyl-L-cysteine residue irreversibly inactivates the protein, allowing only one transfer for each protein, our finding indicates a high degree of demethylation oTmethylguanine–DNA methyltransferase (MGMT) that clinically have been shown to influence the prognosis and treatment effects of especially alkylating substances such as temozolomide in the treatment of high-grade glioma (Hegi et al., 2005). In this regard, S-methyl-L-cysteine as a detectable metabolite is highly interesting. Following radiotherapy, we found that S-methyl-L-cysteine decreased in tumour tissue which may suggest that the methylated-DNA-[protein]-cysteine S-methyltransferase mediated demethylation process is hampered by the treatment. This finding could provide an additional explanation for the reported combined antitumoral effect of radiation and temozolomide (Chakravarti et al., 2006; Kil et al., 2008; Stupp et al., 2005). Maybe also the extent of radiation-induced decrease of methyl-L-cysteine could serve as an early predictor of treatment effect when giving an alkylating agent together with radiotherapy. The predictive metabolomics strategy used as a means for data processing and analysis combined the predictive features of the

both batches. Since this predictive processing is extremely fast compared to the training set processing, it allows processing of large sample quantities in a short time without compromising the data quality. In addition, this also gives possibilities for treating longitudinal samples exactly the same way every time, which is a prerequisite for diagnostic modelling based on patterns of mpanother 116 samples for further multivariate analysis of the combined data set. Since it would have been computationally impossible to perform the curve resolution on all 198 (82+116) samples simultaneously, the predictive approach was crucial for obtaining high quality data for the full sample set. For the multivariate classification analysis using OPLS-DA we applied cross-validated model scores for visualization and interpretation of the detected metabolite patterns of interest. Thus, we based all our conclusions from the models on predicted values, which greatly decreased the risk for overestimation of the data. A key step in the data analysis was the normalization of the data using the ITOT approach, which allowed a facilitated visualization and interpretation of treatment induced metabolic changes in the individual patients over the time course from catheter implantation. Hence, it was possible to compare

9

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individual patients response to treatment irrespective of differences in average abundance of specific metabolites between patients and to distinguish solely time related metabolic pattern changes, originating from how long the catheter has been implanted, from treatment specific patters on an individual patient basis. Without the ITOT normalization this would not have been possible and would have led us to draw false conclusions. It was evident in the presented results that the magnitude of the metabolic response to treatment differed between patients. This is not surprising since it is known that individuals do respond differently to treatment and rather points out how the presented methodology can be used to follow the effect of treatment on an

dividual patient basis. The multivariate statistical analysis was

C-MS data of microdialysis samples to detect etabolic changes as a result of radiotherapy treatment in

of the findings in this microdialysis study flecting the extracellular space concords with the findings from

es in clinical edicine. However, some of the metabolites that significantly

rgenheim, A.T., Roslin, M., Ungerstedt, U., Waldenstrom, A., Henriksson, R. & Ronquist, G. (2006). Metabolic manipulation of

by retrograde microdialysis of L-2, 4 B). J Neurooncol, 80, 285-93.

2966-77.

inuseful for highlighting metabolic patterns of relevance. However, for detecting and validating single biomarkers univariate statistical testing was used as a complement. In our opinion this combination of multivariate and univariate statistical criteria for detecting features of interest in metabolomic data is a requirement for being able to extract and validate both patterns of and single metabolites of predictive value. In addition, this also works to justify the results for readers coming from different scientific communities, which is an issue that should not be neglected. Predictive metabolomics has earlier been successfully applied to human studies (Chorell et al., 2009; Pohjanen et al., 2007). However, this is the first time it is shown how it can be applied to Gmindividual patients. The present study utilises micro-dialysis as a technique to sample molecules from the extracellular fluid longitudinally in patients during treatment. The method is unique in the sense that it makes analysis of relative metabolite concentrations possible almost in real time. However, when discussing the findings in our study we have to be aware that the results are reflecting the composition of the extracellular space and not the intracellular space. There is a possible discrepancy between metabolite concentrations intracellular and the concentrations observed in the extra cellular fluid. Due to the dialysis method used, the results are also is influenced by the recovery rate of the dialysis catheters. In vitro we have earlier estimated this recovery to be approximately 100% for small molecules such as glucose, while the recovery for other molecules such as amino acids seems to be lower. The recovery for glutamate was 76%. In any case, the recovery rate from the different catheter locations will probably be similar which makes comparison between them relevant, although the absolute values may be slightly different. Interestingly, many reH-MRS studies analysing a tissue sample including both the intracellular and the extracellular compartments. This further validates the results presented in the present study. Our findings clearly demonstrate that by using microdialysis and GC-MS in combination with predictive metabolomics it is

possible to study changes in the tumour metabolome during treatment in individual patients. By increasing our knowledge of metabolic responses to treatment, new treatment targets may be discovered. However, to draw conclusions regarding radiotherapy induced changes for specific metabolites is in general difficult. The variations can often be reflective of a change in synthesis, consumption or supply. Many metabolites may also be affected secondary to other metabolic processes or membrane related radiation changes. Nevertheless, an important issue for future research is the highly significant and predictive changes found among the metabolic patterns of many metabolites, in comparison to the less predictive value from changes in individual metabolites that were found. It seems that the tissue metabolomic response to RT is complex and that can be difficult to explain treatment response by changes in just one or few metabolites. Thus it may be more rewarding to investigate biomarker patterns rather than individual moleculmcontributed to the predictive patterns discovered also did show a high degree of inter-individual consistency in response to treatment and some of those may have the potential to constitute future biomarkers. In addition, our study provides important novel information regarding the basic metabolome in high-grade glioma, information that enhances our knowledge of the pathophysiology of glial tumours.

Acknowledgment

The study was supported by grants from Lion’s Cancer Research Foundation, the research foundation of Clinical Neuroscience, the research foundation of Umeå University, Kempe foundation, Umeå, Sweden, as well as from Dagmar Ferbs foundation, Karolinska Institute, Stockholm, Sweden and the Swedish research council, Stockholm, Sweden. Kristin Nyman and Sonja Edvinsson are acknowledged for their skilful technical assistance. Special thanks to Annika Nordstrand for expertly completing the artwork and formatting.

References

Behrens, P.F., Langemann, H., Strohschein, R., Draeger, J. & Hennig, J. (2000). Extracellular glutamate and other metabolites in and around RG2 rat glioma: an intracerebral microdialysis study. J Neurooncol, 47, 11-22.

Be

glioblastoma in vivo diaminobutyric acid (DA

Chakravarti, A., Erkkinen, M.G., Nestler, U., Stupp, R., Mehta, M., Aldape, K., Gilbert, M.R., Black, P.M. & Loeffler, J.S. (2006). Temozolomide-mediated radiation enhancement in glioblastoma: a report on underlying mechanisms. Clin Cancer Res, 12, 4738-46.

Chorell, E., Moritz, T., Branth, S., Antti, H. & Svensson, M.B. (2009). Predictive metabolomics evaluation of nutrition-modulated metabolic stress responses in human blood serum during the early recovery phase of strenuous physical exercise. Journal of Proteome Research, 8,

10

Manuscript

Christensen, E., Evans, K.R., Menard, C., Pintilie, M. & Bristow, R.G. (2008). Practical approaches to proteomic biomarkers within prostate cancer radiotherapy trials. Cancer metastasis reviews, 27, 375-85. nn, W.B., Bailey, N.J. & Johnson, H.E. (2005). Measuring the

urrent analytical technologies. Analyst, 130, 606-25. K., Piotto, M., Raya, J., Neuville, A., Mohr, M., Maitrot,

Fl .L., Preece, N.E., Bhakoo, K.K., Williams, S.R. & Noble, M.

Fo lation of O6-

Fo000).

G

G ., Hoehn-Berlage, M., Kloiber, O., Michaelis, T., Ernestus,

in

He

au, P., Mirimanoff, R.O., Cairncross, J.G., Janzer,

H sson, L. (1998). Interstitial

e for routine

Jen cancer cells by means of capillary gas

Jo Trygg, J., A, J., Grung, B.,

JoI., Kusano, M., Sjostrom, M., Trygg, J., Moritz, T. & Antti,

.

Kexaphosphate-mediated apoptosis in human malignant

K

methylating agent

K

Lilanda, M., Beres, A. & De Riggo, J. (2005). In vitro study

Ly

M d, U., Alfieri, A.A., Ballon, D., Traganos, F. & Koutcher, J.A.

ability in brain tumour

Momeus, F., Aparicio, A.,

human brain tumor

M

N P. & Enblad, P.

physical exercise in human serum. Journal of Proteome

Ro

Si

seizure thresholds in the

So on of

Sre

an, B., Cao, X., Byun, J., Omenn, G.S.,

Dumetabolome: c

Erb, G., Elbayed, D., Kehrli, P. & Namer, I.J. (2008). Toward improved grading of malignancy in oligodendrogliomas using metabolomics. Magn Reson Med, 59, 959-65.

orian, C(1995). Characteristic metabolic profiles revealed by 1H NMR spectroscopy for three types of human brain and nervous system tumours. NMR in Biomedicine, 8, 253-64. ote, R.S., Mitra, S. & Pal, B.C. (1980). Demethymethylguanine in a synthetic DNA polymer by an inducible activity in Escherichia coli. Biochem Biophys Res Commun, 97, 654-9. untas, K.N., Kapsalaki, E.Z., Gotsis, S.D., Kapsalakis, J.Z., Smisson, H.F., 3rd, Johnston, K.W., Robinson, J.S., Jr. & Papadakis, N. (2In vivo proton magnetic resonance spectroscopy of brain tumors. Stereotact Funct Neurosurg, 74, 83-94.

riffin, J.L. & Kauppinen, R.A. (2007). Tumour metabolomics in animal models of human cancer. J Proteome Res, 6, 498-505.

yngell, M.LR.I., Horstermann, D. & Frahm, J. (1992). Localized proton NMR spectroscopy of experimental gliomas in rat brain in vivo. NMR Biomed, 5, 335-40.

Hattingen, E., Raab, P., Franz, K., Zanella, F.E., Lanfermann, H. & Pilatus, U. (2008). Myo-inositol: a marker of reactive astrogliosisglial tumors? NMR Biomed, 21, 233-41. gi, M.E., Diserens, A.C., Gorlia, T., Hamou, M.F., de Tribolet, N., Weller, M., Kros, J.M., Hainfellner, J.A., Mason, W., Mariani, L., Bromberg, J.E., HR.C. & Stupp, R. (2005). MGMT gene silencing and benefit from temozolomide in glioblastoma. New England Journal of Medicine, 352, 997-1003.

illered, L., Valtysson, J., Enblad, P. & Perglycerol as a marker for membrane phospholipid degradation in the acutely injured human brain. J Neurol Neurosurg Psychiatry, 64, 486-91.

Hillered, L., Vespa, P.M. & Hovda, D.A. (2005). Translational neurochemical research in acute human brain injury: the current status and potential future for cerebral microdialysis. J Neurotrauma, 22, 3-41.

Hillman, J., Aneman, O., Anderson, C., Sjogren, F., Saberg, C. & Mellergard, P. (2005). A microdialysis techniqumeasurement of macromolecules in the injured human brain. Neurosurgery, 56, 1264-8; discussion 1268-70.

llum, E., Bjornson, I., Nesbakken, R., Johansson, E. & Wold, S. (1981). Classification of humachromatography and pattern recognition analysis. Journal of Chromatography, 217, 231-7.

nsson, P., Johansson, A.I., Gullberg, J.,Marklund, S., Sjostrom, M., Antti, H. & Moritz, T. (2005). High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses. Anal Chem, 77, 5635-42.

nsson, P., Johansson, E.S., Wuolikainen, A., Lindberg, J., Schuppe-Koistinen, H. (2006). Predictive Metabolite Profiling Applying Hierarchical Multivariate Curve Resolution to GCâˆ’MS DataA Potential Tool for Multi-parametric Diagnosis. Journal of Proteome Research, 5, 1407-1414

armakar, S., Banik, N.L. & Ray, S.K. (2007). Molecular mechanism of inositol hglioblastoma T98G cells. Neurochem Res, 32, 2094-102.

il, W.J., Cerna, D., Burgan, W.E., Beam, K., Carter, D., Steeg, P.S., Tofilon, P.J. & Camphausen, K. (2008). In vitro and in vivo radiosensitization induced by the DNAtemozolomide. Clin Cancer Res, 14, 931-8.

inoshita, Y., Kajiwara, H., Yokota, A. & Koga, Y. (1994). Proton magnetic resonance spectroscopy of brain tumors: an in vitro study. Neurosurgery, 35, 606-13; discussion 613-4.

Kinoshita, Y. & Yokota, A. (1997). Absolute concentrations of metabolites in human brain tumors using in vitro proton magnetic resonance spectroscopy. NMR Biomed, 10, 2-12.

kavcanova, K., Dobrota, D., Liptaj, T., Pronayova, N., Mlynarik, V., Belan, V., Gaof astrocytic tumour metabolism by proton magnetic resonance spectroscopy. Gen Physiol Biophys, 24, 327-35. ons, S.A., Chung, W.J., Weaver, A.K., Ogunrinu, T. & Sontheimer, H. (2007). Autocrine glutamate signaling promotes glioma cell invasion. Cancer Res, 67, 9463-71. ahmoo(1995). In vitro and in vivo 31P nuclear magnetic resonance measurements of metabolic changes post radiation. Cancer Res, 55, 1248-54.

Majos, C., Alonso, J., Aguilera, C., Serrallonga, M., Perez-Martin, J., Acebes, J.J., Arus, C. & Gili, J. (2003). Proton magnetic resonance spectroscopy ((1)H MRS) of human brain tumours: assessment of differences between tumour types and its appliccategorization. Eur Radiol, 13, 582-91. axwell, R.J., Martinez-Perez, I., Cerdan, S., Cabanas, M.E., Arus, C., Moreno, A., Capdevila, A., Ferrer, E., BartConesa, G., Roda, J.M., Carceller, F., Pascual, J.M., Howells, S.L., Mazucco, R. & Griffiths, J.R. (1998). Pattern recognition analysis of 1H NMR spectra from perchloric acid extracts ofbiopsies. Magn Reson Med, 39, 869-77. cKnight, T.R. (2004). Proton magnetic resonance spectroscopic evaluation of brain tumor metabolism. Semin Oncol, 31, 605-17.

ordmark, J., Rubertsson, S., Mortberg, E., Nilsson,(2009). Intracerebral monitoring in comatose patients treated with hypothermia after a cardiac arrest. Acta anaesthesiologica Scandinavica, 53, 289-98.

Pohjanen, E., Thysell, E., Jonsson, P., Eklund, C., Silfver, A., Carlsson, I.B., Lundgren, K., Moritz, T., Svensson, M.B. & Antti, H. (2007). A multivariate screening strategy for investigating metabolic effects of strenuousResearch, 6, 2113-2120. slin, M., Henriksson, R., Bergstrom, P., Ungerstedt, U. & Bergenheim, A.T. (2003). Baseline levels of glucose metabolites, glutamate and glycerol in malignant glioma assessed by stereotactic microdialysis. Journal of Neuro-Oncology, 61, 151-60.

Sibtain, N.A., Howe, F.A. & Saunders, D.E. (2007). The clinical value of proton magnetic resonance spectroscopy in adult brain tumours. Clin Radiol, 62, 109-19.

erra-Paredes, G., Senra-Vidal, A. & Sierra-Marcuno, G. (2001). Effect of extracellular long-time microperfusion of high concentrations of glutamate and glycine on picrotoxinhippocampus of freely moving rats. Brain Res, 888, 19-25. ntheimer, H. (2008). A role for glutamate in growth and invasiprimary brain tumors. J Neurochem, 105, 287-95. ekumar, A., Poisson, L.M., Rajendiran, T.M., Khan, A.P., Cao, Q., Yu, J., Laxman, B., Mehra, R., Lonigro, R.J., Li, Y., Nyati, M.K., Ahsan, A., Kalyana-Sundaram, S., HGhosh, D., Pennathur, S., Alexander, D.C., Berger, A., Shuster, J.R., Wei, J.T., Varambally, S., Beecher, C. & Chinnaiyan, A.M. (2009).

11

Manuscript

12

St lidatory Choice and Assessment of Statistical

St.A., Marosi, C., Bogdahn, U.,

y plus concomitant and adjuvant temozolomide

Sz

of inositol

5, 2427-32.

Ta

Tr rojections to latent structures

U matrix metalloprotease-2 and protein

Wi

Wi rowicz, E.J., Edlund, U.,

W ponent

Wes for collecting

Ye, Z.C. & Sontheimer, H. (1999). Glioma cells release excitotoxic

Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457, 910-4.

one, M. (1974). Cross-VaPredictions. Journal of the Royal Statistical Society. Series B (Methodological), 36, 111-147.

upp, R., Mason, W.P., van den Bent, M.J., Weller, M., Fisher, B., Taphoorn, M.J., Belanger, K., Brandes, ACurschmann, J., Janzer, R.C., Ludwin, S.K., Gorlia, T., Allgeier, A., Lacombe, D., Cairncross, J.G., Eisenhauer, E. & Mirimanoff, R.O. (2005). Radiotherapfor glioblastoma. N Engl J Med, 352, 987-96. ado, T., Vanderheyden, V., Parys, J.B., De Smedt, H., Rietdorf, K., Kotelevets, L., Chastre, E., Khan, F., Landegren, U., Soderberg, O., Bootman, M.D. & Roderick, H.L. (2008). Phosphorylation1,4,5-trisphosphate receptors by protein kinase B/Akt inhibits Ca2+ release and apoptosis. Proc Natl Acad Sci U S A, 10

Tabatabaei, P., Bergstrom, P., Henriksson, R. & Bergenheim, A.T. (2008). Glucose metabolites, glutamate and glycerol in malignant glioma tumours during radiotherapy. J Neurooncol, 90, 35-9. kano, T., Lin, J.H., Arcuino, G., Gao, Q., Yang, J. & Nedergaard, M. (2001). Glutamate release promotes growth of malignant gliomas. Nat Med, 7, 1010-5. ygg, J. & Wold, S. (2002). Orthogonal p(O-PLS). Journal Of Chemometrics, 16, 119-128.

hm, J.H., Dooley, N.P., Villemure, J.G. & Yong, V.W. (1996). Glioma invasion in vitro: regulation bykinase C. Clin Exp Metastasis, 14, 421-33. bom, C., Pettersson, F., Sjostrom, M., Henriksson, R., Johansson, M. & Bergenheim, A.T. (2006). Protein expression in experimental malignant glioma varies over time and is altered by radiotherapy treatment. Br J Cancer, 94, 1853-63. klund, S., Johansson, E., Sjostrom, L., MelleShockcor, J.P., Gottfries, J., Moritz, T. & Trygg, J. (2008). Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Analytical Chemistry, 80, 115-122. old, S., Esbensen, K. & Geladi, P. (1987). Principal comanalysis. Chemometrics and Intelligent Laboratory Systems, 2, 37-52. uolikainen, A., Hedenstrom, M., Moritz, T., Marklund, S.L., Antti, H. & Andersen, P.M. (2009). Optimization of procedurand storing of CSF for studying the metabolome in ALS. Amyotroph Lateral Scler, 1-9.

concentrations of glutamate. Cancer Res, 59, 4383-91.

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