May-Britt Tessem

Magnetic Resonance Metabolomics of Intact Tissue: A Biotechnological Tool in Cancer Diagnostics and Treatment Evaluation

Personalized medicine is increasingly important in cancer treatment for its role in staging and its potential to improve stratification of patients. Different types of molecules, genes, proteins, and metabolites are being extensively explored as potential biomarkers. This review discusses the major findings and potential of tissue metabolites determined by high-resolution magic angle spinning magnetic resonance spectroscopy for cancer detection, characterization, and treatment monitoring.

HR MAS MR Spectroscopy in Metabolic Characterization of Cancer

One of the central hallmarks of cancer is the rapid and infinite cellular proliferation. In order to cope with increased requirement for building blocks and energy, cancer cells develop abnormal metabolic properties. Detailed assessment of cancer cell metabolism can provide biological information for use in both drug discovery and development of personalized cancer therapy. Analysis of intact tissue using high resolution magic angle spinning (HR MAS) magnetic resonance spectroscopy (MRS) gives qualitative and quantitative metabolite measures with minimal sample preparation. Multivariate statistical methods are important tools for analysis of complex MR data and have in recent years been used for analysis of HR MAS data from intact tissue. HR MAS analysis of intact tissue allows combination of metabolomic data with genomic or proteomic data, and can therefore be used both for exploring the molecular biology of cancer and for clinical improvements in cancer diagnostics, prognostics and treatment planning. In this review, the basic concepts of HR MAS are presented, and its use in characterisation of cancer metabolism is discussed with specific focus on selected pathways such as choline metabolism and glycolysis. The use of HR MAS in analysis of amino acids and lipid metabolism in cancer is also reviewed. Finally, the expected role of HR-MAS in metabolic characterisation in the near future is discussed.

Spermine and Citrate as Metabolic Biomarkers for Assessing Prostate Cancer Aggressiveness

Separating indolent from aggressive prostate cancer is an important clinical challenge for identifying patients eligible for active surveillance, thereby reducing the risk of overtreatment. The purpose of this study was to assess prostate cancer aggressiveness by metabolic profiling of prostatectomy tissue and to identify specific metabolites as biomarkers for aggressiveness. Prostate tissue samples (n = 158, 48 patients) with a high cancer content (mean: 61.8%) were obtained using a new harvesting method, and metabolic profiles of samples representing different Gleason scores (GS) were acquired by high resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS). Multivariate analysis (PLS, PLS-DA) and absolute quantification (LCModel) were used to examine the ability to predict cancer aggressiveness by comparing low grade (GS = 6, n = 30) and high grade (GS≥7, n = 81) cancer with normal adjacent tissue (n = 47). High grade cancer tissue was distinguished from low grade cancer tissue by decreased concentrations of spermine (p = 0.0044) and citrate (p = 7.73·10−4), and an increase in the clinically applied (total choline+creatine+polyamines)/citrate (CCP/C) ratio (p = 2.17·10−4). The metabolic profiles were significantly correlated to the GS obtained from each tissue sample (r = 0.71), and cancer tissue could be distinguished from normal tissue with sensitivity 86.9% and specificity 85.2%. Overall, our findings show that metabolic profiling can separate aggressive from indolent prostate cancer. This holds promise for the benefit of applying in vivo magnetic resonance spectroscopy (MRS) within clinical MR imaging investigations, and HR-MAS analysis of transrectal ultrasound-guided biopsies has a potential as an additional diagnostic tool.

Peripheral zone prostate cancer localization by multiparametric magnetic resonance at 3 T: unbiased cancer identification by matching to histopathology.

ObjectivesThe aim of this study was to assess the diagnostic accuracy of peripheral zone prostate cancer localization by multiparametric magnetic resonance (MR) at 3 T using segmental matching of histopathology and MR images to avoid bias by image features in selection of cancer and noncancer regions. Materials and MethodsForty-eight patients underwent multiparametric MR imaging (MRI) on a 3 T system using a phased array body coil and spine coil elements for signal detection before prostatectomy. The examination included T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), dynamic contrast-enhanced imaging (DCE-MRI), and MR spectroscopic imaging (MRSI). Histopathology slides were correlated to T2W images and a stringent matching procedure was performed to define cancer and noncancer areas of the peripheral zone without influence of the MR image appearance. Mean T2W signal intensity, apparent diffusion coefficient, area under the enhancement curve, and choline + creatine-to-citrate signal ratio were calculated for cancer and noncancer areas. Receiver operating characteristic (ROC) analysis was performed on MR-derived parameters from the selected areas. Logistic regression was used to create models based on best combination of parameters. A simplified approach assigning a parametric score to each segment based on cutoff values from ROC analysis was also explored. ResultsBy using the stringent matching procedure, 138 noncancer and 41 cancer segments were selected in the T2W images and transferred to the images of the other MR methods. A significant difference between mean values in cancer and noncancer segments was observed for all MR parameters analyzed (P < 0.001). Apparent diffusion coefficient was the best performing single parameter, with an area under the ROC curve Az,DWI of 0.90 for prostate cancer detection. Any combination of T2WI, DWI, and DCE-MRI was significantly better than T2WI alone in separating cancer from noncancer segments (Az,T2WI + DWI + DCE-MRI = 0.94, Az,T2WI + DWI = 0.92, Az,T2WI + DCE-MRI = 0.91, Az,T2WI = 0.85). The combination of T2WI and MRSI was also better than T2WI alone (Az, T2WI + MRSI = 0.90); however, the logistic regression models including MRSI did not have significant parameters. The simplified approach combining all parameters gave similar results to logistic regression combining all parameters (Az = 0.95 and 0.97, respectively). ConclusionBy selecting histopathology defined cancer and noncancer areas without influence of image contrast, this study objectively reveals that all investigated MR parameters have the ability to separate cancer from noncancer areas in the peripheral zone individually and that any combination is better than T2WI alone.

Changes in Gene Transcription Underlying the Aberrant Citrate and Choline Metabolism in Human Prostate Cancer Samples

Purpose: Low concentrations of citrate and high concentrations of choline-containing compounds (ChoCC) are metabolic characteristics observed by magnetic resonance spectroscopy of prostate cancer tissue. The objective was to investigate the gene expression changes underlying these metabolic aberrations to find regulatory genes with potential for targeted therapies. Experimental design: Fresh frozen samples (n = 133) from 41 patients undergoing radical prostatectomy were included. Histopathologic evaluation was carried out for each sample before a metabolic profile was obtained with high-resolution magic angle spinning (HR-MAS) spectroscopy. Following the HR-MAS, RNA was extracted from the same sample and quality controlled before carrying out microarray gene expression profiling. A partial least square statistical model was used to integrate the data sets to identify genes whose expression show significant covariance with citrate and ChoCC levels. Results: Samples were classified as benign, n = 35; cancer of low grade (Gleason score 6), n = 24; intermediate grade (Gleason score 7), n = 41; or high grade (Gleason score ≥8), n = 33. RNA quality was high with a mean RNA Integrity Number score of 9.1 (SD 1.2). Gene products predicting significantly a reduced citrate level were acetyl citrate lyase (ACLY, P = 0.003) and m-aconitase (ACON, P < 0.001). The two genes whose expression most closely accompanied the increase in ChoCC were those of phospholipase A2 group VII (PLA2G7, P < 0.001) and choline kinase α (CHKA, P = 0.002). Conclusions: By integrating histologic, transcriptomic, and metabolic data, our study has contributed to an expanded understanding of the mechanisms underlying aberrant citrate and ChoCC levels in prostate cancer. Clin Cancer Res; 18(12); 3261–9. ©2012 AACR.

Alignment of high resolution magic angle spinning magnetic resonance spectra using warping methods

The peaks of magnetic resonance (MR) spectra can be shifted due to variations in physiological and experimental conditions, and correcting for misaligned peaks is an important part of data processing prior to multivariate analysis. In this paper, five warping algorithms (icoshift, COW, fastpa, VPdtw and PTW) are compared for their feasibility in aligning spectral peaks in three sets of high resolution magic angle spinning (HR-MAS) MR spectra with different degrees of misalignments, and their merits are discussed. In addition, extraction of information that might be present in the shifts is examined, both for simulated data and the real MR spectra. The generic evaluation methodology employs a number of frequently used quality criteria for evaluation of the alignments, together with PLS-DA to assess the influence of alignment on the classification outcome. Peak alignment greatly improved the internal similarity of the data sets. Especially icoshift and COW seem suitable for aligning HR-MAS MR spectra, possibly because they perform alignment segment-wise. The choice of reference spectrum can influence the alignment result, and it is advisable to test several references. Information from the peak shifts was extracted, and in one case cancer samples were successfully discriminated from normal tissue based on shift information only. Based on these findings, general recommendations for alignment of HR-MAS MRS data are presented. Where possible, observations are generalized to other data types (e.g. chromatographic data).

Discrimination of Patients with Microsatellite Instability Colon Cancer using 1H HR MAS MR Spectroscopy and Chemometric Analysis

The primary aim of this study was to analyze human colon cancer and normal adjacent tissue using (1)H HR MAS MR spectroscopy and chemometric analyses, evaluating possible biomarkers for colon cancer. The secondary aim was to investigate metabolic profiles of tissue samples (n = 63, 31 patients) with microsatellite instability (MSI-H) compared to microsatellite stable (MSS) colon tissue. Our hypothesis was that this method may provide an alternative to MSI genotyping. Cancer samples were clearly separated from normal adjacent mucosa by 100% accuracy. Several metabolites such as lactate, taurine, glycine, myo-inositol, scyllo-inositol, phosphocholine (PC), glycerophosphocholine (GPC), creatine, and glucose were identified as potential biomarkers for cancer detection. Adenomas (n = 4) were also separated from cancer and normal samples mainly based on higher GPC and PC levels. Interestingly, metabolic differences in normal colon mucosa between MSI-H and MSS patients were observed. MSI status was validated with 80% accuracy with a sensitivity and specificity of 79% and 82%, respectively, including both cancer and normal samples The metabolic differences between MSI-H and MSS may be very interesting in the early detection of cancer development and of high clinical importance in the work of improving diagnosis and characterization of colon cancer.

Effect of UVA and UVB Irradiation on the Metabolic Profile of Rabbit Cornea and Lens Analysed by HR-MAS 1H NMR Spectroscopy

Purpose:The aim of the study was to investigate the metabolic profiles of intact rabbit corneas and lenses exposed to UVA and UVB radiation by using high-resolution (HR) magic angle spinning (MAS) 1H nuclear magnetic resonance (NMR) spectroscopy and pattern recognition methods. Methods:Adult albino rabbits were exposed to UVA (366 nm, 0.589 J/cm2) or UVB (312 nm, 1.667 J/cm2) radiation for 8 min, once a day for 5 days. Three days after the last irradiation day, samples of corneas and lenses were dissected. HR-MAS 1H NMR spectroscopy combined with pattern recognition methods (principal component analysis and soft independent modelling of class analogy) and one-way ANOVA were applied to obtain metabolic information from intact corneal and lens tissue. Results: UVB irradiation caused statistically significant metabolic changes in the rabbit corneas. A decrease in metabolites as ascorbate (84%), myo-inositol (59%), hypotaurine (91%) and choline (76%) was observed. Exposure to UVA radiation caused no significant metabolic alteration in this tissue. The metabolic profile of the rabbit lenses showed no detectable changes after UVA or UVB exposure. Conclusions:The combination of HR-MAS 1H NMR spectroscopy and multivariate methods proved effective to analyse intact corneal and lens tissue after exposure to UV radiation of different wavelengths. By avoiding extraction methods and obtaining complete metabolic profiles from one sample, HR-MAS 1H NMR spectroscopy provided important information about metabolic alteration occurring in rabbit corneal and lens tissue after UV exposure.

A New Method to Provide a Fresh Frozen Prostate Slice Suitable for Gene Expression Study and MR Spectroscopy

Fresh frozen tissue from radical prostatectomy specimens is highly valuable material for research on gene expression and cellular metabolites. The purpose of this study was to develop a standardized method to provide a representative high quality research sample from radical prostatectomy specimens without interfering with the routine histopathological procedure.

Metabolic markers in blood can separate prostate cancer from benign prostatic hyperplasia.

Background:An individualised risk-stratified screening for prostate cancer (PCa) would select the patients who will benefit from further investigations as well as therapy. Current detection methods suffer from low sensitivity and specificity, especially for separating PCa from benign prostatic conditions. We have investigated the use of metabolomics analyses of blood samples for separating PCa patients and controls with benign prostatic hyperplasia (BPH).Methods:Blood plasma and serum samples from 29 PCa patient and 21 controls with BPH were analysed by metabolomics analysis using magnetic resonance spectroscopy, mass spectrometry and gas chromatography. Differences in blood metabolic patterns were examined by multivariate and univariate statistics.Results:By combining results from different methodological platforms, PCa patients and controls were separated with a sensitivity and specificity of 81.5% and 75.2%, respectively.Conclusions:The combined analysis of serum and plasma samples by different metabolomics measurement techniques gave successful discrimination of PCa and controls, and provided metabolic markers and insight into the processes characteristic of PCa. Our results suggest changes in fatty acid (acylcarnitines), choline (glycerophospholipids) and amino acid metabolism (arginine) as markers for PCa compared with BPH.