Benign and atypical meningioma metabolic signatures by high-resolution magic-angle spinning molecular profiling

Application of 5T glutamate chemical exchange saturation transfer imaging in brain tumors: preliminary results

PURPOSE

Glutamate chemical exchange saturation transfer (GluCEST) is a non-invasive CEST imaging technique for detecting glutamate levels in tissues. We aimed to investigate the reproducibility of the 5T GluCEST technique in healthy volunteers and preliminarily explore its potential clinical application in patients with brain tumors.

METHODS

Ten volunteers (4 males, mean age 29 years) underwent three 5T GluCEST imaging scans. The reproducibility of the three imaging GluCEST measurements was assessed using one-way repeated measures analysis of variance (ANOVA), generalized estimating equations, and linear mixed models. Twenty-eight patients with brain tumors (10 males, mean age 54 years) underwent a single GluCEST scan preoperatively, and t-tests were used to compare the differences in GluCEST values between different brain tumors. In addition, the diagnostic accuracy of GluCEST values in differentiating brain tumors was assessed using the receiver work characteristics (ROC) curve.

RESULTS

The coefficients of variation of GluCEST values in healthy volunteers were less than 5% for intra-day, inter-day, and within-subjects and less than 10% for between-subjects. High-grade gliomas (HGG) had higher GluCEST values compared to low-grade gliomas (LGG) (P < 0.001). In addition, cerebellopontine angle (CPA) meningiomas had higher GluCEST values than acoustic neuromas (P < 0.001). The area under the curve (AUC) of the GluCEST value for differentiating CPA meningioma from acoustic neuroma was 0.93.

CONCLUSION

5T GluCEST images are highly reproducible in healthy brains. In addition, the 5T GluCEST technique has potential clinical applications in differentiating LGG from HGG and CPA meningiomas from acoustic neuromas.

Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning

Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.

Bone Invasive Meningioma: Recent Advances and Therapeutic Perspectives

Meningioma is the most common primary neoplasm of the central nervous system (CNS). Generally, these tumors are benign and have a good prognosis. However, treatment can be challenging in cases with aggressive variants and poor prognoses. Among various prognostic factors that have been clinically investigated, bone invasion remains controversial owing to a limited number of assessments. Recent study reported that bone invasion was not associated with WHO grades, progression, or recurrence. Whereas, patients with longer-recurrence tended to have a higher incidence of bone invasion. Furthermore, bone invasion may be a primary preoperative predictor of the extent of surgical resection. Increasing such evidence highlights the potential of translational studies to understand bone invasion as a prognostic factor of meningiomas. Therefore, this mini-review summarizes recent advances in pathophysiology and diagnostic modalities and discusses future research directions and therapeutic strategies for meningiomas with bone invasion.

Glutamine anaplerosis is required for amino acid biosynthesis in human meningiomas

BACKGROUND

We postulate that meningiomas undergo distinct metabolic reprogramming in tumorigenesis and unravelling their metabolic phenotypes provide new therapeutic insights. Glutamine catabolism is key to the growth and proliferation of tumors. Here, we investigated the metabolomics of freshly resected meningiomas and glutamine metabolism in patient-derived meningioma cells.

METHODS

1H NMR spectroscopy of tumor tissues from 33 meningioma patients was used to differentiate the metabolite profiles of grade-I and grade-II meningiomas. Glutamine metabolism was examined using 13C/ 15N glutamine tracer, in five patient-derived meningioma cells.

RESULTS

Alanine, lactate, glutamate, glutamine, and glycine were predominantly elevated only in grade-II meningiomas by 74%, 76%, 35%, 75% and 33% respectively, with alanine, and glutamine being statistically significant (p ≤ 0.02). 13C/ 15N glutamine tracer experiments revealed that both grade-I and -II meningiomas actively metabolize glutamine to generate various key carbon intermediates including alanine and proline that are necessary for the tumor growth. Also, it is shown that glutaminase (GLS1) inhibitor, CB-839 is highly effective in downregulating glutamine metabolism and decreasing proliferation in meningioma cells.

CONCLUSION

Alanine and glutamine/glutamate are mainly elevated in grade-II meningiomas. Grade-I meningiomas possess relatively higher glutamine metabolism providing carbon/nitrogen for the biosynthesis of key nonessential amino acids. GLS1 inhibitor (CB-839) would be very effective in downregulating glutamine metabolic pathways in grade-I meningiomas leading to decreased cellular proliferation.

Magnetic resonance spectroscopy for the study of cns malignancies

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

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Metabolomic Profile of Aggressive Meningiomas by Using High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance

Meningiomas are in most cases benign brain tumors. The WHO 2016 classification defines three grades of meningiomas. This classification had a prognosis value because grade III meningiomas have a worse prognosis value compared to grades I and II meningiomas. However, some benign or atypical meningiomas can have a clinical aggressive behavior. There are currently no reliable markers which allow distinguishing between the meningiomas with a good prognosis and those which may recur. High-resolution magic angle spinning (HRMAS) spectrometry is a noninvasive method able to determine the metabolite profile of a tissue sample. We retrospectively analyzed 62 meningioma samples by using HRMAS spectrometry (43 metabolites). We described a metabolic profile defined by a high concentration for acetate, threonine, N-acetyl-lysine, hydroxybutyrate, myoinositol, ascorbate, scylloinositol, and total choline and a low concentration for aspartate, glucose, isoleucine, valine, adenosine, arginine, and alanine. This metabolomic signature was associated with poor prognosis histological markers [Ki-67 ≥ 40%, high histological grade and negative progesterone receptor (PR) expression]. We also described a similar metabolomic spectrum between grade III and grade I meningiomas. Moreover, all grade I meningiomas with a low Ki-67 expression and a positive PR expression did not have the same metabolomic profile. Metabolomic analysis could be used to determine an aggressive meningioma in order to discuss a personalized treatment. Further studies are needed to confirm these results and to correlate this metabolic profile with survival data.

Metabolomic prostate cancer fields in HRMAS MRS-profiled histologically benign tissue vary with cancer status and distance from cancer

In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication.

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

An early investigative serum Raman spectroscopy study of meningioma

Meningiomas represent one of the most frequently reported non-glial, primary brain and central nervous system (CNS) tumors. Meningiomas often display a spectrum of anomalous locations and morphological attributes, deterring their timely diagnosis. Majority of them are sporadic in nature and thus the present-day screening strategies, including radiological investigations, often result in misdiagnosis due to their aberrant and equivocal radiological facets. Therefore, it is pertinent to explore less invasive and patient-friendly biofluids such as serum for their screening and diagnostics. The utility of serum Raman spectroscopy in diagnosis and therapeutic monitoring of cancers has been reported in the literature. In the present study, for the first time, to the best of our knowledge, we have explored Raman spectroscopy to classify the sera of meningioma and control subjects. For this exploration, 35 samples each of meningioma and control subjects were accrued and the spectra revealed variance in the levels of DNA, proteins, lipids, amino acids and β-carotene, i.e., a relatively higher protein, DNA and lipid content in meningioma. Subsequent Principal Component Analysis (PCA) and Principal Component-Linear Discriminant Analysis (PC-LDA) followed by Leave-One-Out Cross-Validation (LOOCV) and limited independent test data, in a patient-wise approach, yielded a classification efficiency of 92% and 80% for healthy and meningioma, respectively. Additionally, in the analogous analysis between healthy and different grades of meningioma, similar results were obtained. These results indicate the potential of Raman spectroscopy in differentiating meningioma. As present methods suffer from known limitations, with the prospective validation on a larger cohort, serum Raman spectroscopy could be an adjuvant/alternative approach in the clinical management of meningioma.

Applications of high-resolution magic angle spinning MRS in biomedical studies II-Human diseases

High-resolution magic angle spinning (HRMAS) MRS is a powerful method for gaining insight into the physiological and pathological processes of cellular metabolism. Given its ability to obtain high-resolution spectra of non-liquid biological samples, while preserving tissue architecture for subsequent histopathological analysis, the technique has become invaluable for biochemical and biomedical studies. Using HRMAS MRS, alterations in measured metabolites, metabolic ratios, and metabolomic profiles present the possibility to improve identification and prognostication of various diseases and decipher the metabolomic impact of drug therapies. In this review, we evaluate HRMAS MRS results on human tissue specimens from malignancies and non-localized diseases reported in the literature since the inception of the technique in 1996. We present the diverse applications of the technique in understanding pathological processes of different anatomical origins, correlations with in vivo imaging, effectiveness of therapies, and progress in the HRMAS methodology.

Assessing Heterogeneity of Osteolytic Lesions in Multiple Myeloma by ¹H HR-MAS NMR Metabolomics

Multiple myeloma (MM) is a malignancy of plasma cells characterized by multifocal osteolytic bone lesions. Macroscopic and genetic heterogeneity has been documented within MM lesions. Understanding the bases of such heterogeneity may unveil relevant features of MM pathobiology. To this aim, we deployed unbiased ¹H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) metabolomics to analyze multiple biopsy specimens of osteolytic lesions from one case of pathological fracture caused by MM. Multivariate analyses on normalized metabolite peak integrals allowed clusterization of samples in accordance with a posteriori histological findings. We investigated the relationship between morphological and NMR features by merging morphological data and metabolite profiling into a single correlation matrix. Data-merging addressed tissue heterogeneity, and greatly facilitated the mapping of lesions and nearby healthy tissues. Our proof-of-principle study reveals integrated metabolomics and histomorphology as a promising approach for the targeted study of osteolytic lesions.

Determination of urinary 5-hydroxyindoleacetic acid as a metabolomics in gastric cancer

PURPOSE

The aim of this paper is to study urinary5-hydroxyindoleacetic acid (5-HIAA) in gastric cancer patients with a biochemical method and compare this metabolite with normal control and individuals with chronic gastritis.

MATERIALS AND METHODS

The subjects were 48 histologically proven gastric adenocarcinoma patients. They were 10 women and 38 men with mean age of 63.73 years. For determination of urinary excretion of 5-HIAA, a biochemical method was applied. According to kit protocol, the patients' fresh urine was added to the reagent material, and the color of the sediment that was the result of interaction between 5-HIAA and the mercury salt was compared with the standard colorimetric plate of the kit. The same method was also performed for a group of 47 patients with chronic gastritis and also a group of 50 normal individuals (age and sex matched).

RESULTS

Urinary 5-HIAA was significantly higher in gastric cancer patients compared to individuals with chronic gastritis and normal controls (P value <0.001), but no association was detected in urinary 5-HIAA based on age, sex, or site of tumor and tumor grade in gastric cancer patients group. Also, no significant difference was noted in 5-HIAA excretion between chronic gastritis and normal control groups.

CONCLUSION

Urinary excretion of 5-HIAA is significantly higher in the gastric cancer patients in comparison with that of chronic gastritis patients or normal individuals. So, this test could be regarded as a tumor marker in conjunction with other modalities in diagnosis of gastric cancer.

Biomarkers in bladder cancer: A metabolomic approach using in vitro and ex vivo model systems

Metabolomics has recently proved to be useful in the area of biomarker discovery for cancers in which early diagnostic and prognostic biomarkers are urgently needed, as is the case of bladder cancer (BC). This article presents a comprehensive review of the literature on the metabolomic studies on BC, highlighting metabolic pathways perturbed in this disease and the altered metabolites as potential biomarkers for BC detection. Current disease model systems used in the study of BC metabolome include in vitro-cultured cancer cells, ex vivo neoplastic bladder tissues and biological fluids, mainly urine but also blood serum/plasma, from BC patients. The major advantages and drawbacks of each model system are discussed. Based on available data, it seems that BC metabolic signature is mainly characterized by alterations in metabolites related to energy metabolic pathways, particularly glycolysis, amino acid and fatty acid metabolism, known to be crucial for cell proliferation, as well as glutathione metabolism, known to be determinant in maintaining cellular redox balance. In addition, purine and pyrimidine metabolism as well as carnitine species were found to be altered in BC. Finally, it is emphasized that, despite the progress made in respect to novel biomarkers for BC diagnosis, there are still some challenges and limitations that should be addressed in future metabolomic studies to ensure their translatability to clinical practice.

The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.

Immunocytochemical analysis of glucose transporter protein-1 (GLUT-1) in typical, brain invasive, atypical and anaplastic meningioma

Glucose transporter-1 (GLUT-1) is one of the major isoforms of the family of glucose transporter proteins that facilitates the import of glucose in human cells to fuel anaerobic metabolism. The present study was meant to determine the extent of the anaerobic/hypoxic state of the intratumoral microenvironment by staining for GLUT-1 in intracranial non-embolized typical (WHO grade I; n = 40), brain invasive and atypical (each WHO grade II; n = 38) and anaplastic meningiomas (WHO grade III, n = 6). In addition, GLUT-1 staining levels were compared with the various histological criteria used for diagnosing WHO grade II and III meningiomas, namely, brain invasion, increased mitotic activity and atypical cytoarchitectural change, defined by the presence of at least three out of hypercellularity, sheet-like growth, prominent nucleoli, small cell change and "spontaneous" necrosis. The level of tumor hypoxia was assessed by converting the extent and intensity of the stainings by multiplication in an immunoreactive score (IRS) and statistically evaluated. The results were as follows. (1) While GLUT-1 expression was found to be mainly weak in WHO grade I meningiomas (IRS = 1-4) and to be consistently strong in WHO grade III meningiomas (IRS = 6-12), in WHO grade II meningiomas GLUT-1 expression was variable (IRS = 1-9). (2) Histologically typical, but brain invasive meningiomas (WHO grade II) showed no or similarly low levels of GLUT-1 expression as observed in WHO grade I meningiomas (IRS = 0-4). (3) GLUT-1 expression was observed in the form of a patchy, multifocal staining reaction in 76% of stained WHO grade I-III meningiomas, while diffuse staining (in 11%) and combined multifocal and areas of diffuse staining (in 13%) were only detected in WHO grades II and III meningiomas, except for uniform staining in angiomatous WHO grade I meningioma. (4) "Spontaneous" necrosis and small cell change typically occurred away from the intratumoral capillary network embedded within the pattern of GLUT-1 staining. Taken together, GLUT-1 staining cannot be applied as a substitute for histologic grading in order to predict tumor behavior. However, assessment of tumor hypoxia in association with "spontaneous" necrosis and foci of small cell change may substantially contribute to the neuropathologic diagnosis of WHO grades II and III meningioma.

Metabolomic studies of human gastric cancer: Review

Metabolomics is a field of study in systems biology that involves the identification and quantification of metabolites present in a biological system. Analyzing metabolic differences between unperturbed and perturbed networks, such as cancerous and non-cancerous samples, can provide insight into underlying disease pathology, disease prognosis and diagnosis. Despite the large number of review articles concerning metabolomics and its application in cancer research, biomarker and drug discovery, these reviews do not focus on a specific type of cancer. Metabolomics may provide biomarkers useful for identification of early stage gastric cancer, potentially addressing an important clinical need. Here, we present a short review on metabolomics as a tool for biomarker discovery in human gastric cancer, with a primary focus on its use as a predictor of anticancer drug chemosensitivity, diagnosis, prognosis, and metastasis.

Metabolic phenotyping and systems biology approaches to understanding neurological disorders

The development of high-throughput metabolic profiling and the study of the metabolome are particularly important in brain research where small molecules or metabolites play fundamental signalling roles: neurotransmitters, signalling lipids, osmolytes and even ions. Metabolic profiling has shown that metabolic perturbations in the brain go beyond alterations of neurotransmission and that variations in brain metabolic homeostasis are associated with neurological disorders. In this report, we will focus on recent developments in the field of metabolic phenotyping that have contributed to unravelling the pathophysiology of neurological diseases. Also, we will highlight the necessity of implementing systems biology approaches to integrate metabolic data and tackle the structural and functional complexity of the brain in normal and pathological conditions.

Minimization of spectral pattern changes during HRMAS experiments at 37 degrees celsius by prior focused microwave irradiation

OBJECT

High-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy provides detailed metabolomic information from intact tissue. However, long acquisition times and high rotation speed may lead to timedependent spectral pattern changes, which may affect proper interpretation of results. We report a strategy to minimize those changes, even at physiological recording temperature.

MATERIALS AND METHODS

Glioblastoma(Gbm) tumours were induced in 12 mice by stereotactic injection of GL261 cells. Animals were sacrificed and tumours were removed and stored in liquid N2. Half of the samples were exposed to focused microwave (FMW) irradiation prior to HRMAS while the other half was not. Time-course experiments (374 min at 37°C, 9.4T, 3,000 Hz spinning rate) were carried out to monitor spectral pattern changes. Differences were assessed with Unianova test while post-HRMAS histopathology analysis was performed to assess tissue integrity.

RESULTS

Significant changes (up to 1.7 fold) were observed in samples without FMW irradiation in several spectral regions e.g. mobile lipids/lactate (0.90-1.30 ppm), acetate (1.90 ppm), N-acetyl aspartate (2.00 ppm), and Choline-containing compounds (3.19-3.25 ppm). No significant changes in the spectral pattern of FMW-irradiated samples were recorded.

CONCLUSION

We describe here a successful strategy to minimize spectral pattern changes in mouse Gbm samples using a FMW irradiation system.

MRS study of meningeal hemangiopericytoma and edema: a comparison with meningothelial meningioma

Intracranial hemangiopericytomas (HPCs) are rare tumors and their radiological appearance resembles that of meningiomas, especially meningothelial meningiomas. To increase the knowledge on the biochemical composition of this type of tumor for better diagnosis and prognosis, we performed a molecular study using ex vivo high resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) perfomed on HPC and peritumoral edematous tissues. Moreover, to help in the discrimination between HPC and meningothelial meningioma we compared the ex vivo HR-MAS spectra of samples from one patient with HPC and 5 patients affected by meningothelial meningioma. Magnetic resonance imaging (MRI), in vivo localized single voxel 1H-MRS was also performed on the same patients prior to surgery and the in vivo and ex vivo MRS spectra were compared. We observed the presence of OH-butyrate, together with glucose in HPC and a low amount of N-acetylaspartate in the edema, that may reflect neuronal alteration responsible for associated epilepsy. Many differences between HPC and meningothelial meningioma were identified. The relative ratios of myo-inositol, glucose and gluthatione with respect to glutamate are higher in HPC compared to meningioma; whereas the relative ratios of creatine, glutamine, alanine, glycine and choline-containing compounds with respect to glutamate are lower in HPC compared to meningioma. These data will be useful to improve the interpretation of in vivo MRS spectra resulting in a more accurate diagnosis of these rare tumors.

Assessment of compatibility between extraction methods for NMR- and LC/MS-based metabolomics

Because of the wide range of chemically and structurally diverse metabolites, efforts to survey the complete metabolome rely on the implementation of multiplatform approaches based on nuclear magnetic resonance (NMR) and mass spectrometry (MS). Sample preparation disparities between NMR and MS, however, may limit the analysis of the same samples by both platforms. Specifically, deuterated solvents used in NMR strategies can complicate LC/MS analysis as a result of potential mass shifts, whereas acidic solutions typically used in LC/MS methods to enhance ionization of metabolites can severely affect reproducibility of NMR measurements. These intrinsically different sample preparation requirements result in the application of different procedures for metabolite extraction, which involve additional sample and unwanted variability. To address this issue, we investigated 12 extraction protocols in liver tissue involving different aqueous/organic solvents and temperatures that may satisfy the requirements for both NMR and LC/MS simultaneously. We found that deuterium exchange did not affect LC/MS results, enabling the measurement of metabolites by NMR and, subsequently, the direct analysis of the same samples by using LC/MS with no need for solvent exchange. Moreover, our results show that the choice of solvents rather than the temperature determined the extraction efficiencies of metabolites, a combination of methanol/chloroform/water and methanol/water being the extraction methods that best complement NMR and LC/MS analysis for metabolomic studies.

Malignancies of the spinal cord

The management of intramedullary spinal cord tumors (IMSCT) is primarily concerned with the preservation of existing neurologic function. To this end, clinical scientists are continually seeking tools and techniques to improve the safety and efficacy of tumor resection and control. Further advances in safety and efficacy can be proposed at each phase of management, from pre-operative screening to post-treatment monitoring. Innovations within the areas of molecular biology and genetics, intraoperative imaging and stereotactic radiosurgery offer exciting new options to explore in the management of IMSCT. This section will review the pathophysiology and epidemiology of IMSCT and the state-of-the-art management before delving into the promising new tools and techniques for each phase of management.

(1)H MR Spectroscopy of Meningiomas at 3.0T: the Role of Glutamate-Glutamine Complex and Glutathione

Proton magnetic resonance spectroscopy ((1)H MRS) has been used extensively for the characterization of the intracranial meningiomas. A major emphasis is placed on identification of an alanine (Ala) content within these tumors. Less attention is given to other metabolites such as glutamine and glutamate (Glx). Our objective was to assess the incidence and the relevance of the Glx content in meningiomas, to evaluate their usefulness versus Ala in the diagnosis of intracranial meningiomas and to indicate a potential role of other biochemical compounds such as glutathione (GSH). We performed a retrospective review of the (1)H MRS spectra at 3.0T of 16 intracranial meningiomas in 16 consecutive patients with newly diagnosed tumors. All meningiomas were evaluated with single- voxel (1)H MRS at short echo time using an automatic quantitation of the metabolites by linear combination model (LCModel) fitting. Detailed analysis of the spectra showed that the Glx content was a more common result (100%) than the Ala content (44%). The Glx content can be considered in high concentrations within these tumors resulting in overall levels comparable to normal brain values (P > 0.2). A glutathione (GSH) spectrum was added to the LCModel basis set in six meningiomas and in all of them a GSH peak was detected at 2.95 ppm (100%). Other metabolites such as guanidinoacetate (Gua) were detected in six meningiomas (38%) and this was not reported previously. Our data indicate that Glx and GSH are far more likely to be biochemical predictors than Ala in the (1)H MRS evaluation of intracranial meningiomas. The significance of Gua as another potential marker of the meningioma cell metabolism needs to be further investigated.

Magic angle spinning NMR-based metabolic profiling of head and neck squamous cell carcinoma tissues

High-resolution magic-angle spinning (HR-MAS) proton NMR spectroscopy is used to explore the metabolic signatures of head and neck squamous cell carcinoma (HNSCC) which included matched normal adjacent tissue (NAT) and tumor originating from tongue, lip, larynx and oral cavity, and associated lymph-node metastatic (LN-Met) tissues. A total of 43 tissues (18 NAT, 18 Tumor and 7 LN-Met) from 22 HNSCC patients were analyzed. Principal Component Analysis of NMR data showed a clear classification between NAT and tumor tissues, however, LN-Met tissues were classified among tumor. A partial least-squares discriminant analysis model generated from NMR metabolic profiles was used to differentiate normal from tumor samples (Q(2) > 0.80, Receiver Operator Characteristic area under the curve >0.86, using 7-fold cross validation). HNSCC and LN-Met tissues showed elevated levels of lactate, amino acids including leucine, isoleucine, valine, alanine, glutamine, glutamate, aspartate, glycine, phenylalanine and tyrosine, choline containing compounds, creatine, taurine, glutathione, and decreased levels of triglycerides. These elevated metabolites were associated with highly active glycolysis, increased amino acids influx (anaplerosis) into the TCA cycle, altered energy metabolism, membrane choline phospholipid metabolism, and oxidative and osmotic defense mechanisms. Moreover, decreased levels of triglycerides may indicate lipolysis followed by β-oxidation of fatty acids that may exist to deliver bioenergy for rapid tumor cell proliferation and growth.

Analysis of metabolic and gene expression changes after hydrodynamic DNA injection into mouse liver

The hydrodynamic injection in mice tail vein of a plasmid (40 µg DNA) bearing the human α1-antitrypsin gene mediates: a) good liver gene transfer resulting in therapeutic plasma levels of human protein (1 mg/ml, approximately) from days 1-10 after injection; b) low liver injury as demonstrated by a poor and transient increase of aspartate aminotransferase (AST) and alanine transaminase (ALT) in mouse plasma; 3) limited expression and metabolic changes in host liver genes and metabolites as evaluated on days 2 and 10 after injection. Groups of three mice were uninjected (control) or hydrodynamically injected with saline or plasmid DNA and then sacrificed on days 2 and 10 after injection. The results of principal component analysis (PCA) show, both in expression microarray and metabolomic analysis, that changes between control and hydrodynamically injected groups are not dramatic and tend to normalize after 10 d. The differences are even smaller between DNA and saline hydrodynamically injected mice. Hydrodynamic injection induces a complex but limited gene expression and metabolic change which includes variations in molecules related to energy metabolism and stress response. The results contribute to support that hydrodynamic method is a safe procedure of liver gene transfer but the long-term effect of hydrodynamic gene transfer procedure, remains to be studied.

Prediction of gastric cancer metastasis through urinary metabolomic investigation using GC/MS

AIM: To gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic values to predict tumor metastasis.

METHODS: Human gastric cancer SGC-7901 cells were implanted into 24 severe combined immune deficiency (SCID) mice, which were randomly divided into metastasis group (n = 8), non-metastasis group (n = 8), and normal group (n = 8). Urinary metabolomic information was obtained by gas chromatography/mass spectrometry (GC/MS).

RESULTS: There were significant metabolic differences among the three groups (t test, P < 0.05). Ten selected metabolites were different between normal and cancer groups (non-metastasis and metastasis groups), and seven metabolites were also different between non-metastasis and metastasis groups. Two diagnostic models for gastric cancer and metastasis were constructed respectively by the principal component analysis (PCA). These PCA models were confirmed by corresponding receiver operating characteristic analysis (area under the curve = 1.00).

CONCLUSION: The urinary metabolomic profile is different, and the selected metabolites might be instructive to clinical diagnosis or screening metastasis for gastric cancer.

pH optimization for a reliable quantification of brain tumor cell and tissue extracts with (1)H NMR: focus on choline-containing compounds and taurine

The aim of this study was to define the optimal pH for (1)H nuclear magnetic resonance (NMR) spectroscopy analysis of perchloric acid or methanol-chloroform-water extracts from brain tumor cells and tissues. The systematic study of the proton chemical shift variations as a function of pH of 13 brain metabolites in model solutions demonstrated that recording (1)H NMR spectra at pH 10 allowed resolving resonances that are overlapped at pH 7, especially in the 3.2-3.3 ppm choline-containing-compounds region. (1)H NMR analysis of extracts at pH 7 or 10 showed that quantitative measurements of lactate, alanine, glutamate, glutamine (Gln), creatine + phosphocreatine and myo-inositol (m-Ino) can be readily performed at both pHs. The concentrations of glycerophosphocholine, phosphocholine and choline that are crucial metabolites for tumor brain malignancy grading were accurately measured at pH 10 only. Indeed, the resonances of their trimethylammonium moieties are cleared of any overlapping signal, especially those of taurine (Tau) and phosphoethanolamine. The four non-ionizable Tau protons resonating as a singlet in a non-congested spectral region permits an easier and more accurate quantitation of this apoptosis marker at pH 10 than at pH 7 where the triplet at 3.43 ppm can be overlapped with the signals of glucose or have an intensity too low to be measured. Glycine concentration was determined indirectly at both pHs after subtracting the contribution of the overlapped signals of m-Ino at pH 7 or Gln at pH 10.

NMR methods for unravelling the spectra of complex mixtures

The main methods for the simplification of the NMR of complex mixtures by selective attenuation/suppression of the signals of certain components are presented. The application of relaxation, diffusion and PSR filters and other techniques to biological samples, pharmaceuticals, foods, living organisms and natural products are illustrated with examples.

Metabolic aggressiveness in benign meningiomas with chromosomal instabilities

Meningiomas are often considered benign tumors curable by surgery, but most recurrent meningiomas correspond to histologic benign tumors. Because alterations in chromosome 14 among others have suggested clinical aggressiveness and recurrence, determining both the molecular phenotype and the genetic profile may help distinguish tumors with aggressive metabolism. The aim of this study was to achieve higher specificity in the detection of meningioma subgroups by measuring chromosomal instabilities by fluorescence in situ hybridization and cytogenetics and metabolic phenotypes by high-resolution magic angle spinning spectroscopy. We studied 46 meningioma biopsies with these methodologies. Of these, 34 were of WHO grade 1 and 12 were of WHO grade 2. Genetic analysis showed a subgroup of histologic benign meningioma with chromosomal instabilities. The metabolic phenotype of this subgroup indicated an aggressive metabolism resembling that observed for atypical meningioma. According to the metabolic profiles, these tumors had increased energy demand, higher hypoxic conditions, increased membrane turnover and cell proliferation, and possibly increased resistance to apoptosis. Taken together, our results identify distinct metabolic phenotypes for otherwise benign meningiomas based on cytogenetic studies and global metabolic profiles of intact tumors. Measuring the metabolic phenotype of meningioma intact biopsies at the same time as histopathologic analysis may allow the early detection of clinically aggressive tumors.

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.

Characterizing Human Cancer Metabolomics with ex vivo 1H HRMAS MRS

Publications of proton high resolution magic angle spinning (1 H HRMAS) magnetic resonance spectroscopy (MRS) and its role in identification of metabolic markers for human cancer reported between 2005 and 2009 are reviewed according the anatomic sites of cancer: lung, breast, prostate, brain, colorectal, and cervical. Limited and insufficient screening options for the general public have indicated a need for more advanced tests that can identify and locate cancer at an early stage. 1 H HRMAS MRS is a valuable tool that can elucidate relevant biological metabolite information that is being used to distinguish cancer from benign tissue, and even classify types of tumors. Researchers are working to translate this ex vivo spectroscopy information into an in vivo system that could be implemented in cancer clinics. For instance, in the case of lung cancer, the goal is to identify the at risk population through a simple blood test, which would be the first level of screening. From these tests, patients identified as at risk will be able to undergo further non-invasive radiological testing for diagnostic purposes. Not only will this ex vivo technology become a valuable diagnostic tool, it will also provide a way to monitor treatments on an individual basis so they can be adjusted accordingly for the best possible outcome.

Metabolic profiling of human lung cancer tissue by 1H high resolution magic angle spinning (HRMAS) NMR spectroscopy

This work aims at characterizing the metabolic profile of human lung cancer, to gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic value in the future. Paired samples of tumor and noninvolved adjacent tissues from 12 lung tumors have been directly analyzed by (1)H HRMAS NMR (500/600 MHz) enabling, for the first time to our knowledge, the identification of over 50 compounds. The effect of temperature on tissue stability during acquisition time has also been investigated, demonstrating that analysis should be performed within less than two hours at low temperature (277 K), to minimize glycerophosphocholine (GPC) and phosphocholine (PC) conversion to choline and reduce variations in some amino acids. The application of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to the standard 1D (1)H spectra resulted in good separation between tumor and control samples, showing that inherently different metabolic signatures characterize the two tissue types. On the basis of spectral integration measurements, lactate, PC, and GPC were found to be elevated in tumors, while glucose, myo-inositol, inosine/adenosine, and acetate were reduced. These results show the valuable potential of HRMAS NMR-metabonomics for investigating the metabolic phenotype of lung cancer.

Chemometrics in metabolomics--a review in human disease diagnosis

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

Longitudinal profiling of articular cartilage degradation in osteoarthritis by high-resolution magic angle spinning 1H NMR spectroscopy: experimental study in the meniscectomized guinea pig model

This study assessed the 1H HRMAS NMR spectroscopic profile of articular cartilage in both physiological and osteoarthitic situations. One-dimensional and two-dimensional 1H HRMAS NMR spectra were obtained from the tibial plateau cartilage of healthy and operated (unilateral medial meniscectomy and sham surgery) guinea pigs at different stages of disease, over a 6-month period. The major osteoarthritis-induced 1H HRMAS NMR changes were an increase of the N-acetyl peak of proteoglycans (at day 20 after meniscectomy) and a decrease after day 60 as the pathology evolved. These proteoglycan changes revealed by 1H HRMAS NMR analysis were validated by proteoglycan biochemistry assays. 1H HRMAS NMR analysis also evidenced a sharp increase in methylene resonances of chondrocyte membrane lipids from day 90 as a marker of apoptosis. There was an increase of the mobile methyl group of collagen at day 120, which was associated with collagen breakdown. 1H HRMAS NMR analysis provided a multifactorial and sequential picture of cartilage degradation at the extracellular matrix and chondrocyte levels.

Metabolomics: moving to the clinic

Assessment of a biological system by means of global and non-targeted metabolite profiling--metabolomics or metabonomics--provides the investigator with molecular information that is close to the phenotype in question in the sense that metabolites are an ultimate product of gene, mRNA, and protein activity. Over the last few years, there has been a rapidly growing number of metabolomics applications aimed at finding biomarkers which could assist diagnosis, provide therapy guidance, and evaluate response to therapy for particular diseases. Also, within the fields of drug discovery, drug toxicology, and personalized pharmacology, metabolomics is emerging as a powerful tool. This review seeks to update the reader on analytical strategies, biomarker findings, and implications of metabolomics for the clinic. Particular attention is paid to recent biomarkers found related to neurological, cardiovascular, and cancer diseases. Moreover, the impact of metabolomics in the drug discovery and development process is examined.

Metabolite profiling of fecal water extracts from human colorectal cancer

Colorectal cancer is the second leading cause of cancer death in developed countries. There is a need for better preventive strategies to improve the outcome of this disease. The increasing availability of high-throughput methodologies opens up new possibilities for screening new markers. The application of NMR metabolic profiling to fecal water extracts has interesting potential as a diagnostic tool for detecting colorectal cancer. We obtained NMR metabolic profiles of fecal water extracts from patients with colorectal cancer and healthy individuals, to characterize possible differences between them and to identify potential diagnostic markers. Our results show that metabolic profiling of fecal water extracts is a cheap, reproducible and effective method for detecting colorectal cancer markers and therefore complements other stool-screening methods. A low concentration of short-chain fatty acids, such as acetate and butyrate, previously associated with the development of colorectal cancer, appears to be the most effective marker. Concentrations of proline and cysteine, which are major components of most colonic epithelium mucus glycoproteins, also display significant changes in samples from colorectal cancer. Differentiation between fecal water extracts from controls and patients with colorectal cancer by NMR spectroscopy combined with chemometric techniques opens up new possibilities for developing new, efficient, high-throughput screening protocols.