NMR-based metabolomics approach to target biomarkers for human prostate cancer

Closing the gap between in vivo and in vitro omics: using QA/QC to strengthen ex vivo NMR metabolomics

Metabolomics aims to achieve a global quantitation of the pool of metabolites within a biological system. Importantly, metabolite concentrations serve as a sensitive marker of both genomic and phenotypic changes in response to both internal and external stimuli. NMR spectroscopy greatly aids in the understanding of both in vitro and in vivo physiological systems and in the identification of diagnostic and therapeutic biomarkers. Accordingly, NMR is widely utilized in metabolomics and fluxomics studies due to its limited requirements for sample preparation and chromatography, its non-destructive and quantitative nature, its utility in the structural elucidation of unknown compounds, and, importantly, its versatility in the analysis of in vitro, in vivo, and ex vivo samples. This review provides an overview of the strengths and limitations of in vitro and in vivo experiments for translational research and discusses how ex vivo studies may overcome these weaknesses to facilitate the extrapolation of in vitro insights to an in vivo system. The application of NMR-based metabolomics to ex vivo samples, tissues, and biofluids can provide essential information that is close to a living system (in vivo) with sensitivity and resolution comparable to those of in vitro studies. The success of this extrapolation process is critically dependent on high-quality and reproducible data. Thus, the incorporation of robust quality assurance and quality control checks into the experimental design and execution of NMR-based metabolomics experiments will ensure the successful extrapolation of ex vivo studies to benefit translational medicine.

Ex Vivo High-Resolution Magic Angle Spinning (HRMAS) 1H NMR Spectroscopy for Early Prostate Cancer Detection

Simple Summary

Prostate cancer is the second leading cancer diagnosed in men worldwide. Current diagnostic standards lack sufficient reliability in detecting and characterizing prostate cancer. Due to the cancer’s multifocality, prostate biopsies are associated with high numbers of false negatives. Whereas several studies have already shown the potential of metabolomic information for PCa detection and characterization, in this study, we focused on evaluating its predictive power for future PCa diagnosis. In our study, metabolomic information differed substantially between histobenign patients based on their risk for receiving a future PCa diagnosis, making metabolomic information highly valuable for the individualization of active surveillance strategies.

Abstract

The aim of our study was to assess ex vivo HRMAS (high-resolution magic angle spinning) ¹H NMR spectroscopy as a diagnostic tool for early PCa detection by testing whether metabolomic alterations in prostate biopsy samples can predict future PCa diagnosis. In a primary prospective study (04/2006–10/2018), fresh biopsy samples of 351 prostate biopsy patients were NMR spectroscopically analyzed (Bruker 14.1 Tesla, Billerica, MA, USA) and histopathologically evaluated. Three groups of 16 patients were compared: group 1 and 2 represented patients whose NMR scanned biopsy was histobenign, but patients in group 1 were diagnosed with cancer before the end of the study period, whereas patients in group 2 remained histobenign. Group 3 included cancer patients. Single-metabolite concentrations and metabolomic profiles were not only able to separate histobenign and malignant prostate tissue but also to differentiate between samples of histobenign patients who received a PCa diagnosis in the following years and those who remained histobenign. Our results support the hypothesis that metabolomic alterations significantly precede histologically visible changes, making metabolomic information highly beneficial for early PCa detection. Thanks to its predictive power, metabolomic information can be very valuable for the individualization of PCa active surveillance strategies.

Salivary metabolomics for cancer detection

Introduction: Saliva is an ideal biofluid that can be collected in a noninvasive manner, enabling safe and frequent screening of various diseases. Recent studies have revealed that salivary metabolomics analysis has the potential to detect both oral and systemic cancers. Area covered: We reviewed the technical aspects, as well as applications, of salivary metabolomics for cancer detection. The topics include the effects of preconditioning and the method of sample collection, sample storage, processing, measurement, data analysis, and validation of the results. We also examined the rational relationship between salivary biomarkers and tumors distant from the oral cavity. A strategy to establish standard operating protocols for obtaining reproducible quantification data is also discussed Expert opinion: Salivary metabolomics reflects oral and systematic health status, which potently enables cancer detection. The sensitivity and specificity of each marker and their combinations have been well evaluated, but a validation study is required. Further, the standard operating protocol for each procedure should be established to obtain reproducible data before clinical usage.

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.

Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy

Technological advances in mass spectrometry (MS), liquid chromatography (LC) separations, nuclear magnetic resonance (NMR) spectroscopy, and big data analytics have made possible studying metabolism at an "omics" or systems level. Here, we applied a multiplatform (NMR + LC-MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence. Thus far, predicting which patients will recur even after radical prostatectomy has not been possible. Correlation analysis on metabolite abundances detected on serum samples collected prior to surgery from prostate cancer patients ( n = 40 remission vs n = 40 recurrence) showed significant alterations in a number of pathways, including amino acid metabolism, purine and pyrimidine synthesis, tricarboxylic acid cycle, tryptophan catabolism, glucose, and lactate. Lipidomics experiments indicated higher lipid abundances on recurrent patients for a number of classes that included triglycerides, lysophosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, diglycerides, acyl carnitines, and ceramides. Machine learning approaches led to the selection of a 20-metabolite panel from a single preoperative blood sample that enabled prediction of recurrence with 92.6% accuracy, 94.4% sensitivity, and 91.9% specificity under cross-validation conditions.

Innovative Diagnostic Methods for Early Prostate Cancer Detection through Urine Analysis: A Review

Prostate cancer is the second most common cause of cancer death among men. It is an asymptomatic and slow growing tumour, which starts occurring in young men, but can be detected only around the age of 40–50. Although its long latency period and potential curability make prostate cancer a perfect candidate for screening programs, the current procedure lacks in specificity. Researchers are rising to the challenge of developing innovative tools able of detecting the disease during its early stage that is the most curable. In recent years, the interest in characterisation of biological fluids aimed at the identification of tumour-specific compounds has increased significantly, since cell neoplastic transformation causes metabolic alterations leading to volatile organic compounds release. In the scientific literature, different approaches have been proposed. Many studies focus on the identification of a cancer-characteristic “odour fingerprint” emanated from biological samples through the application of sensorial or senso-instrumental analyses, others suggest a chemical characterisation of biological fluids with the aim of identifying prostate cancer (PCa)-specific biomarkers. This paper focuses on the review of literary studies in the field of prostate cancer diagnosis, in order to provide an overview of innovative methods based on the analysis of urine, thereby comparing them with the traditional diagnostic procedures.

Nuclear Magnetic Resonance technique in tumor metabolism

Cancer is one of the most serious diseases that cause an enormous number of deaths all over the world. Tumor metabolism has great discrimination from that of normal tissues. Exploring the tumor metabolism may be one of the best ways to find biomarkers for cancer detection, diagnosis and to provide novel insights into internal physiological state where subtle changes may happen in metabolite concentrations. Nuclear Magnetic Resonance (NMR) technique nowadays is a popular tool to analyze cell extracts, tissues and biological fluids, etc, since it is a relatively fast and an accurate technique to supply abundant biochemical information at molecular levels for tumor research. In this review, approaches in tumor metabolism are discussed, including sample collection, data profiling and multivariate data analysis methods etc. Some typical applications of NMR are also summarized in tumor metabolism.

Metabolomics in cell culture--a strategy to study crucial metabolic pathways in cancer development and the response to treatment

Metabolomics is a comprehensive tool for monitoring processes within biological systems. Thus, metabolomics may be widely applied to the determination of diagnostic biomarkers for certain diseases or treatment outcomes. There is significant potential for metabolomics to be implemented in cancer research because cancer may modify metabolic pathways in the whole organism. However, not all biological questions can be answered solely by the examination of small molecule composition in biofluids; in particular, the study of cellular processes or preclinical drug testing requires ex vivo models. The major objective of this review was to summarise the current achievement in the field of metabolomics in cancer cell culture-focusing on the metabolic pathways regulated in different cancer cell lines-and progress that has been made in the area of drug screening and development by the implementation of metabolomics in cell lines.

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.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Studies of single-walled carbon nanotubes-induced hepatotoxicity by NMR-based metabonomics of rat blood plasma and liver extracts

The toxicological effects of single-walled carbon nanotubes (SWCNTs) were investigated after intratracheal instillation in male Wistar rats over a 15-day period using metabonomic analysis of 1H (nuclear magnetic resonance) NMR spectra of blood plasma and liver tissue extracts. Concurrent liver histopathology examinations and plasma clinical chemistry analyses were also performed. Significant changes were observed in clinical chemistry features, including alkaline phosphatase, total protein, and total cholesterol, and in liver pathology, suggesting that SWCNTs clearly have hepatotoxicity in the rat. 1H NMR spectra and pattern recognition analyses from nanomaterial-treated rats showed remarkable differences in the excretion of lactate, trimethylamine oxide, bilineurin, phosphocholine, amylaceum, and glycogen. Indications of amino acid metabolism impairment were supported by increased lactate concentrations and decreased alanine concentrations in plasma. The rise in plasma and liver tissue extract concentrations of choline and phosphocholine, together with decreased lipids and lipoproteins, after SWCNTs treatment indicated a disruption of membrane fluidity caused by lipid peroxidation. Energy, amino acid, and fat metabolism appeared to be affected by SWCNTs exposure. Clinical chemistry and metabonomic approaches clearly indicated liver injury, which might have been associated with an indirect mechanism involving nanomaterial-induced oxidative stress.

Molecular preservation by extraction and fixation, mPREF: a method for small molecule biomarker analysis and histology on exactly the same tissue

Background

Histopathology is the standard method for cancer diagnosis and grading to assess aggressiveness in clinical biopsies. Molecular biomarkers have also been described that are associated with cancer aggressiveness, however, the portion of tissue analyzed is often processed in a manner that is destructive to the tissue. We present here a new method for performing analysis of small molecule biomarkers and histology in exactly the same biopsy tissue.

Methods

Prostate needle biopsies were taken from surgical prostatectomy specimens and first fixed, each in a separate vial, in 2.5 ml of 80% methanol:water. The biopsies were fixed for 24 hrs at room temperature and then removed and post-processed using a non-formalin-based fixative (UMFIX), embedded, and analyzed by hematoxylin and eosin (H&E) and by immunohistochemical (IHC) staining. The retained alcohol pre-fixative was analyzed for small molecule biomarkers by mass spectrometry.

Results

H&E analysis was successful following the pre-fixation in 80% methanol. The presence or absence of tumor could be readily determined for all 96 biopsies analyzed. A subset of biopsy sections was analyzed by IHC, and cancerous and non-cancerous regions could be readily visualized by PIN4 staining. To demonstrate the suitability for analysis of small molecule biomarkers, 28 of the alcohol extracts were analyzed using a mass spectrometry-based metabolomics platform. All extracts tested yielded successful metabolite profiles. 260 named biochemical compounds were detected in the alcohol extracts. A comparison of the relative levels of compounds in cancer containing vs. non-cancer containing biopsies showed differences for 83 of the compounds. A comparison of the results with prior published reports showed good agreement between the current method and prior reported biomarker discovery methods that involve tissue destructive methods.

Conclusions

The Molecular Preservation by Extraction and Fixation (mPREF) method allows for the analysis of small molecule biomarkers from exactly the same tissue that is processed for histopathology.

Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.

From cancer metabolism to new biomarkers and drug targets

Great interest is presently given to the analysis of metabolic changes that take place specifically in cancer cells. In this review we summarize the alterations in glycolysis, glutamine utilization, fatty acid synthesis and mitochondrial function that have been reported to occur in cancer cells and in human tumors. We then propose considering cancer as a system-level disease and argue how two hallmarks of cancer, enhanced cell proliferation and evasion from apoptosis, may be evaluated as system-level properties, and how this perspective is going to modify drug discovery. Given the relevance of the analysis of metabolism both for studies on the molecular basis of cancer cell phenotype and for clinical applications, the more relevant technologies for this purpose, from metabolome and metabolic flux analysis in cells by Nuclear Magnetic Resonance and Mass Spectrometry technologies to positron emission tomography on patients, are analyzed. The perspectives offered by specific changes in metabolism for a new drug discovery strategy for cancer are discussed and a survey of the industrial activity already going on in the field is reported.

Metabolomics in urogenital cancer

Although in recent decades the development of many drugs against cancer has been witnessed, the morbidity and mortality for the most prevalent urogenital cancer have not been significantly reduced. A key task in cancer medicine is to detect the disease as early as possible. In order to achieve this, many new technologies have been developed for cancer biomarker discovery. Monitoring fluctuations of certain metabolite levels in body fluids, such as urine, has become an important way to detect early stages in carcinogenesis. Moreover metabolomic approaches are likely to be used to screen for potential diagnostic and prognostic biomarkers of urogenital cancer. In future work, these potential biomarkers should be further validated with a large enough patient cohort to achieve earlier diagnosis not only of urogenital cancer, but also other malignancies. Moreover, the improvement of patient prognosis will be another aim of such investigations. This novel metabolomic approach has the potential to provide more information about the pathophysiological status of an organism and distinguish precancerous and cancerous stages.

Biomarker discovery in biological specimens (plasma, hair, liver and kidney) of diabetic mice based upon metabolite profiling using ultra-performance liquid chromatography with electrospray ionization time-of-flight mass spectrometry

BACKGROUND

The number of diabetic patients has recently been increasing worldwide. Diabetes is a multifactorial disorder based on environmental factors and genetic background. In many cases, diabetes is asymptomatic for a long period and the patient is not aware of the disease. Therefore, the potential biomarker(s), leading to the early detection and/or prevention of diabetes mellitus, are strongly required. However, the diagnosis of the prediabetic state in humans is a very difficult issue, because the lifestyle is variable in each person. Although the development of a diagnosis method in humans is the goal of our research, the extraction and structural identification of biomarker candidates in several biological specimens (i.e., plasma, hair, liver and kidney) of ddY strain mice, which undergo naturally occurring diabetes along with aging, were carried out based upon a metabolite profiling study.

METHODS

The low-molecular-mass compounds including metabolites in the biological specimens of diabetic mice (ddY-H) and normal mice (ddY-L) were globally separated by ultra-performance liquid chromatography (UPLC) using different reversed-phase columns (i.e., T3-C18 and HS-F5) and detected by electrospray ionization time-of-flight mass spectrometry (ESI-TOF-MS). The biomarker candidates related to diabetes mellitus were extracted from a multivariate statistical analysis, such as an orthogonal partial least-squares-discriminant analysis (OPLS-DA), followed by a database search, such as ChemSpider, KEGG and HMDB.

RESULTS

Many metabolites and unknown compounds in each biological specimen were detected as the biomarker candidates related to diabetic mellitus. Among them, the elucidation of the chemical structures of several possible metabolites, including more than two biological specimens, was carried out along with the comparison of the tandem MS/MS analyses using authentic compounds. One metabolite was clearly identified as N-acetyl-L-leucine based upon the MS/MS spectra and the retention time on the chromatograms.

CONCLUSIONS

N-acetyl-L-leucine is an endogenous compound included in all biological specimens (plasma, hair, liver and kidney). Therefore, this metabolite appears to be a potential biomarker candidate related to diabetes. Although the structures of other biomarker candidates have still not yet determined, the present approach based upon a metabolite profiling study using UPLC-ESI-TOF-MS could be helpful for understanding the abnormal state of various diseases.

Practical analytical approach for the identification of biomarker candidates in prediabetic state based upon metabonomic study by ultraperformance liquid chromatography coupled to electrospray ionization time-of-flight mass spectrometry

The number of diabetic patients has recently been increasing worldwide. Thus, the discovery of potential diabetic biomarker(s), leading to the early detection and/or prevention of diabetes mellitus, is strongly required. The diagnosis of the prediabetic state in humans is a very difficult issue because of the lifestyle differences in each person and ethical consideration. Upon the basis of these considerations, animal experiments using ddY strain mice (ddY-H), which undergo naturally occurring diabetes along with age, were carried out in this study. Biomarker discovery based upon a metabonome study is now quite common, the same as that in the proteome analysis. Reversed-phase liquid chromatography-mass spectrometry (LC-MS) has mainly been used for the extensive analysis of low-molecular mass compounds including metabolites. The metabolites in the plasma of diabetic mice (ddY-H) and normal mice (ddY-L) were exhaustively separated and detected by ultraperformance liquid chromatography along with electrospray ionization time-of-flight mass spectrometry (UPLC-ESI-TOF-MS) using T3-C18 and HS-F5 columns. The biomarker candidates related to diabetes mellitus were extracted from the metabolite profiling of ddY-H and ddY-L at 5, 9 13, and 20 weeks old using a multivariate statistical analysis such as orthogonal partial least-squares-discriminant analysis (OPLS-DA). Various metabolites and unknown compounds were detected as biomarker candidates related to diabetic mellitus. Furthermore, the concentration of several metabolites on Lysine biosynthesis and Lysine degradation pathways were remarkably changed between the 9-week old ddY-H and ddY-L mice. Because a couple of biomarker candidates related to the prediabetic state were identified using the present approach, the metabolite profiling study could be helpful for understanding the abnormal state of various diseases.

Metabolomic analysis in severe childhood pneumonia in the Gambia, West Africa: findings from a pilot study

BACKGROUND

Pneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting.

METHODOLOGY/PRINCIPAL FINDINGS

Eleven children with World Health Organization (WHO)-defined severe pneumonia of non-homogeneous aetiology were selected in The Gambia, West Africa, along with community controls. Metabolomic analysis of matched plasma and urine samples was undertaken using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS). Biomarker extraction was done using SIMCA-P+ and Random Forests (RF). 'Unsupervised' (blinded) data were analyzed by Principal Component Analysis (PCA), while 'supervised' (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5'-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.

CONCLUSIONS/SIGNIFICANCE

Metabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia.

NMR-based metabonomic analysis of the hepatotoxicity induced by combined exposure to PCBs and TCDD in rats

A metabonomic approach using (1)H NMR spectroscopy was adopted to investigate the metabonomic pattern of rat urine after oral administration of environmental endocrine disruptors (EDs) polychlorinated biphenyls (PCBs) and 2,3,7,8-tetrachlorodibenzo- p-dioxin (TCDD) alone or in combination and to explore the possible hepatotoxic mechanisms of combined exposure to PCBs and TCDD. (1)H NMR spectra of urines collected 24h before and after exposure were analyzed via pattern recognition by using principal component analysis (PCA). Serum biochemistry and liver histopathology indicated significant hepatotoxicity in the rats of the combined group. The PCA scores plots of urinary (1)H NMR data showed that all the treatment groups could be easily distinguished from the control group, so could the PCBs or TCDD group and the combined group. The loadings plots of the PCA revealed remarkable increases in the levels of lactate, glucose, taurine, creatine, and 2-hydroxy-isovaleric acid and reductions in the levels of 2-oxoglutarate, citrate, succinate, hippurate, and trimethylamine-N-oxide in rat urine after exposure. These changes were more striking in the combined group. The changed metabolites may be considered possible biomarker for the hepatotoxicity. The present study demonstrates that combined exposure to PCBs and TCDD induced significant hepatotoxicity in rats, and mitochondrial dysfunction and fatty acid metabolism perturbations might contribute to the hepatotoxicity. There was good conformity between changes in the urine metabonomic pattern and those in serum biochemistry and liver histopathology. These results showed that the NMR-based metabonomic approach may provide a promising technique for the evaluation of the combined toxicity of EDs.

Markers for detection of prostate cancer

Early detection of prostate cancer is problematic, not just because of uncertainly whether a diagnosis will benefit an individual patient, but also as a result of the imprecise and invasive nature of establishing a diagnosis by biopsy. Despite its low sensitivity and specificity for identifying patients harbouring prostate cancer, serum prostate specific antigen (PSA) has become established as the most reliable and widely-used diagnostic marker for this condition. In its wake, many other markers have been described and evaluated. This review focuses on the supporting evidence for the most prominent of these for detection and also for predicting outcome in prostate cancer.

Statistical significance analysis of nuclear magnetic resonance-based metabonomics data

Use of nuclear magnetic resonance (NMR)-based metabonomics to search for human disease biomarkers is becoming increasingly common. For many researchers, the ultimate goal is translation from biomarker discovery to clinical application. Studies typically involve investigators from diverse educational and training backgrounds, including physicians, academic researchers, and clinical staff. In evaluating potential biomarkers, clinicians routinely use statistical significance testing language, whereas academicians typically use multivariate statistical analysis techniques that do not perform statistical significance evaluation. In this article, we outline an approach to integrate statistical significance testing with conventional principal components analysis data representation. A decision tree algorithm is introduced to select and apply appropriate statistical tests to loadings plot data, which are then heat map color-coded according to P score, enabling direct visual assessment of statistical significance. A multiple comparisons correction must be applied to determine P scores from which reliable inferences can be made. Knowledge of means and standard deviations of statistically significant buckets enabled computation of effect sizes and study sizes for a given statistical power. Methods were demonstrated using data from a previous study. Integrated metabonomics data assessment methodology should facilitate translation of NMR-based metabonomics discovery of human disease biomarkers to clinical use.

Technologies for localization and diagnosis of prostate cancer

The gold standard for detecting prostate cancer (PCa), systematic biopsy, lacks sensitivity as well as grading accuracy. PSA screening leads to over-treatment of many men, and it is unclear whether screening reduces PCa mortality. This review provides an understanding of the difficulties of localizing and diagnosing PCa. It summarizes recent developments of ultrasound (including elastography) and MRI, and discusses some alternative experimental techniques, such as resonance sensor technology and vibrational spectroscopy. A comparison between the different methods is presented. It is concluded that new ultrasound techniques are promising for targeted biopsy procedures, in order to detect more clinically significant cancers while reducing the number of cores. MRI advances are very promising, but MRI remains expensive and MR-guided biopsy is complex. Resonance sensor technology and vibrational spectroscopy have shown promising results in vitro. There is a need for large prospective multicentre trials that unambiguously prove the clinical benefits of these new techniques.

Comprehensive analytical strategy for biomarker identification based on liquid chromatography coupled to mass spectrometry and new candidate confirmation tools

A comprehensive analytical LC-MS(/MS) platform for low weight biomarkers molecule in biological fluids is described. Two complementary retention mechanisms were used in HPLC by optimizing the chromatographic conditions for a reversed-phase column and a hydrophilic interaction chromatography column. LC separation was coupled to mass spectrometry by using an electrospray ionization operating in positive polarity mode. This strategy enables us to correctly retain and separate hydrophobic as well as polar analytes. For that purpose artificial model study samples were generated with a mixture of 38 well characterized compounds likely to be present in biofluids. The set of compounds was used as a standard aqueous mixture or was spiked into urine at different concentration levels to investigate the capability of the LC-MS(/MS) platform to detect variations across biological samples. Unsupervised data analysis by principal component analysis was performed and followed by principal component variable grouping to find correlated variables. This tool allows us to distinguish three main groups whose variables belong to (a) background ions (found in all type of samples), (b) ions distinguishing urine samples from aqueous standard and blank samples, (c) ions related to the spiked compounds. Interpretation of these groups allows us to identify and eliminate isotopes, adducts, fragments, etc. and to generate a reduced list of m/z candidates. This list is then submitted to the prototype MZSearcher software tool which simultaneously searches several lists of potential metabolites extracted from metabolomics databases (e.g., KEGG, HMDB, etc) to propose biomarker candidates. Structural confirmation of these candidates was done off-line by fraction collection followed by nanoelectrospray infusion to provide high quality MS/MS data for spectral database queries.

NMR metabolomics and drug discovery

NMR is an integral component of the drug discovery process with applications in lead discovery, validation, and optimization. NMR is routinely used for fragment-based ligand affinity screens, high-resolution protein structure determination, and rapid protein-ligand co-structure modeling. Because of this inherent versatility, NMR is currently making significant contributions in the burgeoning area of metabolomics, where NMR is successfully being used to identify biomarkers for various diseases, to analyze drug toxicity and to determine a drug's in vivo efficacy and selectivity. This review describes advances in NMR-based metabolomics and discusses some recent applications.

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.

Metabonomics: a useful tool for the future surgeon

BACKGROUND

In the past decade or so, a range of technologies have emerged that have shown promise in increasing our understanding of disease processes and progression. These advances are referred to as the "omics" technologies; genomics, transcriptomics, and proteomics. More recently, another "omics" approach has come to the fore: metabonomics, and this technology has the potential for significant clinical impact. Metabonomics refers to the analysis of the metabolome, that is, the metabolic profile of a system. The advantage of studying the metabolome is that the end points of biological events are elucidated.

RESULTS

Although still in its infancy, the metabonomics approach has shown immense promise in areas as diverse as toxicology studies to the discovery of biomarkers of disease. It has also been applied to studies of both renal and hepatic transplants. Metabolome analysis may be conducted on a variety of biological fluids and tissue types and may utilize a number of different technology platforms, mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy being the most popular. In this review, we cover the background to the evolution of metabonomics and its applications with particular emphasis on clinical applications.

CONCLUSIONS

We conclude with the suggestion that metabonomics offers a platform for further biomarker development, drug development, and in the field of medicine.

[Recent development of metabonomics and its applications in clinical research]

In the post-genomic era, systems biology is central to the biological sciences. Functional genomics such as transcriptomics and proteomics can simultaneous determine massive gene or protein expression changes following drug treatment or other intervention. However, these changes can't be coupled directly to changes in biological function. As a result, metabonomics and its many pseudonyms (metabolomics, metabolic profiling, etc.) have exploded onto the scientific scene in the past several years. Metabonomics is a rapidly growing research area and a system approach for comprehensive and quantitative analysis of the global metabolites in a biological matrix. Analytical chemistry approach is necessary for the development of comprehensive metabonomics investigations. Fundamentally, there are two types of metabonomics approaches: mass-spectrometry (MS) based and nuclear magnetic resonance (NMR) methodologies. Metabonomics measurements provide a wealth of data information and interpretation of these data relies mainly on chemometrics approaches to perform large-scale data analysis and data visualization, such as principal and independent component analysis, multidimensional scaling, a variety of clustering techniques, and discriminant function analysis, among many others. In this review, the recent development of analytical and statistical techniques used in metabonomics is summarized. Major applications of metabonomics relevant to clinical and preclinical study are then reviewed. The applications of metabonomics in study of liver diseases, cancers and other diseases have proved useful both as an experimental tool for pathogenesis mechanism re-search and ultimately a tool for diagnosis and monitoring treatment response of these diseases. Next, the applications of metabonomics in preclinical toxicology are discussed and the role that metabonomics might do in pharmaceutical research and development is explained with special reference to the aims and achievements of the Consortium for Metabonomic Toxicology (COMET), and the concept of pharmacometabonomics as a way of predicting an individual's response to treatment is highlighted. Finally, the role of metabonomics in elucidating the function of the unknown or novel enzyme is mentioned.

Beyond clinical phenotype: the biologic integratome

Current classification of medical diagnosis derives from observational correlation between clinical syndromes and pathologic analysis. Limited understanding of the molecular determinants of diseases encountered in the critically ill remains a major obstacle to the rationale selection of therapeutic targets. Indeed, many human diseases reflect a disorder in physiologic processes that are known to involve the interaction of many complex control loops and to respond to a variety of pharmacologic agents and environmental factors. The advent of whole-genome sequencing and other high-throughput technologies have changed biomedical research into a data-rich discipline. "Omics" data sets that describe virtually all biomolecules in the cell are now publicly available. One of the challenges faced by investigators now lies in the interpretation of vast amounts of biological data sets to derive fundamental and applied biological information about whole systems. As mechanistic understanding of disease requires more than an agglomeration of information on the expression and activities of disease-associated molecules, network analysis has been applied to biological problems. Network analysis of the biological integratome promises to identify factors that influence disease phenotype, providing unique insight into disease mechanism. Network analysis also provides a mechanistic basis for defining phenotypic differences through consideration of unique genetic and environmental factors that govern intermediate phenotypes contributing to disease expression. Lastly, network analysis offers a unique method for identifying therapeutic targets that can alter disease expression.

How close is the bench to the bedside? Metabolic profiling in cancer research

Metabolic profiling using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) is integral to the rapidly expanding field of metabolomics, which is making progress in toxicology, plant science and various diseases, including cancer. In the area of oncology and metabolic phenotyping, researchers have probed the known changes in malignant cellular pathways using new experimental techniques to gain more insights, and others are exploiting these same cellular pathways for therapeutic drug targets and for novel cancer biomarkers, with the ultimate goal of translation to the clinic. Here, we discuss the challenges and opportunities in metabolic phenotyping for discovering novel cancer biomarkers, and we assess the clinical applicability of MS and NMR.

Isoflavones and the prevention of breast and prostate cancer: new perspectives opened by nutrigenomics

Epidemiological evidence together with preclinical data from animal and in vitro studies strongly support a correlation between soy isoflavone consumption and protection towards breast and prostate cancers. The biological processes modulated by isoflavones, and especially by genistein, have been extensively studied, yet without leading to a clear understanding of the cellular and molecular mechanisms of action involved. This review discusses the existing gaps in our knowledge and evaluates the potential of the new nutrigenomic approaches to improve the study of the molecular effects of isoflavones. Several issues need to be taken into account for the proper interpretation of the results already published for isoflavones. Too often knowledge on isoflavone bioavailability is not taken into account; supra-physiological doses are frequently used. Characterization of the individual variability as defined by the gut microflora composition and gene polymorphisms may also help to explain the discrepancies observed so far in the clinical studies. Finally, the complex inter-relations existing between tissues and cell types as well as cross-talks between metabolic and signalling pathways have been insufficiently considered. By appraising critically the abundant literature with these considerations in mind, the mechanisms of action that are the more likely to play a role in the preventive effects of isoflavones towards breast and prostate cancers are reviewed. Furthermore, the new perspectives opened by the use of genetic, transcriptomic, proteomic and metabolomic approaches are highlighted.

Factor analysis reveals differences in brain metabolism in macaques with SIV/AIDS and those with SIV-induced encephalitis

MRS has often been used to study metabolic processes in the HIV-infected brain. However, it remains unclear how changes in individual metabolites are related to one another in this context of virus-induced central nervous system dysfunction. We used factor analysis (FA) to identify patterns of metabolite distributions from an MRS study of healthy macaques and those infected with simian immunodeficiency virus (SIV) which were moribund with AIDS. FA summarized the correlations from nine metabolites into three main factors. Factor 3 identified patterns that discern healthy animals from those with SIV/AIDS. Factor 2 was able to differentiate between animals that had encephalitis and those moribund with AIDS but lacking encephalitis. Specifically, Factor 2 was able to distinguish animals with moderate to severe encephalitis from animals with mild or no encephalitis as well as uninfected controls. FA not only confirmed the involvement of neuronal metabolites (N-acetylaspartate and glutamate) in disease severity, but also detected changes in creatine and myo-inositol that have not been observed in the SIV macaque model previously. These results suggest that the divergent pathways of N-acetylaspartate and creatine in this disease may enable the commonly reported ratio N-acetylaspartate/creatine to be a more sensitive marker of disease severity.

Metabolomics in biomarker discovery: future uses for cancer prevention

Metabolomics is the systematic study of small-molecular-weight substances in cells, tissues and/or whole organisms as influenced by multiple factors including genetics, diet, lifestyle and pharmaceutical interventions. These substances may directly or indirectly interact with molecular targets and thereby influence the risk and complications associated with various diseases, including cancer. Since the interaction between metabolites and specific targets is dynamic, knowledge regarding genetics, susceptibility factors, timing, and degree of exposure to an agent (drug or food component) is fundamental to understanding the metabolome and its potential use for predicting and preventing early phenotypic changes. The future of metabolomics rests with its ability to monitor subtle changes in the metabolome that occur prior to the detection of a gross phenotypic change reflecting disease. The integrated analysis of metabolomics and other 'omics' may provide more sensitive ways to detect changes related to disease and discover novel biomarkers. Knowledge regarding these multivariant characteristics is critical for establishing validated and predictive metabolomic models for cancer prevention. Understanding the metabolome will not only provide insights into the critical sites of regulation in health promotion, but will also assist in identifying intermediate or surrogate cancer biomarkers for establishing preemptive/preventative or therapeutic approaches for health. While unraveling the metabolome will not be simple, the societal implications are enormous.