Metabolic signatures of cancer unveiled by NMR spectroscopy of human biofluids

Investigation of the metabolomic crosstalk between liver sinusoidal endothelial cells and hepatocytes exposed to paracetamol using organ-on-chip technology

Organ-on-chip technology is a promising in vitro approach recapitulating human physiology for the study of responses to drug exposure. Organ-on-chip cell cultures have paved new grounds for testing and understanding metabolic dose-responses when evaluating pharmaceutical and environmental toxicity. Here, we present a metabolomic investigation of a coculture of liver sinusoidal endothelial cells (LSECs, SK-HEP-1) with hepatocytes (HepG2/C3a) using advanced organ-on-chip technology. To reproduce the physiology of the sinusoidal barrier, LSECs were separated from hepatocytes by a membrane (culture insert integrated organ-on-chip platform). The tissues were exposed to acetaminophen (APAP), an analgesic drug widely used as a xenobiotic model in liver and HepG2/C3a studies. The differences between the SK-HEP-1, HepG2/C3a monocultures and SK-HEP-1/HepG2/C3a cocultures, treated or not with APAP, were identified from metabolomic profiles using supervised multivariate analysis. The pathway enrichment coupled with metabolite analysis of the corresponding metabolic fingerprints contributed to extracting the specificity of each type of culture and condition. In addition, we analysed the responses to APAP treatment by mapping the signatures with significant modulation of the biological processes of the SK-HEP-1 APAP, HepG2/C3a APAP and SK-HEP-1/HepG2/C3a APAP conditions. Furthermore, our model shows how the presence of the LSECs barrier and APAP first pass can modify the metabolism of HepG2/C3a. Altogether, this study demonstrates the potential of a "metabolomic-on-chip" strategy for pharmaco-metabolomic applications predicting individual response to drugs.

Metabolomic study of serum in patients with invasive ductal breast carcinoma with LC-MS/MS approach

BACKGROUND

Invasive ductal carcinoma (IDC) is the most common type of breast cancer so its early detection can lead to a significant decrease in mortality rate. However, prognostic factors for IDC are not adequate and we need novel markers for the treatment of different individuals. Although positron emission tomography and magnetic resonance imaging techniques are available, they are based on morphological features that do not provide any clue for molecular events accompanying cancer progression. In recent years, "omics" approaches have been extensively developed to propose novel molecular signatures of cancers as putative biomarkers, especially in biofluids. Therefore, a mass spectrometry-based metabolomics investigation was performed to find some putative metabolite markers of IDC and potential metabolites with prognostic value related to the estrogen receptor, progesterone receptor, lymphovascular invasion, and human epidermal growth factor receptor 2.

METHODS

An untargeted metabolomics study of IDC patients was performed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). The multivariate principal component analysis by XCMS online built a model that could separate the study groups and define the significantly altered m/z parameters. The most important biological pathways were also identified by pathway enrichment analysis.

RESULTS

The results showed that the significantly altered metabolites in IDC serum samples mostly belonged to amino acids and lipids. The most important involved pathways included arginine and proline metabolism, glycerophospholipid metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis.

CONCLUSIONS

Significantly altered metabolites in IDC serum samples compared to healthy controls could lead to the development of metabolite-based potential biomarkers after confirmation with other methods and in large cohorts.

Urinary Mass Spectrometry Profiles in Age-Related Macular Degeneration

We and others have shown that patients with different severity stages of age-related macular degeneration (AMD) have distinct plasma metabolomic profiles compared to controls. Urine is a biofluid that can be obtained non-invasively and, in other fields, urine metabolomics has been proposed as a feasible alternative to plasma biomarkers. However, no studies have applied urinary mass spectrometry (MS) metabolomics to AMD. This study aimed to assess urinary metabolomic profiles of patients with different stages of AMD and a control group. We included two prospectively designed, multicenter, cross-sectional study cohorts: Boston, US (n = 185) and Coimbra, Portugal (n = 299). We collected fasting urine samples, which were used for metabolomic profiling (Ultrahigh Performance Liquid chromatography—Mass Spectrometry). Multivariable logistic and ordinal logistic regression models were used for analysis, accounting for gender, age, body mass index and use of AREDS supplementation. Results from both cohorts were then meta-analyzed. No significant differences in urine metabolites were seen when comparing patients with AMD and controls. When disease severity was considered as an outcome, six urinary metabolites differed significantly (p < 0.01). In particular, two of the metabolites identified have been previously shown by our group to also differ in the plasma of patients of AMD compared to controls and across severity stages. While there are fewer urinary metabolites associated with AMD than plasma metabolites, this study identified some differences across stages of disease that support previous work performed with plasma, thus highlighting the potential of these metabolites as future biomarkers for AMD.

Metabolomics Monitoring of Treatment Response to Brain Tumor Immunotherapy

Immunotherapy has revolutionized care for many solid tissue malignancies, and is being investigated for efficacy in the treatment of malignant brain tumors. Identifying a non-invasive monitoring technique such as metabolomics monitoring to predict patient response to immunotherapy has the potential to simplify treatment decision-making and to ensure therapy is tailored based on early patient response. Metabolomic analysis of peripheral immune response is feasible due to large metabolic shifts that immune cells undergo when activated. The utility of this approach is under investigation. In this review, we discuss the metabolic changes induced during activation of an immune response, and the role of metabolic profiling to monitor immune responses in the context of immunotherapy for malignant brain tumors. This review provides original insights into how metabolomics monitoring could have an important impact in the field of tumor immunotherapy if achievable.

Feasibility and characterization of a safe susceptibility-matched endorectal coil for MR spectroscopy

When using endorectal coils, local radiofrequency (RF) heating may occur in the surrounding tissue. Furthermore, most endorectal coils create a susceptibility artifact detrimental to both anatomical magnetic resonance imaging (MRI) and spectroscopy (MRS) acquisitions. We aimed at assessing the safety and MRS performance of a susceptibility-matched endorectal coil for further rectal wall analysis. Experiments were performed on a General Electric MR750 3 T scanner. A variable number of miniaturized passive RF traps were incorporated in the reception cable. The assessment of RF heating and coil sensitivity was conducted on a 1.5% agar-agar phantom doped with NaCl. Several susceptibility-matched materials such as Ultem, perfluorocarbon and barium sulfate were then compared with an external coil. Finally, Ultem was used as a solid support for an endorectal coil and compared with a reference coil. Phantom experiments exhibited a complete suppression of both the RF heating phenomenon and the coil sensitivity artifact. Ultem was the material that produced the smallest image distortion. The full width at half maximum of MR spectra acquired using the susceptibility-matched endorectal coil showed at least 30% narrowing compared with a reference endorectal coil. A susceptibility-matched endorectal coil with RF traps incorporated was validated on phantoms. This coil appears to be a promising device for future in vivo experiments.

The follicular fluid metabolome differs according to the endometriosis phenotype

RESEARCH QUESTION

Is there a follicular fluid-specific metabolic profile in deep infiltrating endometriosis (DIE) depending on the presence of an associated ovarian endometrioma (OMA) that could lead to the identification of biomarkers for diagnosis and prognosis of the disease?

DESIGN

In this prospective cohort study, proton nuclear magnetic resonance (¹H-NMR) experiments were carried out on 50 follicular fluid samples from patients presenting with DIE, associated or not associated with an OMA, and 29 follicular fluid samples from patients with infertility caused by a tubal obstruction.

RESULTS

Concentrations of glucose, citrate, creatine and amino acids such as tyrosine and alanine were lower in women with DIE than control participants, whereas concentrations of lactate, pyruvate, lipids and ketone bodies were higher. Metabolic analysis revealed enhanced concentrations of glycerol and ketone bodies in patients with OMA, indicative of an activation of lipolysis followed by beta-oxidation. Concentrations of lactate and pyruvate were increased in patients without OMA, whereas the concentration of glucose was decreased, highlighting activation of the anaerobic glycolysis pathway. Differences in concentrations of amino acids such as threonine and glutamine were also statistically relevant in discriminating between the presence or absence of OMA.

CONCLUSIONS

Results indicate a mitochondrial dysregulation in endometriosis phenotypes, with a modified balance between anaerobic glycolysis and beta-oxidation in OMA phenotypes that could affect the fertility of women with endometriosis. As the composition of the follicular fluid has been shown to be correlated with oocyte development and outcome of implantation after fertilization, these findings may help explain the high level of infertility in these patients.

Diagnosis of papillary thyroid carcinoma by 1H NMR spectroscopy-based metabolomic analysis of whole blood

The incidence rate of thyroid carcinoma, especially papillary thyroid carcinoma (PTC), has increased significantly over time. As a primary pathway for metastasis, the lymphatic system is an important prognostic factor for PTC patients. Although the metabolic changes in PTC patients have been investigated in extensive studies, few studies focused on the whole blood metabolic profiling of PTC patients. In this study, we investigated the ¹H NMR-based metabolic profiles of whole blood samples that were obtained from healthy individuals and PTC patients, with or without lymph node metastasis. The estimation of the predictive potential of metabolites was evaluated using multivariate statistical analyses, which revealed that the whole blood carries information that is sufficient for distinguishing between PTC patients and healthy individuals. However, PTC patients were not well classified as positive or negative according to the lymph nodes. We did not find a metabolite that could discriminate the presence of lymph node metastasis. Further studies with larger sample sizes are needed to elucidate significant metabolites to indicate the presence of lymph node metastasis in patients with PTC.

Endometriosis phenotypes are associated with specific serum metabolic profiles determined by proton-nuclear magnetic resonance

RESEARCH QUESTION

What is the correlation between serum metabolic profile and endometriosis phenotype?

DESIGN

A pilot study nestled in a prospective cohort study at a university hospital, including 46 patients with painful endometriosis who underwent surgery and 21 controls who did not have macroscopic endometriotic lesions. Endometriosis was strictly classified into two groups of 23 patients each: endometrioma (OMA) and deep infiltrating endometriosis (DIE). Serum samples were collected before surgery for metabolomic profiling based on proton-nuclear magnetic resonance spectroscopy in combination with statistical approaches. Comparative identification of the metabolites in the serum from endometriosis patients and from controls was carried out, including an analysis according to endometriosis phenotype.

RESULTS

The serum metabolic profiles of the endometriosis patients revealed significantly lower concentrations of several amino acids compared with the controls, whereas the concentrations of free fatty acids and ketone bodies were significantly higher. The OMA and the DIE phenotypes each had a specific metabolic profile, with higher concentrations of two ketone bodies in the OMA group, and higher concentrations of free fatty acids and lipids in the DIE group.

CONCLUSION

Proton-nuclear magnetic resonance-based metabolomics of serum samples were found to have ample potential for identifying metabolic changes associated with endometriosis phenotypes. This information may improve our understanding of the pathogenesis of endometriosis.

Metabolomic studies of breast cancer in murine models: A review

BACKGROUND

Metabolomic strategies have been extensively used to search for biomarkers of disease, including cancer, in biological complex mixtures such as cells, tissues and biofluids. In breast cancer research, murine models are of great value and metabolomics has been increasingly applied to characterize tumor or organ tissues, or biofluids, for instance to follow-up metabolism during cancer progression or response to specific therapies.

SCOPE OF REVIEW

This review briefly introduces the different murine models used in breast cancer research and proceeds to present the metabolomic studies reported so far to describe the deviant metabolic behavior associated to breast cancer, in each type of model: xenografts (cell- or patient-derived), spontaneous (naturally-occurring or genetically engineered) and carcinogen-induced. The type of sample and strategies followed are identified, as well as the main findings from of study.

MAJOR CONCLUSIONS

Metabolomics has gradually become relevant in characterizing murine models of breast cancer, using either Nuclear Magnetic Resonance (NMR) or Mass Spectromety (MS). Both tissue and biofluids are matrixes of interest in this context, although in some type of models, reports have focused primarily on the former. The aims of tissue studies have comprised the search for mechanistic knowledge of carcinogenesis, metastasis development and response/resistance to therapies. Biofluid metabolomics has mainly aimed at finding non-invasive biomarkers for early breast cancer detection or prognosis determination.

GENERAL SIGNIFICANCE

Metabolomics provides exquisite detail on murine tumor and systemic metabolism of breast cancer. This knowledge paves the way for the discovery of new biomarkers, potentially translatable to in vivo non-invasive patient follow-up.

Correlations between the metabolic profile and 18F-FDG-Positron Emission Tomography-Computed Tomography parameters reveal the complexity of the metabolic reprogramming within lung cancer patients

Several studies have demonstrated that the metabolite composition of plasma may indicate the presence of lung cancer. The metabolism of cancer is characterized by an enhanced glucose uptake and glycolysis which is exploited by ¹⁸F-FDG positron emission tomography (PET) in the work-up and management of cancer. This study aims to explore relationships between ¹H-NMR spectroscopy derived plasma metabolite concentrations and the uptake of labeled glucose (¹⁸F-FDG) in lung cancer tissue. PET parameters of interest are standard maximal uptake values (SUV_max), total body metabolic active tumor volumes (MATV_WTB) and total body total lesion glycolysis (TLG_WTB) values. Patients with high values of these parameters have higher plasma concentrations of N-acetylated glycoproteins which suggest an upregulation of the hexosamines biosynthesis. High MATV_WTB and TLG_WTB values are associated with higher concentrations of glucose, glycerol, N-acetylated glycoproteins, threonine, aspartate and valine and lower levels of sphingomyelins and phosphatidylcholines appearing at the surface of lipoproteins. These higher concentrations of glucose and non-carbohydrate glucose precursors such as amino acids and glycerol suggests involvement of the gluconeogenesis pathway. The lower plasma concentration of those phospholipids points to a higher need for membrane synthesis. Our results indicate that the metabolic reprogramming in cancer is more complex than the initially described Warburg effect.

Nuclear magnetic resonance-based metabolomics and metabolic pathway networks from patient-matched esophageal carcinoma, adjacent noncancerous tissues and urine

BACKGROUND

Several studies have demonstrated a correlation between esophageal cancer (EC) and perturbed urinary metabolomic profiles, but none has described the correlation between urine metabolite profiles and those of the tumor and adjacent esophageal mucosa in the same patient.

AIM

To investigate how urinary metabolic phenotypes were linked to the changes in the biochemical landscape of esophageal tumors.

METHODS

Nuclear magnetic resonance-based metabolomics were applied to esophageal tumor tissues and adjacent normal mucosal tissues alongside patient-matched urine samples.

RESULTS

Analysis revealed that specific metabolite changes overlapped across both metrics, including glucose, glutamate, citrate, glycine, creatinine and taurine, indicating that the networks for metabolic pathway perturbations in EC, potentially involved in but not limited to disruption of fatty acid metabolism, glucose and glycolytic metabolism, tricarboxylic acid cycle and glutaminolysis. Additionally, changes in most urinary biomarkers correlated with changes in biomarker candidates in EC tissues, implying enhanced energy production for rapid cell proliferation.

CONCLUSION

Overall, these associations provide evidence for distinct metabolic signatures and pathway disturbances between the tumor tissues and urine of EC patients, and changes in urinary metabolic signature could reflect reprogramming of the aforementioned metabolic pathways in EC tissues. Further investigation is needed to validate these initial findings using larger samples and to establish the underlying mechanism of EC progression.

Osteopenia-osteoporosis discrimination in postmenopausal women by 1H NMR-based metabonomics

This is a report on how ¹H NMR-based metabonomics was employed to discriminate osteopenia from osteoporosis in postmenopausal women, identifying the main metabolites associated to the separation between the groups. The Assays were performed using seventy-eight samples, being twenty-eight healthy volunteers, twenty-six osteopenia patients and twenty-four osteoporosis patients. PCA, LDA, PLS-DA and OPLS-DA formalisms were used. PCA discriminated the samples from healthy volunteers from diseased patient samples. Osteopenia-osteoporosis discrimination was only obtained using Analysis Discriminants formalisms, as LDA, PLS-DA and OPLS-DA. The metabonomics model using LDA formalism presented 88.0% accuracy, 88.5% specificity and 88.0% sensitivity. Cross-Validation, however, presented some problems as the accuracy of modeling decreased. LOOCV resulted in 78.0% accuracy. The OPLS-DA based model was better: R2Y and Q2 values equal to 0.871 (p<0.001) and 0.415 (p<0.001). LDA and OPLS-DA indicated the important spectral regions for discrimination, making possible to assign the metabolites involved in the skeletal system homeostasis, as follows: VLDL, LDL, leucine, isoleucine, allantoin, taurine and unsaturated lipids. These results indicate that ¹H NMR-based metabonomics can be used as a diagnosis tool to discriminate osteoporosis from osteopenia using a single serum sample.

Urine Nuclear Magnetic Resonance (NMR) Metabolomics in Age-Related Macular Degeneration

Biofluid biomarkers of age-related macular degeneration (AMD) are still lacking, and their identification is challenging. Metabolomics is well-suited to address this need, and urine is a valuable accessible biofluid. This study aimed to characterize the urinary metabolomic signatures of patients with different stages of AMD and a control group (>50 years). It was a prospective, cross-sectional study, where subjects from two cohorts were included: 305 from Coimbra, Portugal (AMD patients n = 252; controls n = 53) and 194 from Boston, United States (AMD patients n = 147; controls n = 47). For all participants, we obtained color fundus photographs (for AMD staging) and fasting urine samples, which were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy. Our results revealed that in both cohorts, urinary metabolomic profiles differed mostly between controls and late AMD patients, but important differences were also found between controls and subjects with early AMD. Analysis of the metabolites responsible for these separations revealed that, even though distinct features were observed for each cohort, AMD was in general associated with depletion of excreted citrate and selected amino acids at some stage of the disease, suggesting enhanced energy requirements. In conclusion, NMR metabolomics enabled the identification of urinary signals of AMD and its severity stages, which might represent potential metabolomic biomarkers of the disease.

NMR-Based Urinary Metabolomics Applications

The field of metabolomics has been growing tremendously over the recent years and, consistent with that growth, a number of investigators have been looking at the potential of NMR-based urinary metabolomics for several applications. While such applications have shown promising results, there still remains an enormous amount of work to be done before this approach becomes accepted and widely used in clinical diagnostics and other biomedical applications. To achieve such goals, optimization of parameters and standardization of protocols are of paramount importance. In view of this, in this chapter, we present some recommended methods and procedures that can help researchers in the field. Furthermore, we have highlighted some of the challenges encountered in such applications and suggested some possible ways to overcome those challenges.

NMR-based metabolomics of biofluids in cancer

This review describes the current status of NMR-based metabolomics of biofluids with respect to cancer risk assessment, detection, disease characterization, prognosis, and treatment monitoring. While the metabolism of cancer cells is altered compared with that of non-proliferating cells, the metabolome of blood and urine reflects the entire organism. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, but translation to clinical practice is currently hindered by lack of validation, difficulties in biological interpretation, and non-standardized analytical procedures.

Oblique rotation of factors: a novel pattern recognition strategy to classify fluorescence excitation-emission matrices of human blood plasma for early diagnosis of colorectal cancer

Colorectal cancer (CRC) ranks high in both men and women, accounting for about 13% of all cancers. In this study, a novel pattern recognition strategy is proposed to improve early diagnosis of CRC through visualizing the relationship between different spectral patterns in a case-control research. Partial least squares-discriminant analysis (PLS-DA) and supervised Kohonen network (SKN) were used to classify the fluorescence excitation-emission matrices (EEMs) from 289 human blood plasma samples containing CRC patients, adenomas tumor, other non-malignant findings and healthy individuals. To obtain optimal factors, oblique rotation (OR) and genetic algorithm (GA) were used to rotate the factors by optimizing transformation matrix elements. Transformed factors were introduced to SKN to build a classification model and the model performance was examined via comparison with a common classifier; PLS-DA. Classification models were built for CRC-healthy and adenomas-healthy samples and the best results were obtained through applying GA-OR on PLS factors and introducing them to the classifiers. Non-error rates for SKN and PLS-DA models assisted with GA (for selecting more informative PLS factors) and OR were equal to 0.97 and 0.95 in cross validation and 0.93 and 0.90 for prediction of the external test set, respectively. Moreover, according to the acceptable results for adenomas-healthy cases using optimal factors, CRC can be diagnosed in early stages. Combining classifiers and optimal factors proved to be efficient for distinguishing healthy and malignant samples, and OR can significantly improve performance of the classification model.

Human plasma metabolomics in age-related macular degeneration (AMD) using nuclear magnetic resonance spectroscopy

PURPOSE

To differentiate the plasma metabolomic profile of patients with age related macular degeneration (AMD) from that of controls, by Nuclear Magnetic Resonance (NMR) spectroscopy.

METHODS

Two cohorts (total of 396 subjects) representative of central Portugal and Boston, USA phenotypes were studied. For each cohort, subjects were grouped according to AMD stage (early, intermediate and late). Multivariate analysis of plasma NMR spectra was performed, followed by signal integration and univariate analysis.

RESULTS

Small changes were detected in the levels of some amino acids, organic acids, dimethyl sulfone and specific lipid moieties, thus providing some biochemical information on the disease. The possible confounding effects of gender, smoking history and age were assessed in each cohort and found to be minimal when compared to that of the disease. A similar observation was noted in relation to age-related comorbidities. Furthermore, partially distinct putative AMD metabolite fingerprints were noted for the two cohorts studied, reflecting the importance of nutritional and other lifestyle habits in determining AMD metabolic response and potential biomarker fingerprints. Notably, some of the metabolite changes detected were noted as potentially differentiating controls from patients diagnosed with early AMD.

CONCLUSION

For the first time, this study showed metabolite changes in the plasma of patients with AMD as compared to controls, using NMR. Geographical origins were seen to affect AMD patients´ metabolic profile and some metabolites were found to be valuable in potentially differentiating controls from early stage AMD patients. Metabolomics has the potential of identifying biomarkers for AMD, and further work in this area is warranted.

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia

INTRODUCTION

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results.

OBJECTIVES

In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH.

METHODS

Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using 1H nuclear magnetic resonance (1H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches.

RESULTS

The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH.

CONCLUSION

PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

Diagnostic Applications of Nuclear Magnetic Resonance-Based Urinary Metabolomics

Metabolomics is a rapidly growing field with potential applications in various disciplines. In particular, metabolomics has received special attention in the discovery of biomarkers and diagnostics. This is largely due to the fact that metabolomics provides critical information related to the downstream products of many cellular and metabolic processes which could provide a snapshot of the health/disease status of a particular tissue or organ. Many of these cellular products eventually find their way to urine; hence, analysis of urine via metabolomics has the potential to yield useful diagnostic and prognostic information. Although there are a number of analytical platforms that can be used for this purpose, this review article will focus on nuclear magnetic resonance-based metabolomics. Furthermore, although there have been many studies addressing different diseases and metabolic disorders, the focus of this review article will be in the following specific applications: urinary tract infection, kidney transplant rejection, diabetes, some types of cancer, and inborn errors of metabolism. A number of methodological considerations that need to be taken into account for the development of a clinically useful optimal test are discussed briefly.

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.

Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma

RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n = 42) and controls (n = 49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced.

Plasma metabolomic analysis of human hepatocellular carcinoma: Diagnostic and therapeutic study

Many hepatocellular carcinoma (HCC) patients suffer from late stages when diagnosed, leading to dismal prospects for cure. Improving the diagnosis and treatment of HCC remains a challenge. In this work, NMR-based metabolomic techniques were used to investigate the metabolic alterations of HCC patients from different pathological backgrounds. Metabolic improvement of clinical surgical treatments or transcatheter arterial chemoembolization (TACE) for recurrent or metastatic HCC was also evaluated. HCC was characterized by enhanced lipid metabolism and high consumption in response to liver injury. Expectedly, higher consumption of glucose and lactate production in TACE group confirmed that recurrent or metastatic HCC is more active in citric acid cycle and oxidative phosphorylation. However, TACE or surgical treatments did not immediately improve the metabolic profiles of HCC patients. Combining multivariate statistical analyses with univariate t-test, a series of characteristic metabolites were identified and served as biomarkers for discrimination of HCC patients in different pathological backgrounds. The relative metabolic pathway analyses help to get insight into the underlying biochemical mechanism and extend clinical relevance. Furthermore, algorithm of support vector classification was used to identify HCC and control subjects, and diagnostic sensitivity and specificity reached to 100% and 81.08% respectively by receiver operating characteristic analysis. It is concluded that NMR-based metabolomic analysis of plasma can provide a powerful approach to discover diagnostic and therapeutic biomarkers, and subsequently contribute to clinical disease management.

NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients.

A serum metabolomic fingerprint of bevacizumab and temsirolimus combination as first-line treatment of metastatic renal cell carcinoma

BACKGROUND

Renal cell carcinoma is one of the most chemoresistant cancers, and its metastatic form requires administration of targeted therapies based on angiogenesis or mTOR inhibitors. Understanding how these treatments impact the human metabolism is essential to predict the host response and adjust personalised therapies. We present a metabolomic investigation of serum samples from patients with metastatic RCC (mRCC) to identify metabolic signatures associated with targeted therapies.

METHODS

Pre-treatment and serial on-treatment sera were available for 121 patients participating in the French clinical trial TORAVA, in which 171 randomised patients with mRCC received a bevacizumab and temsirolimus combination (experimental arm A) or a standard treatment: either sunitinib (B) or interferon-α+bevacizumab (C). Metabolic profiles were obtained using nuclear magnetic resonance spectroscopy and compared on-treatment or between treatments.

RESULTS

Multivariate statistical modelling discriminates serum profiles before and after several weeks of treatment for arms A and C. The combination A causes faster changes in patient metabolism than treatment C, detectable after only 2 weeks of treatment. Metabolites related to the discrimination include lipids and carbohydrates, consistently with the known RCC metabolism and side effects of the drugs involved. Comparison of the metabolic profiles for the three arms shows that temsirolimus, an mTOR inhibitor, is responsible for the faster host metabolism modification observed in the experimental arm.

CONCLUSIONS

In mRCC, metabolomics shows a faster host metabolism modification induced by a mTOR inhibitor as compared with standard treatments. These results should be confirmed in larger cohorts and other cancer types.

NMR metabolomics of renal cancer: an overview

This paper reviews the use of NMR metabolomics for the metabolic characterization of renal cancer. The existing challenges in the clinical management of this disease are first presented, followed by a brief introduction to the metabolomics approach, in the context of cancer research. A subsequent review of the literature on NMR metabolic studies of renal cancer reveals that the subject has been clearly underdeveloped, compared with other types of cancer, particularly regarding cultured cells and tissue analysis. NMR analysis of biofluids has focused on blood (plasma or serum) metabolomics, comprising no account of studies on human urine, in spite of its noninvasiveness and physiological proximity to the affected organs. Finally, some areas of potential future development are identified.

Nuclear magnetic resonance metabolomic profiling of urine provides a noninvasive alternative to the identification of biomarkers associated with endometriosis

OBJECTIVE

To investigate whether urine metabolomic profile can be used to identify biomarkers associated to endometriosis.

DESIGN

Prospective study. For each subject, a urine sample was collected after overnight fasting and before surgery.

SETTING

University medical center.

PATIENT(S)

The clinical cohort included 45 endometriosis patients, diagnosed at early (n = 6) and advanced (n = 39) stages of the disease, and 36 healthy women. All women underwent diagnostic laparoscopy to visually confirm the presence or absence of endometriotic lesions.

INTERVENTION(S)

Metabolomic profiling of urine samples based on (1)H-nuclear magnetic resonance (NMR) spectroscopy in combination with statistical approaches.

MAIN OUTCOME MEASURE(S)

Comparative identification of metabolites present in urine from endometriosis patients and healthy women.

RESULT(S)

The urine metabolomic profile of endometriosis patients exhibited higher concentrations of N(1)-methyl-4-pyridone-5-carboxamide, guanidinosuccinate, creatinine, taurine, valine, and 2-hydroxyisovalerate and decreased concentrations of lysine compared with healthy women. Most of these metabolites are involved in inflammation and oxidative stress processes. These pathophysiologic events had been previously described to be present in ectopic endometrial proliferation foci.

CONCLUSION(S)

Overall, the results demonstrate the potential of (1)H-NMR-based metabolomics, a rapid and noninvasive approach, to identify metabolic changes associated to endometriosis in urine samples. This information could be useful to get a better understanding of the pathogenesis of endometriosis, thus providing support to the noninvasive diagnosis of this pathology.

Application of metabolomics in drug resistant breast cancer research

The metabolic profiles of breast cancer cells are different from normal mammary epithelial cells. Breast cancer cells that gain resistance to therapeutic interventions can reprogram their endogenous metabolism in order to adapt and proliferate despite high oxidative stress and hypoxic conditions. Drug resistance in breast cancer, regardless of subgroups, is a major clinical setback. Although recent advances in genomics and proteomics research has given us a glimpse into the heterogeneity that exists even within subgroups, the ability to precisely predict a tumor’s response to therapy remains elusive. Metabolomics as a quantitative, high through put technology offers promise towards devising new strategies to establish predictive, diagnostic and prognostic markers of breast cancer. Along with other “omics” technologies that include genomics, transcriptomics, and proteomics, metabolomics fits into the puzzle of a comprehensive systems biology approach to understand drug resistance in breast cancer. In this review, we highlight the challenges facing successful therapeutic treatment of breast cancer and the innovative approaches that metabolomics offers to better understand drug resistance in cancer.

Cancer metabolomics in basic science perspective

As metabolomics investigates metabolic pathways with the focus on metabolites, it is a suitable approach to address the complex metabolic alteration in cancer. In addition, metabolic profiles are affected by environmental and post-natal changes, and therefore, directly measuring many metabolites may provide epigenetically relevant information in cancer. Despite much development in our understanding of cancer metabolism, focus is often directed to signaling or metabolic proteins that modulate the metabolite levels. In this review, we discuss the "metabolite-oriented view" on cancer metabolism. We cover how metabolomics research contributed to our current insights into the basic mechanism of metabolic alterations leading to cancer. Then, we discuss specific metabolites and related enzymatic pathways directly related with tumorigenesis. We particularly pay attention to how metabolites regulate signaling proteins and metabolic enzymes ultimately leading to cancer phenotypes. Finally, we address future prospects and challenges of metabolomics in cancer research.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

Rapid and non-invasive diagnosis of coronary artery disease via clinical laboratory parameters and1H-NMR spectra of human blood plasma

Coronary artery disease (CAD), one of the most common fatal diseases in the world, was examinedviainvestigation of the¹H-NMR spectra of human blood plasma and clinical laboratory parameters with the aim of early disease diagnosis.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

Proteomics, and metabolomics: magnetic resonance spectroscopy for the presurgical screening of thyroid nodules

We review the progress and state-of-the-art applications of studies in Magnetic Resonance Spectroscopy (MRS) and Imaging as an aid for diagnosis of thyroid lesions of different nature, especially focusing our attention to those lesions that are cytologically undetermined. It appears that the high-resolution of High-Resolution Magic-Angle-Spinning (HRMAS) MRS improves the overall accuracy of the analysis of thyroid lesions to a point that a significant improvement in the diagnosis of cytologically undetermined lesions can be expected. This analysis, in the meantime, allows a more precise comprehension of the alterations in the metabolic pathways induced by the development of the different tumors. Although these results are promising, at the moment, a clinical application of the method to the common workup of thyroid nodules cannot be used, due to both the limitation in the availability of this technology and the wide range of techniques, that are not uniformly used. The coming future will certainly see a wider application of these methods to the clinical practice in patients affected with thyroid nodules and various other neoplastic diseases.

Metabolic profiling of biofluids: potential in lung cancer screening and diagnosis

The knowledge that the organism's metabolome is a potentially informative mirror of the impact of disease and its dynamics has led to promising developments in cancer research, strongly geared toward the discovery of new biomarkers of disease onset and progression. The present text reviews the advances made in the last 10 years in lung cancer research making use of the metabolomics strategies, particularly concerning metabolite profiling of human biofluids (blood serum and plasma, urine and others), expected to reflect the deviant metabolic behavior of lung tumors. The main goal of this article is to provide the reader with an up-to-date summary of the main metabolic variations taking place in biofluids, in relation to lung cancer, as well as of the analytical strategies employed to unveil them. Furthermore, particular needs and challenges are identified and possible developments envisaged.

¹H magnetic resonance spectroscopy: a review of the current literature and its potential utility in veterinary oncology

Advanced imaging of veterinary cancer patients has evolved in recent years and modalities once limited to human medicine have now been described for diagnostic purposes in veterinary medicine (positron emission tomography/computed tomography, single-photon emission computed tomography, whole body magnetic resonance imaging). Magnetic resonance spectroscopy (MRS) is a non-invasive and non-ionizing technique that is well described in the human medical literature and is most frequently used to evaluate the metabolic activity of tissues with questionable malignant transformation. Differentiation of neoplastic tissue from surrounding normal tissue is dependent on variations in cellular metabolism. Positive identification of malignancy can be made when neoplastic alterations are occurring at the cellular level prior to gross anatomic changes. This improved, early detection of cancer occurrence (or recurrence) can improve patient survival and direct medical therapy. MRS techniques are largely underutilized in veterinary medicine, with current research predominantly limited to the brain (both evaluation of normal and diseased tissue). Given the clinical utility of MRS in humans, the technique may also be useful in the staging of cancer in veterinary medicine.

MRS-based Metabolomics in Cancer Research

Metabolomics is a relatively new technique that is gaining importance very rapidly. MRS-based metabolomics, in particular, is becoming a useful tool in the study of body fluids, tissue biopsies and whole organisms. Advances in analytical techniques and data analysis methods have opened a new opportunity for such technology to contribute in the field of diagnostics. In the MRS approach to the diagnosis of disease, it is important that the analysis utilizes all the essential information in the spectra, is robust, and is non-subjective. Although some of the data analytic methods widely used in chemical and biological sciences are sketched, a more extensive discussion is given of a 5-stage Statistical Classification Strategy. This proposes powerful feature selection methods, based on, for example, genetic algorithms and novel projection techniques. The applications of MRS-based metabolomics in breast cancer, prostate cancer, colorectal cancer, pancreatic cancer, hepatobiliary cancers, gastric cancer, and brain cancer have been reviewed. While the majority of these applications relate to body fluids and tissue biopsies, some in vivo applications have also been included. It should be emphasized that the number of subjects studied must be sufficiently large to ensure a robust diagnostic classification. Before MRS-based metabolomics can become a widely used clinical tool, however, certain challenges need to be overcome. These include manufacturing user-friendly commercial instruments with all the essential features, and educating physicians and medical technologists in the acquisition, analysis, and interpretation of metabolomics data.

Applications of metabolomics in cancer research

The first discovery of metabolic changes in cancer occurred almost a century ago. While the genetic underpinnings of cancer have dominated its study since then, altered metabolism has recently been acknowledged as a key hallmark of cancer and metabolism-focused research has received renewed attention. The emerging field of metabolomics – which attempts to profile all metabolites within a cell or biological system – is now being used to analyze cancer metabolism on a system-wide scale, painting a broad picture of the altered pathways and their interactions with each other. While a large fraction of cancer metabolomics research is focused on finding diagnostic biomarkers, metabolomics is also being used to obtain more fundamental mechanistic insight into cancer and carcinogenesis. Applications of metabolomics are also emerging in areas such as tumor staging and assessment of treatment efficacy. This review summarizes contributions that metabolomics has made in cancer research and presents the current challenges and potential future directions within the field.

NMR Metabolomics Analysis of Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disease, which is characterized by progressive death of dopaminergic neurons in the substantia nigra pars compacta. Although mitochondrial dysfunction and oxidative stress are linked to PD pathogenesis, its etiology and pathology remain to be elucidated. Metabolomics investigates metabolite changes in biofluids, cell lysates, tissues and tumors in order to correlate these metabolomic changes to a disease state. Thus, the application of metabolomics to investigate PD provides a systematic approach to understand the pathology of PD, to identify disease biomarkers, and to complement genomics, transcriptomics and proteomics studies. This review will examine current research into PD mechanisms with a focus on mitochondrial dysfunction and oxidative stress. Neurotoxin-based PD animal models and the rationale for metabolomics studies in PD will also be discussed. The review will also explore the potential of NMR metabolomics to address important issues related to PD treatment and diagnosis.