MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status

Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline

African American (AA) women in the United States have a 40% higher breast cancer mortality rate than Non-Hispanic White (NHW) women. The survival disparity is particularly striking among (estrogen receptor positive) ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic pathways typically activated in patients with ER+ breast cancer. We collected pretreatment plasma from AA and NHW ER+ breast cancer cases (AA n = 48, NHW n = 54) and cancer-free controls (AA n = 100, NHW n = 48) to conduct an untargeted metabolomics analysis using gas chromatography mass spectrometry (GC-MS) to identify metabolites that may be altered in the different racial groups. Unpaired t-test combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus in AA patients with breast cancer. The clinical relevance of the identified pathways was further examined in PanCancer Atlas breast cancer data set from The Cancer Genome Atlas Program (TCGA). We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed decreased circulating levels of amino acids compared to healthy controls, while fatty acids were significantly higher in NHW patients. By mapping these metabolites to potential epigenetic regulatory mechanisms, this study identified significant associations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases and Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific gene Negative Elongation Factor Complex E (NELFE) with histone methyltransferase activity, was associated with poor survival exclusively for AA individuals. We employed a comprehensive and novel approach that integrates multiple machine learning and statistical methods, coupled with human functional pathway analyses. The metabolic profile of plasma samples identified may help elucidate underlying molecular drivers of disproportionately aggressive ER+ tumor biology in AA women. It may ultimately lead to the identification of novel therapeutic targets. To our knowledge, this is a novel finding that describes a link between metabolic alterations and epigenetic regulation in AA breast cancer and underscores the need for detailed investigations into the biological underpinnings of breast cancer health disparities.

Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism

A common malignancy that affects women is breast cancer. It is the second leading cause of cancer-related death among women. Metabolic reprogramming occurs during cancer growth, invasion, and metastases. Functional magnetic resonance (MR) methods comprising an array of techniques have shown potential for illustrating physiological and molecular processes changes before anatomical manifestations on conventional MR imaging. Among these, in vivo proton (1H) MR spectroscopy (MRS) is widely used for differentiating breast malignancy from benign diseases by measuring elevated choline-containing compounds. Further, the use of hyperpolarized 13C and 31P MRS enhanced the understanding of glucose and phospholipid metabolism. The metabolic profiling of an array of biological specimens (intact tissues, tissue extracts, and various biofluids such as blood, urine, nipple aspirates, and fine needle aspirates) can also be investigated through in vitro high-resolution NMR spectroscopy and high-resolution magic angle spectroscopy (HRMAS). Such studies can provide information on more metabolites than what is seen by in vivo MRS, thus providing a deeper insight into cancer biology and metabolism. The analysis of a large number of NMR spectral data sets through multivariate statistical methods classified the tumor sub-types. It showed enormous potential in the development of new therapeutic approaches. Recently, multiparametric MRI approaches were found to be helpful in elucidating the pathophysiology of cancer by quantifying structural, vasculature, diffusion, perfusion, and metabolic abnormalities in vivo. This review focuses on the applications of NMR, MRS, and MRI methods in understanding breast cancer biology and in the diagnosis and therapeutic monitoring of breast cancer.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

Hormone-Independent Mouse Mammary Adenocarcinomas with Different Metastatic Potential Exhibit Different Metabolic Signatures

The metabolic characteristics of metastatic and non-metastatic breast carcinomas remain poorly studied. In this work, untargeted Nuclear Magnetic Resonance (NMR) metabolomics was used to compare two medroxyprogesterone acetate (MPA)-induced mammary carcinomas lines with different metastatic abilities. Different metabolic signatures distinguished the non-metastatic (59-2-HI) and the metastatic (C7-2-HI) lines, with glucose, amino acid metabolism, nucleotide metabolism and lipid metabolism as the major affected pathways. Non-metastatic tumours appeared to be characterised by: (a) reduced glycolysis and tricarboxylic acid cycle (TCA) activities, possibly resulting in slower NADH biosynthesis and reduced mitochondrial transport chain activity and ATP synthesis; (b) glutamate accumulation possibly related to reduced glutathione activity and reduced mTORC1 activity; and (c) a clear shift to lower phosphoscholine/glycerophosphocholine ratios and sphingomyelin levels. Within each tumour line, metabolic profiles also differed significantly between tumours (i.e., mice). Metastatic tumours exhibited marked inter-tumour changes in polar compounds, some suggesting different glycolytic capacities. Such tumours also showed larger intra-tumour variations in metabolites involved in nucleotide and cholesterol/fatty acid metabolism, in tandem with less changes in TCA and phospholipid metabolism, compared to non-metastatic tumours. This study shows the valuable contribution of untargeted NMR metabolomics to characterise tumour metabolism, thus opening enticing opportunities to find metabolic markers related to metastatic ability in endocrine breast cancer.

Clinical applications of breast cancer metabolomics using high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H MRS): systematic scoping review

INTRODUCTION

Breast cancer is a heterogeneous disease with different prognoses and responses to systemic treatment depending on its molecular characteristics, which makes it imperative to develop new biomarkers for an individualized diagnosis and personalized oncological treatment. Ex vivo high-resolution magic angle spinning proton magnetic resonance spectroscopy (HRMAS 1H MRS) is the most common technique for metabolic quantification in human surgical and biopsy tissue specimens.

OBJECTIVE

To perform a review of the current available literature on the clinical applications of HRMAS 1H MRS metabolic analysis in tissue samples of breast cancer patients.

METHODS

This systematic scoping review included original research papers published in the English language in peer-reviewed journals. Study selection was performed independently by two reviewers and preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were followed.

RESULTS

The literature search returned 159 studies and 26 papers were included as part of this systematic review. There was considerable variation regarding tissue type, aims, and statistical analysis methods across the different studies. To facilitate the interpretation of the results, the included studies were grouped according to their aims or main outcomes into: feasibility and tumor diagnosis (n = 6); tumor heterogeneity (n = 2); correlation with proteomics/transcriptomics (n = 3); correlation with prognostic factors (n = 11); and response evaluation to NAC (n = 4).

CONCLUSION

There is a lot of potential in including metabolic information of breast cancer tissue obtained with HRMAS 1H MRS. To date, studies show that metabolic concentrations quantified by this technique can be related to the diagnosis, prognosis, and treatment response in breast cancer patients.

HR-MAS NMR Based Quantitative Metabolomics in Breast Cancer

High resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy is increasingly used for profiling of breast cancer tissue, delivering quantitative information for approximately 40 metabolites. One unique advantage of the method is that it can be used to analyse intact tissue, thereby requiring only minimal sample preparation. Importantly, since the method is non-destructive, it allows further investigations of the same specimen using for instance transcriptomics. Here, we discuss technical aspects critical for a successful analysis—including sample handling, measurement conditions, pulse sequences for one- and two dimensional analysis, and quantification methods—and summarize available studies, with a focus on significant associations of metabolite levels with clinically relevant parameters.

BI-RADS 3-5 microcalcifications: prediction of lymph node metastasis of breast cancer

Purpose

To determine whether the clinicopathological parameters and Breast Imaging Reporting and Data System (BI-RADS) 3–5 microcalcifications differed between lymph node positive (LN (+)) and lymph node negative (LN (−)) invasive ductal carcinoma (IDC).

Results

For microcalcification-associated breast cancers, seven selected features (age, tumor size, Ki-67 status, lymphovascular invasion, calcification range, calcification diameter and calcification density) were significantly associated with LN status (all P < 0.05). Multivariate logistic regression analysis found that three risk factors (age: older vs. younger OR: 0.973 P = 0.006, tumor size: larger vs. smaller OR: 1.671, P < 0.001 and calcification density: calcifications > 20/cm² vs. calcifications ≤ 20/cm² OR: 1.698, P < 0.001) were significant independent predictors. This model had an area under the receiver operating characteristic curve (AUC) of 0.701. The nodal staging (N0 and N1 χ² = 5.701, P = 0.017; N0 and N2 χ² = 6.614, P = 0.013) was significantly positively associated with calcification density. The luminal B subtype had the highest risk of LN metastasis. Multivariate analysis demonstrated that calcification > 2 cm in range (OR: 2.209) and larger tumor size (OR: 1.882) were independently predictive of LN metastasis in the luminal B subtype (AUC = 0.667).

Materials and Methods

Mammographic images of 419 female breast cancer patients were included. Associations between the risk factors and LN status were evaluated using a Chi-square test, ANOVA and binary logistic regression analysis.

Conclusions

This study found that age, tumor size and calcifications density can be conveniently used to facilitate the preoperative prediction of LN metastasis. The luminal B subtype has the highest risk of LN metastasis among the microcalcification-associated breast cancers.

Impact of intratumoral heterogeneity of breast cancer tissue on quantitative metabolomics using high-resolution magic angle spinning 1 H NMR spectroscopy

High-resolution magic angle spinning (HR MAS) nuclear magnetic resonance (NMR) spectroscopy is increasingly being used to study metabolite levels in human breast cancer tissue, assessing, for instance, correlations with prognostic factors, survival outcome or therapeutic response. However, the impact of intratumoral heterogeneity on metabolite levels in breast tumor tissue has not been studied comprehensively. More specifically, when biopsy material is analyzed, it remains questionable whether one biopsy is representative of the entire tumor. Therefore, multi-core sampling (n = 6) of tumor tissue from three patients with breast cancer, followed by lipid (0.9- and 1.3-ppm signals) and metabolite quantification using HR MAS ¹ H NMR, was performed, resulting in the quantification of 32 metabolites. The mean relative standard deviation across all metabolites for the six tumor cores sampled from each of the three tumors ranged from 0.48 to 0.74. This was considerably higher when compared with a morphologically more homogeneous tissue type, here represented by murine liver (0.16-0.20). Despite the seemingly high variability observed within the tumor tissue, a random forest classifier trained on the original sample set (training set) was, with one exception, able to correctly predict the tumor identity of an independent series of cores (test set) that were additionally sampled from the same three tumors and analyzed blindly. Moreover, significant differences between the tumors were identified using one-way analysis of variance (ANOVA), indicating that the intertumoral differences for many metabolites were larger than the intratumoral differences for these three tumors. That intertumoral differences, on average, were larger than intratumoral differences was further supported by the analysis of duplicate tissue cores from 15 additional breast tumors. In summary, despite the observed intratumoral variability, the results of the present study suggest that the analysis of one, or a few, replicates per tumor may be acceptable, and supports the feasibility of performing reliable analyses of patient tissue.

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

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

Metabolomic signature of brain cancer

Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.

Breast Tissue Metabolism by Magnetic Resonance Spectroscopy

Metabolic alterations are known to occur with oncogenesis and tumor progression. During malignant transformation, the metabolism of cells and tissues is altered. Cancer metabolism can be studied using advanced technologies that detect both metabolites and metabolic activities. Identification, characterization, and quantification of metabolites (metabolomics) are important for metabolic analysis and are usually done by nuclear magnetic resonance (NMR) or by mass spectrometry. In contrast to the magnetic resonance imaging that is used to monitor the tumor morphology during progression of the disease and during therapy, in vivo NMR spectroscopy is used to study and monitor tumor metabolism of cells/tissues by detection of various biochemicals or metabolites involved in various metabolic pathways. Several in vivo, in vitro and ex vivo NMR studies using ¹H and ³¹P magnetic resonance spectroscopy (MRS) nuclei have documented increased levels of total choline containing compounds, phosphomonoesters and phosphodiesters in human breast cancer tissues, which is indicative of altered choline and phospholipid metabolism. These levels get reversed with successful treatment. Another method that increases the sensitivity of substrate detection by using nuclear spin hyperpolarization of ¹³C-lableled substrates by dynamic nuclear polarization has revived a great interest in the study of cancer metabolism. This review discusses breast tissue metabolism studied by various NMR/MRS methods.

Serum Metabolomic Profiles for Breast Cancer Diagnosis, Grading and Staging by Gas Chromatography-Mass Spectrometry

Detection of metabolic signature for breast cancer (BC) has the potential to improve patient prognosis. This study identified potentially significant metabolites differentiating between breast cancer patients and healthy controls to help in diagnosis, grading, staging and determination of neoadjuvant status. Serum was collected from 152 pre-operative breast cancer (BC) patients and 155 healthy controls in this case-controlled study. Gas chromatography-mass spectrometry (GC-MS) was used to obtain metabolic profiles followed by chemometric analysis with the identification of significantly differentiated metabolites including 7 for diagnosis, 18 for grading, 23 for staging, 15 for stage III subcategory and 10 for neoadjuvant status (p-value < 0.05). Partial Least Square Discriminant Analysis (PLS-DA) model revealed a distinct separation between healthy controls and BC patients with a sensitivity of 96% and specificity of 100% on external validation. Models for grading, staging and neoadjuvant status were built with Decision Tree Algorithm with predictive accuracy of 71.5%, 71.3% and 79.8% respectively. Pathway analysis revealed increased glycolysis, lipogenesis, and production of volatile organic metabolites indicating the metabolic alterations in breast cancer.

Metabolomics of Breast Cancer Using High-Resolution Magic Angle Spinning Magnetic Resonance Spectroscopy: Correlations with 18F-FDG Positron Emission Tomography-Computed Tomography, Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging MRI

Purpose

Our goal in this study was to find correlations between breast cancer metabolites and conventional quantitative imaging parameters using high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) and to find breast cancer subgroups that show high correlations between metabolites and imaging parameters.

Materials and methods

Between August 2010 and December 2013, we included 53 female patients (mean age 49.6 years; age range 32–75 years) with a total of 53 breast lesions assessed by the Breast Imaging Reporting and Data System. They were enrolled under the following criteria: breast lesions larger than 1 cm in diameter which 1) were suspicious for malignancy on mammography or ultrasound (US), 2) were pathologically confirmed to be breast cancer with US-guided core-needle biopsy (CNB) 3) underwent 3 Tesla MRI with dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) and positron emission tomography-computed tomography (PET-CT), and 4) had an attainable immunohistochemistry profile from CNB. We acquired spectral data by HR-MAS MRS with CNB specimens and expressed the data as relative metabolite concentrations. We compared the metabolites with the signal enhancement ratio (SER), maximum standardized FDG uptake value (SUV max), apparent diffusion coefficient (ADC), and histopathologic prognostic factors for correlation. We calculated Spearman correlations and performed a partial least squares-discriminant analysis (PLS-DA) to further classify patient groups into subgroups to find correlation differences between HR-MAS spectroscopic values and conventional imaging parameters.

Results

In a multivariate analysis, the PLS-DA models built with HR-MAS MRS metabolic profiles showed visible discrimination between high and low SER, SUV, and ADC. In luminal subtype breast cancer, compared to all cases, high SER, ADV, and SUV were more closely clustered by visual assessment. Multiple metabolites were correlated with SER and SUV in all cases. Multiple metabolites showed correlations with SER and SUV in the ER positive, HER2 negative, and Ki-67 negative groups.

Conclusion

High levels of PC, choline, and glycine acquired from HR-MAS MRS using CNB specimens were noted in the high SER group via DCE MRI and the high SUV group via PET-CT, with significant correlations between choline and SER and between PC and SUV. Further studies should investigate whether HR-MAS MRS using CNB specimens can provide similar or more prognostic information than conventional quantitative imaging parameters.

Metabonomics and diagnostics

Metabonomic techniques have considerable potential in the field of clinical diagnostics, typifying the application of a translational research paradigm. Care must be taken at all stages to apply appropriate methodology with accurate patient selection and profiling, and rigorous data acquisition and handling, to ensure clinical validity.An ever-increasing number of publications in a wide range of diseases and diverse patient groups suggest a variety of potential clinical uses; prospective studies in large validation cohorts are required to bring metabonomics into routine clinical practice. In this chapter, the utility of metabonomics as a diagnostic tool will be discussed.

Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups

BACKGROUND

Tumour classification, based on histopathology or molecular pathology, is of value to predict tumour behaviour and to select appropriate treatment. In retinoblastoma, pathology information is not available at diagnosis and only exists for enucleated tumours. Alternative methods of tumour classification, using noninvasive techniques such as magnetic resonance spectroscopy, are urgently required to guide treatment decisions at the time of diagnosis.

METHODS

High-resolution magic-angle spinning magnetic resonance spectroscopy (HR-MAS MRS) was undertaken on enucleated retinoblastomas. Principal component analysis and cluster analysis of the HR-MAS MRS data was used to identify tumour subgroups. Individual metabolite concentrations were determined and were correlated with histopathological risk factors for each group.

RESULTS

Multivariate analysis identified three metabolic subgroups of retinoblastoma, with the most discriminatory metabolites being taurine, hypotaurine, total-choline and creatine. Metabolite concentrations correlated with specific histopathological features: taurine was correlated with differentiation, total-choline and phosphocholine with retrolaminar optic nerve invasion, and total lipids with necrosis.

CONCLUSIONS

We have demonstrated that a metabolite-based classification of retinoblastoma can be obtained using ex vivo magnetic resonance spectroscopy, and that the subgroups identified correlate with histopathological features. This result justifies future studies to validate the clinical relevance of these subgroups and highlights the potential of in vivo MRS as a noninvasive diagnostic tool for retinoblastoma patient stratification.

Metabolomic profiling of hormone-dependent cancers: a bird's eye view

Hormone-dependent cancers present a significant public health challenge, because they are among the most common cancers in the world. One factor associated with cancer development and progression is metabolic reprogramming. By understanding these alterations, we can identify potential markers and novel biochemical therapeutic targets. Metabolic profiling is an advanced technology that allows investigators to assess low-molecular-weight compounds that reflect physiological alterations. Current research in metabolomics on prostate (PCa) and breast cancer (BCa) have made great strides in uncovering specific metabolic pathways that are associated with cancer development, progression, and resistance. In this review, we highlight some of the major findings and potential therapeutic advances that have been reported utilizing this technology.

Distinct Metabolomic Profiles of Papillary Thyroid Carcinoma and Benign Thyroid Adenoma

Papillary thyroid carcinoma (PTC) and benign thyroid adenoma (BTA) are the most common head and neck tumors. However, the metabolic differences between PTC and BTA have not been characterized. The aim of this study was to identify the metabolic profiles of these two types of tumors using a metabolomics approach. Tumors and adjacent nontumor specimens collected from 57 patients with PTC and 48 patients with BTA were profiled using gas chromatography-time-of-flight mass spectrometry and ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. A panel of 46 and 44 differentially expressed metabolites were identified in the PTC and BTA specimens, respetively, and compared with nontumor tissues. Common metabolic signatures, as characterized by increased glycolysis, amino acid metabolism, one carbon metabolism and tryptophan metabolism, were found in both types of tumors. Purine and pyrimidine metabolism was significantly elevated in the PTC specimens, and taurine and hypotaurine levels were also higher in the PTC tissues. Increased fatty acid and bile acid levels were found, especially in the BTA tissues. The metabolic profiles of the PTC and BTA tissues include both similar and remarkably different metabolites, suggesting the presence of common and unique mechanistic pathways in these types of tumors during tumorigenesis.

1H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells

A major obstacle of successful chemotherapy is the development of multidrug resistance (MDR) in the cancer cells, which is difficult to reverse. Metabolomic analysis, an emerging approach that has been increasingly applied in various fields, is able to reflect the unique chemical fingerprints of specific cellular processes in an organism. The assessment of such metabolite changes can be used to identify novel therapeutic biomarkers. In the present study, ¹H nuclear magnetic resonance (NMR) spectroscopy was used to analyze the extracellular metabolomic spectrum of the Tca8113 oral squamous carcinoma cell line, in which MDR was induced using the carboplatin (CBP) and pingyangmycin (PYM) chemotherapy drugs in vitro. The data were analyzed using the principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods. The results demonstrated that the extracellular metabolomic spectrum of metabolites such as glutamate, glycerophosphoethanol amine, α-Glucose and β-Glucose for the drug-induced Tca8113 cells was significantly different from the parental Tca8113 cell line. A number of biochemicals were also significantly different between the groups based on their NMR spectra, with drug-resistant cells presenting relatively higher levels of acetate and lower levels of lactate. In addition, a significantly higher peak was observed at δ 3.35 ppm in the spectrum of the PYM-induced Tca8113 cells. Therefore, ¹H NMR-based metabolomic analysis has a high potential for monitoring the formation of MDR during clinical tumor chemotherapy in the future.

Pathway-centric integrative analysis identifies RRM2 as a prognostic marker in breast cancer associated with poor survival and tamoxifen resistance

Breast cancer (BCa) molecular subtypes include luminal A, luminal B, normal-like, HER-2-enriched, and basal-like tumors, among which luminal B and basal-like cancers are highly aggressive. Biochemical pathways associated with patient survival or treatment response in these more aggressive subtypes are not well understood. With the limited availability of pathologically verified clinical specimens, cell line models are routinely used for pathway-centric studies. We measured the metabolome of luminal and basal-like BCa cell lines using mass spectrometry, linked metabolites to biochemical pathways using Gene Set Analysis, and developed a novel rank-based method to select pathways on the basis of their enrichment in patient-derived omics data sets and prognostic relevance. Key mediators of the pathway were then characterized for their role in disease progression. Pyrimidine metabolism was altered in luminal versus basal BCa, whereas the combined expression of its associated genes or expression of one key gene, ribonucleotide reductase subunit M2 (RRM2) alone, associated significantly with decreased survival across all BCa subtypes, as well as in luminal patients resistant to tamoxifen. Increased RRM2 expression in tamoxifen-resistant patients was verified using tissue microarrays, whereas the metabolic products of RRM2 were higher in tamoxifen-resistant cells and in xenograft tumors. Both genetic and pharmacological inhibition of this key enzyme in tamoxifen-resistant cells significantly decreased proliferation, reduced expression of cell cycle genes, and sensitized the cells to tamoxifen treatment. Our study suggests for evaluating RRM2-associated metabolites as noninvasive markers for tamoxifen resistance and its pharmacological inhibition as a novel approach to overcome tamoxifen resistance in BCa.

Application of metabolomics in thyroid cancer research

Thyroid cancer is the most common endocrine malignancy with four major types distinguished on the basis of histopathological features: papillary, follicular, medullary, and anaplastic. Classification of thyroid cancer is the primary step in the assessment of prognosis and selection of the treatment. However, in some cases, cytological and histological patterns are inconclusive; hence, classification based on histopathology could be supported by molecular biomarkers, including markers identified with the use of high-throughput "omics" techniques. Beside genomics, transcriptomics, and proteomics, metabolomic approach emerges as the most downstream attitude reflecting phenotypic changes and alterations in pathophysiological states of biological systems. Metabolomics using mass spectrometry and magnetic resonance spectroscopy techniques allows qualitative and quantitative profiling of small molecules present in biological systems. This approach can be applied to reveal metabolic differences between different types of thyroid cancer and to identify new potential candidates for molecular biomarkers. In this review, we consider current results concerning application of metabolomics in the field of thyroid cancer research. Recent studies show that metabolomics can provide significant information about the discrimination between different types of thyroid lesions. In the near future, one could expect a further progress in thyroid cancer metabolomics leading to development of molecular markers and improvement of the tumor types classification and diagnosis.

Metabolic characterization of triple negative breast cancer

Background

The aims of this study were to characterize the metabolite profiles of triple negative breast cancer (TNBC) and to investigate the metabolite profiles associated with human epidermal growth factor receptor-2/neu (HER-2) overexpression using ex vivo high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). Metabolic alterations caused by the different estrogen receptor (ER), progesterone receptor (PgR) and HER-2 receptor statuses were also examined. To investigate the metabolic differences between two distinct receptor groups, TNBC tumors were compared to tumors with ER^pos/PgR^pos/HER-2^pos status which for the sake of simplicity is called triple positive breast cancer (TPBC).

Methods

The study included 75 breast cancer patients without known distant metastases. HR MAS MRS was performed for identification and quantification of the metabolite content in the tumors. Multivariate partial least squares discriminant analysis (PLS-DA) modeling and relative metabolite quantification were used to analyze the MR data.

Results

Choline levels were found to be higher in TNBC compared to TPBC tumors, possibly related to cell proliferation and oncogenic signaling. In addition, TNBC tumors contain a lower level of Glutamine and a higher level of Glutamate compared to TPBC tumors, which indicate an increase in glutaminolysis metabolism. The development of glutamine dependent cell growth or “Glutamine addiction” has been suggested as a new therapeutic target in cancer. Our results show that the metabolite profiles associated with HER-2 overexpression may affect the metabolic characterization of TNBC. High Glycine levels were found in HER-2^pos tumors, which support Glycine as potential marker for tumor aggressiveness.

Conclusions

Metabolic alterations caused by the individual and combined receptors involved in breast cancer progression can provide a better understanding of the biochemical changes underlying the different breast cancer subtypes. Studies are needed to validate the potential of metabolic markers as targets for personalized treatment of breast cancer subtypes.

Metabonomic profiling of bladder cancer

Early diagnosis and life-long surveillance are clinically important to improve the long-term survival of bladder cancer patients. Currently, a noninvasive biomarker that is as sensitive and specific as cystoscopy in detecting bladder tumors is lacking. Metabonomics is a complementary approach for identifying perturbed metabolic pathways in bladder cancer. Significant progress has been made using modern metabonomic techniques to characterize and distinguish bladder cancer patients from control subjects, identify marker metabolites, and shed insights on the disease biology and potential therapeutic targets. With its rapid development, metabonomics has the potential to impact the clinical management of bladder cancer patients in the future by revolutionizing the diagnosis and life-long surveillance strategies and stratifying patients for diagnostic, surgical, and therapeutic clinical trials. An introduction to metabonomics, typical metabonomic workflow, and critical evaluation of metabonomic investigations in identifying biomarkers for the diagnosis of bladder cancer are presented.

Applying metabolomics to understand the aggressive phenotype and identify novel therapeutic targets in glioblastoma

Glioblastoma continues to be an invariably fatal malignancy. The established approach for understanding the biology of these aggressive tumors in an effort to identify novel molecular targets has largely been genotype-based. Unfortunately, clinical gains offered by this level of understanding have been limited, largely based on the complex nature of signaling networks associated with tumorigenesis and the inability to delineate the key “functional” signaling pathways actually driving growth in an individual tumor. Metabolomics is the global quantitative assessment of endogenous metabolites within a biological system, taking into account genetic regulation, altered kinetic activity of enzymes, and changes in metabolic reactions. Thus, compared to genomics and proteomics, metabolomics reflects changes in phenotype and therefore function. In this review, we highlight some of the key advancements that have been made in applying metabolomics to understand the aggressive phenotype of glioblastoma. Collectively, these studies have provided a previously unrecognized window into the underlying biology of these tumors. Current and future efforts are designed to determine how this technology may be applied to improve diagnosis and predict the aggressiveness of glioblastoma, and more importantly, identify novel, therapeutic strategies designed to improve clinical outcomes.

Quantitative measurement of organic acids in tissues from gastric cancer patients indicates increased glucose metabolism in gastric cancer

The levels of organic acids representing metabolic pathway end products are important indicators of physiological status, and may be associated with metabolic changes in cancer. The aim of this study is to investigate the levels of organic acids in cancerous and normal tissues from gastric cancer patients and to confirm the role of metabolic alterations in gastric carcinogenesis. Organic acids in normal and cancerous tissues from forty-five patients with gastric adenocarcinoma were investigated by gas chromatography-mass spectrometry in selected ion monitoring mode as methoxime/tert-butyldimethylsilyl derivatives. We analysed the significant differences in the levels of organic acids in normal and cancer tissues and investigated the correlation of these levels in cancer tissues with clinicopathological features. The levels of Krebs cycle components, including α-ketoglutaric acid, succinic acid, fumaric acid, malic acid and oxaloacetic acid, were significantly increased in cancer tissues compared to normal tissues. In addition, the levels of glycolytic products, including pyruvic acid and lactic acid, as well as the levels of ketone bodies, including 3-hydroxybutyric acid, were also significantly increased in cancer tissues compared to normal tissues. The levels of ketone bodies in cancer tissues with differentiated histology and in intestinal-type cancer tissues were significantly increased. The organic acid profiling analysis described here may be a generally useful clinical tool for understanding the complexity of metabolic events in gastric adenocarcinoma, and organic acids may have potential as metabolic markers for the future discovery of diagnostic and therapeutic modalities.

Silencing of the glycerophosphocholine phosphodiesterase GDPD5 alters the phospholipid metabolite profile in a breast cancer model in vivo as monitored by (31) P MRS

Abnormal choline phospholipid metabolism is an emerging hallmark of cancer, which is implicated in carcinogenesis and tumor progression. The malignant metabolic phenotype is characterized by high levels of phosphocholine (PC) and relatively low levels of glycerophosphocholine (GPC) in aggressive breast cancer cells. Phosphorus ((31) P) MRS is able to non-invasively detect these water-soluble metabolites of choline as well as ethanolamine phospholipid metabolism. Here we have investigated the effects of stably silencing glycerophosphoester diesterase domain containing 5 (GDPD5), which is an enzyme with glycerophosphocholine phosphodiesterase activity, in MDA-MB-231 breast cancer cells and orthotopic tumor xenografts. Tumors in which GDPD5 was stably silenced with GDPD5-specific shRNA contained increased levels of GPC and phosphoethanolamine (PE) compared with control tumors.

Follicular adenomas exhibit a unique metabolic profile. ¹H NMR studies of thyroid lesions

Thyroid cancer is the most common endocrine malignancy. However, more than 90% of thyroid nodules are benign. It remains unclear whether thyroid carcinoma arises from preexisting benign nodules. Metabolomics can provide valuable and comprehensive information about low molecular weight compounds present in living systems and further our understanding of the biology regulating pathological processes. Herein, we applied ¹H NMR-based metabolic profiling to identify the metabolites present in aqueous tissue extracts of healthy thyroid tissue (H), non-neoplastic nodules (NN), follicular adenomas (FA) and malignant thyroid cancer (TC) as an alternative way of investigating cancer lesions. Multivariate statistical methods provided clear discrimination not only between healthy thyroid tissue and pathological thyroid tissue but also between different types of thyroid lesions. Potential biomarkers common to all thyroid lesions were identified, namely, alanine, methionine, acetone, glutamate, glycine, lactate, tyrosine, phenylalanine and hypoxanthine. Metabolic changes in thyroid cancer were mainly related to osmotic regulators (taurine and scyllo- and myo-inositol), citrate, and amino acids supplying the TCA cycle. Thyroid follicular adenomas were found to display metabolic features of benign non-neoplastic nodules and simultaneously displayed a partial metabolic profile associated with malignancy. This finding allows the discrimination of follicular adenomas from benign non-neoplastic nodules and thyroid cancer with similar accuracy. Moreover, the presented data indicate that follicular adenoma could be an individual stage of thyroid cancer development.

Magnetic resonance metabolic profiling of breast cancer tissue obtained with core needle biopsy for predicting pathologic response to neoadjuvant chemotherapy

The purpose of this study was to determine whether metabolic profiling of core needle biopsy (CNB) samples using high-resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) could be used for predicting pathologic response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer. After institutional review board approval and informed consent were obtained, CNB tissue samples were collected from 37 malignant lesions in 37 patients before NAC treatment. The metabolic profiling of CNB samples were performed by HR-MAS MRS. Metabolic profiles were compared according to pathologic response to NAC using the Mann-Whitney test. Multivariate analysis was performed with orthogonal projections to latent structure-discriminant analysis (OPLS-DA). Various metabolites including choline-containing compounds were identified and quantified by HR-MAS MRS in all 37 breast cancer tissue samples obtained by CNB. In univariate analysis, the metabolite concentrations and metabolic ratios of CNB samples obtained with HR-MAS MRS were not significantly different between different pathologic response groups. However, there was a trend of lower levels of phosphocholine/creatine ratio and choline-containing metabolite concentrations in the pathologic complete response group compared to the non-pathologic complete response group. In multivariate analysis, the OPLS-DA models built with HR-MAS MR metabolic profiles showed visible discrimination between the pathologic response groups. This study showed OPLS-DA multivariate analysis using metabolic profiles of pretreatment CNB samples assessed by HR- MAS MRS may be used to predict pathologic response before NAC, although we did not identify the metabolite showing statistical significance in univariate analysis. Therefore, our preliminary results raise the necessity of further study on HR-MAS MR metabolic profiling of CNB samples for a large number of cancers.

Prediction of gene phenotypes based on GO and KEGG pathway enrichment scores

Observing what phenotype the overexpression or knockdown of gene can cause is the basic method of investigating gene functions. Many advanced biotechnologies, such as RNAi, were developed to study the gene phenotype. But there are still many limitations. Besides the time and cost, the knockdown of some gene may be lethal which makes the observation of other phenotypes impossible. Due to ethical and technological reasons, the knockdown of genes in complex species, such as mammal, is extremely difficult. Thus, we proposed a new sequence-based computational method called kNNA-based method for gene phenotypes prediction. Different to the traditional sequence-based computational method, our method regards the multiphenotype as a whole network which can rank the possible phenotypes associated with the query protein and shows a more comprehensive view of the protein's biological effects. According to the prediction result of yeast, we also find some more related features, including GO and KEGG information, which are making more contributions in identifying protein phenotypes. This method can be applied in gene phenotype prediction in other species.

HR-MAS NMR tissue metabolomic signatures cross-validated by mass spectrometry distinguish bladder cancer from benign disease

Effective diagnosis and surveillance of bladder cancer (BCa) is currently challenged by detection methods that are of poor sensitivity, particularly for low-grade tumors, resulting in unnecessary invasive procedures and economic burden. We performed HR-MAS NMR-based global metabolomic profiling and applied unsupervised principal component analysis (PCA) and hierarchical clustering performed on NMR data set of bladder-derived tissues and identified metabolic signatures that differentiate BCa from benign disease. A partial least-squares discriminant analysis (PLS-DA) model (leave-one-out cross-validation) was used as a diagnostic model to distinguish benign and BCa tissues. Receiver operating characteristic curve generated either from PC1 loadings of PCA or from predicted Y-values resulted in an area under curve of 0.97. Relative quantification of more than 15 tissue metabolites derived from HR-MAS NMR showed significant differences (P < 0.001) between benign and BCa samples. Noticeably, striking metabolic signatures were observed even for early stage BCa tissues (Ta-T1), demonstrating the sensitivity in detecting BCa. With the goal of cross-validating metabolic signatures derived from HR-MAS NMR, we utilized the same tissue samples to analyze 8 metabolites through gas chromatography-mass spectrometry (GC-MS)-targeted analysis, which undoubtedly complements HR-MAS NMR-derived metabolomic information. Cross-validation through GC-MS clearly demonstrates the utility of a straightforward, nondestructive, and rapid HR-MAS NMR technique for clinical diagnosis of BCa with even greater sensitivity. In addition to its utility as a diagnostic tool, these studies will lead to a better understanding of aberrant metabolic pathways in cancer as well as the design and implementation of personalized cancer therapy through metabolic modulation.

Leveraging metabolomics to assess the next generation of temozolomide-based therapeutic approaches for glioblastomas

Glioblastoma multiforme (GBM) is the most common adult primary tumor of the central nervous system. The current standard of care for glioblastoma patients involves a combination of surgery, radiotherapy and chemotherapy with the alkylating agent temozolomide. Several mechanisms underlying the inherent and acquired temozolomide resistance have been identified and contribute to treatment failure. Early identification of temozolomide-resistant GBM patients and improvement of the therapeutic strategies available to treat this malignancy are of uttermost importance. This review initially looks at the molecular pathways underlying GBM formation and development with a particular emphasis placed on recent therapeutic advances made in the field. Our focus will next be directed toward the molecular mechanisms modulating temozolomide resistance in GBM patients and the strategies envisioned to circumvent this resistance. Finally, we highlight the diagnostic and prognostic value of metabolomics in cancers and assess its potential usefulness in improving the current standard of care for GBM patients.

Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer

Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long-term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography-mass spectrometry (LC-MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC-MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients.

Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery

In this study, the feasibility of high resolution magic angle spinning (HR MAS) magnetic resonance spectroscopy (MRS) of small tissue biopsies to distinguish between tumor and non-involved adjacent tissue was investigated. With the current methods, delineation of the tumor borders during breast cancer surgery is a challenging task for the surgeon, and a significant number of re-surgeries occur. We analyzed 328 tissue samples from 228 breast cancer patients using HR MAS MRS. Partial least squares discriminant analysis (PLS-DA) was applied to discriminate between tumor and non-involved adjacent tissue. Using proper double cross validation, high sensitivity and specificity of 91% and 93%, respectively was achieved. Analysis of the loading profiles from both principal component analysis (PCA) and PLS-DA showed the choline-containing metabolites as main biomarkers for tumor content, with phosphocholine being especially high in tumor tissue. Other indicative metabolites include glycine, taurine and glucose. We conclude that metabolic profiling by HR MAS MRS may be a potential method for on-line analysis of resection margins during breast cancer surgery to reduce the number of re-surgeries and risk of local recurrence.

2-hydroxyglutarate as a magnetic resonance biomarker for glioma subtyping

Mutations in the isocitrate dehydrogenase (IDH) genes are frequently found in gliomas and in a fraction of acute myeloid leukemia patients. This results in the production of an oncometabolite, 2-hydroxyglutarate (2-HG). Glioma patients harboring IDH mutations have a longer survival than their wild-type counterparts. 2-HG has been detected noninvasively in gliomas with IDH mutations using magnetic resonance spectroscopy (MRS), suggesting its potential clinical relevance for identifying glioma subtypes with better prognosis. In this paper, the recent developments in the MRS detection of the 2-HG in gliomas are reviewed, including the therapeutic potentials and translational values.

NMR HRMAS spectroscopy of lung biopsy samples: Comparison study between human, pig, rat, and mouse metabolomics

PURPOSE

Using the metabolomics by NMR high-resolution magic angle spinning spectroscopy, we assessed the lung metabolome of various animal species in order to identify the animal model that could be substituted to human lung in studies on fresh lung biopsies.

METHODS

The experiments were conducted on intact lung biopsy samples of pig, rat, mouse, and human using a Bruker Advance III 500 spectrometer. Thirty-five to 39 metabolites were identified and 23 metabolites were quantified. Principal component analysis, partial least-squares discriminant analysis, and analysis of variance tests were performed in order to compare the metabolic profiles of each animal lung biopsies to those of the human lung.

RESULTS

The metabolic composition between human and pig lung was similar. However, human lung was distinguishable from mouse and rat regarding: Trimethylamine N-oxide and betaïne which were present in rodents but not in human lung, carnitine, and glycerophosphocholine which were present in mouse but not in human lung. Conversely, succinic acid was undetected in rat lung. Furthermore, fatty acids concentration was significantly higher in rodent lungs compared to human lung.

CONCLUSION

Using the metabolomics by NMR high-resolution magic angle spinning spectroscopy on lung biopsy, samples allowed to highlight that pig lung seems to be close to human lung as regarding its metabolite composition with more similarities than dissimilarities.

Novel methodologies in metabolic profiling with a focus on molecular diagnostic applications

The metabolome contains all the biological end points of genomic, transcriptomic and proteomic perturbations, also including the influence of gut microbiota and the environment, giving a direct picture of an organism's ongoing metabolic state. Metabolomics thus has the potential to be an effective tool for early diagnosis of disease, and also to be a predictor of treatment response and survival. In recent years, the development of instrumental systems has enabled more comprehensive coverage of the metabolome. Advances in mass spectrometry and chromatography have particularly improved both the efficiency of nontargeted metabolic profiling as well as the sensitivity and reliability of targeted analyses. Mass spectrometric techniques are also increasingly becoming accepted as a routine diagnostic tool in clinical laboratories. This review summarizes the most recent advances and current challenges in metabolomics, with a focus on mass spectrometric methods utilized in biomarker research, highlighted with selected examples.

HR-MAS MR spectroscopy of breast cancer tissue obtained with core needle biopsy: correlation with prognostic factors

The purpose of this study was to examine the correlation between high-resolution magic angle spinning (HR-MAS) magnetic resonance (MR) spectroscopy using core needle biopsy (CNB) specimens and histologic prognostic factors currently used in breast cancer patients. After institutional review board approval and informed consent were obtained for this study, CNB specimens were collected from 36 malignant lesions in 34 patients. Concentrations and metabolic ratios of various choline metabolites were estimated by HR-MAS MR spectroscopy using CNB specimens. HR-MAS spectroscopic values were compared according to histopathologic variables [tumor size, lymph node metastasis, histologic grade, status of estrogens receptor (ER), progesterone receptor (PR), HER2 (a receptor for human epidermal growth factor), and Ki-67, and triple negativity]. Multivariate analysis was performed with Orthogonal Projections to Latent Structure-Discriminant Analysis (OPLS-DA). HR-MAS MR spectroscopy quantified and discriminated choline metabolites in all CNB specimens of the 36 breast cancers. Several metabolite markers [free choline (Cho), phosphocholine (PC), creatine (Cr), taurine, myo-inositol, scyllo-inositol, total choline (tCho), glycine, Cho/Cr, tCho/Cr, PC/Cr] on HR-MAS MR spectroscopy were found to correlate with histologic prognostic factors [ER, PR, HER2, histologic grade, triple negativity, Ki-67, poor prognosis]. OPLS-DA multivariate models were generally able to discriminate the status of histologic prognostic factors (ER, PR, HER2, Ki-67) and prognosis groups. Our study suggests that HR-MAS MR spectroscopy using CNB specimens can predict tumor aggressiveness prior to surgery in breast cancer patients. In addition, it may be helpful in the detection of reliable markers for breast cancer characterization.

Lactate and glycine-potential MR biomarkers of prognosis in estrogen receptor-positive breast cancers

Breast cancer is a heterogeneous disease with a variable prognosis. Clinical factors provide some information about the prognosis of patients with breast cancer; however, there is a need for additional information to stratify patients for improved and more individualized treatment. The aim of this study was to examine the relationship between the metabolite profiles of breast cancer tissue and 5-year survival. Biopsies from breast cancer patients (n=98) were excised during surgery and analyzed by high-resolution magic angle spinning MRS. The data were analyzed by multivariate principal component analysis and partial least-squares discriminant analysis, and the findings of important metabolites were confirmed by spectral integration of the metabolite peaks. Predictions of 5-year survival using metabolite profiles were compared with predictions using clinical parameters. Based on the metabolite profiles, patients with estrogen receptor (ER)-positive breast cancer (n=71) were separated into two groups with significantly different survival rates (p=0.024). Higher levels of glycine and lactate were found to be associated with lower survival rates by both multivariate analyses and spectral integration, and are suggested as biomarkers for breast cancer prognosis. Similar metabolic differences were not observed for ER-negative patients, where survivors could not be separated from nonsurvivors. Predictions of 5-year survival of ER-positive patients using metabolite profiles gave better and more robust results than those using traditional clinical parameters. The results imply that the metabolic state of a tumor may provide additional information concerning breast cancer prognosis. Further studies should be conducted in order to evaluate the role of MR metabolomics as an additional clinical tool for determining the prognosis of patients with breast cancer.

Analysis of cancer tissues by means of spectroscopic methods

The personalized approach in cancer treatment stimulates the search for new analytical techniques, including spectroscopic methods such as Raman spectroscopy, mass spectrometry MALDI (matrix-assisted laser desorption/ionization) imaging and high-resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR). The purpose of these studies is determination of metabolic profiles of cancer tissues, and their application in diagnostics and therapy of cancers. The review is mainly focused on application of HR MAS NMR technique. Qualitative and quantitative analysis of metabolites by means of this method is described for breast cancer tissues. In the near future HR MAS NMR in vitro studies of metabolic profiles combined with in vivo studies using MRI scanners may be applied as a new diagnostic tool.

[Preliminary results of metabolite in serum and urine of lung cancer patients detected by metabolomics]

背景与目的

肺癌是当今世界各国最常见的恶性肿瘤之一。目前尚没有寻找到理想的用于肺癌诊断的肿瘤标志物，因而尝试用各种新方法来探索新的生物学标志物已成为肺癌研究的热点。本研究采用代谢组学技术对肺癌患者和其它肺部疾病患者血清及尿液中的小分子代谢物质进行分析，以寻求潜在的肺癌肿瘤标志物。

方法

运用气相色谱/质谱法（gas chromatography/mass spectrometry, GC/MS）对19例肺癌与15例其它肺部疾病患者的血清及尿液样本进行代谢组学分析，采用正交偏最小二乘判别分析法（orthogonal to partial least squares discriminant analysis, OPLS-DA）进行建模，运用两样本的t检验寻找两组间差异性代谢产物。

结果

检测到血清中代谢产物共57种，尿液中代谢产物共38种，多变量统计结果显示肺癌患者与其它肺部疾病患者的代谢谱有明显差异，根据t检验结果寻找到血清相关的差异代谢产物13种，尿液相关的差异代谢产物7种。

结论

利用代谢组学方法能区分肺癌与其它肺部疾病患者，其结果在分子水平辅助肺癌的诊断、未来作为新技术应用于肺癌的诊断有一定的前景。

Absolute quantification of metabolites in breast cancer cell extracts by quantitative 2D (1) H INADEQUATE NMR

Metabolomic studies by NMR spectroscopy are increasingly employed for a variety of biomedical applications. A very standardized 1D proton NMR protocol is generally employed for data acquisition, associated with multivariate statistical tests. Even if targeted approaches have been proposed to quantify metabolites from such experiments, quantification is often made difficult by the high degree of overlap characterizing (1) H NMR spectra of biological samples. Two-dimensional spectroscopy presents a high potential for accurately measuring concentrations in complex samples, as it offers a much higher discrimination between metabolite resonances. We have recently proposed an original approach relying on the (1) H 2D INADEQUATE pulse sequence, optimized for fast quantitative analysis of complex metabolic mixtures. Here, the first application of the quantitative (1) H 2D INADEQUATE experiment to a real metabonomic study is presented. Absolute metabolite concentrations are determined for different breast cancer cell line extracts, by a standard addition procedure. The protocol is characterized by high analytical performances (accuracy better than 1%, excellent linearity), even if it is affected by relatively long acquisition durations (15 min to 1 h per spectrum). It is applied to three different cell lines, expressing different hormonal and tyrosine kinase receptors. The absolute concentrations of 15 metabolites are determined, revealing significant differences between cell lines. The metabolite concentrations measured are in good agreement with previous studies regarding metabolic profile changes of breast cancer. While providing a high degree of discrimination, this methodology offers a powerful tool for the determination of relevant biomarkers.

Implementation of molecular phenotyping approaches in the personalized surgical patient journey

OBJECTIVE

The present review describes commonly employed metabolic profiling platforms and discusses the current and likely future application of these technologies in surgery.

BACKGROUND

The metabolic adaptations that occur in response to surgical illness and trauma are incompletely understood. Evaluating these will be critical to the development of personalized surgical health solutions. Metabonomics is an advancing field in systems biology, which provides a means of interrogating these metabolic shifts.

METHODS

Recent literature regarding metabolic profiling technologies and their applications in surgical practice are discussed. Future strategies are proposed for the incorporation of these and next-generation technologies in the evaluation of all steps in the patient surgical pathway.

RESULTS

Metabolite-based profiling has provided valuable insights into the metabolic irregularities that occur in cancer development and progression across a variety of cancer subclasses including colorectal, breast, prostate, and lung cancers. In addition, metabolic modeling has shown considerable promise in other surgical conditions including trauma and sepsis and in the assessment of pharmacotherapeutic efficacy.

DISCUSSION

Metabonomics offers a posttranscriptional view of system activity providing functional information downstream of the genome and proteome. Information at this level will provide the surgeon with a novel means of evaluating major socioeconomic problems such as cancer and sepsis. In addition, the rapid nature of emerging next generation profiling platforms provides a viable means of "real-time" perioperative metabolic assessment and optimization.