Early detection of recurrent breast cancer using metabolite profiling

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

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

Multidimensional gas chromatography methods for bioanalytical research

Multidimensional gas chromatography (MDGC) methods are high-resolution volatile chemical separation techniques, and comprise classical heart-cutting MDGC and its more recent incarnation, comprehensive 2D GC. Although available for a long period, MDGC approaches are still not widely practiced in the field of bioanalysis, possibly reflecting the general preference for regular GC versus MDGC approaches. With the recent introduction of ‘-omic’ techniques that emphasize global nontargeted profiling of metabolites within living systems, it is evident that MDGC is gaining momentum as a separation tool, since it offers very high resolution. By untangling metabolites within highly complex biological matrices, and expanding the metabolic coverage, MDGC plays a frontline role in ‘-omics’ based studies. This review highlights state-of-the-art MDGC approaches, and summarizes the recent developments in bioanalytics.

The utility of metabolomics in natural product and biomarker characterization

BACKGROUND

Metabolomics is a well-established rapidly developing research field involving quantitative and qualitative metabolite assessment within biological systems. Recent improvements in metabolomics technologies reveal the unequivocal value of metabolomics tools in natural products discovery, gene-function analysis, systems biology and diagnostic platforms.

SCOPE OF REVIEW

We review here some of the prominent metabolomics methodologies employed in data acquisition and analysis of natural products and disease-related biomarkers.

MAJOR CONCLUSIONS

This review demonstrates that metabolomics represents a highly adaptable technology with diverse applications ranging from environmental toxicology to disease diagnosis. Metabolomic analysis is shown to provide a unique snapshot of the functional genetic status of an organism by examining its biochemical profile, with relevance toward resolving phylogenetic associations involving horizontal gene transfer and distinguishing subgroups of genera possessing high genetic homology, as well as an increasing role in both elucidating biosynthetic transformations of natural products and detecting preclinical biomarkers of numerous disease states.

GENERAL SIGNIFICANCE

This review expands the interest in multiplatform combinatorial metabolomic analysis. The applications reviewed range from phylogenetic assignment, biosynthetic transformations of natural products, and the detection of preclinical biomarkers.

Serum metabolomic profiles evaluated after surgery may identify patients with oestrogen receptor negative early breast cancer at increased risk of disease recurrence. Results from a retrospective study

PURPOSE

Metabolomics is a global study of metabolites in biological samples. In this study we explored whether serum metabolomic spectra could distinguish between early and metastatic breast cancer patients and predict disease relapse.

METHODS

Serum samples were analysed from women with metastatic (n = 95) and predominantly oestrogen receptor (ER) negative early stage (n = 80) breast cancer using high resolution nuclear magnetic resonance spectroscopy. Multivariate statistics and a Random Forest classifier were used to create a prognostic model for disease relapse in early patients.

RESULTS

In the early breast cancer training set (n = 40), metabolomics correctly distinguished between early and metastatic disease in 83.7% of cases. A prognostic risk model predicted relapse with 90% sensitivity (95% CI 74.9-94.8%), 67% specificity (95% CI 63.0-73.4%) and 73% predictive accuracy (95% CI 70.6-74.8%). These results were reproduced in an independent early breast cancer set (n = 40), with 82% sensitivity, 72% specificity and 75% predictive accuracy. Disease relapse was associated with significantly lower levels of histidine (p = 0.0003) and higher levels of glucose (p = 0.01), and lipids (p = 0.0003), compared with patients with no relapse.

CONCLUSIONS

The performance of a serum metabolomic prognostic model for disease relapse in individuals with ER-negative early stage breast cancer is promising. A confirmation study is ongoing to better define the potential of metabolomics as a host and tumour-derived prognostic tool.

Metabolomic studies of human gastric cancer: Review

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

Fingerprinting breast cancer vs. normal mammary cells by mass spectrometric analysis of volatiles

There is increasing interest in the development of noninvasive diagnostic methods for early cancer detection, to improve the survival rate and quality of life of cancer patients. Identification of volatile metabolic compounds may provide an approach for noninvasive early diagnosis of malignant diseases. Here we analyzed the volatile metabolic signature of human breast cancer cell lines versus normal human mammary cells. Volatile compounds in the headspace of conditioned culture medium were directly fingerprinted by secondary electrospray ionization-mass spectrometry. The mass spectra were subsequently treated statistically to identify discriminating features between normal vs. cancerous cell types. We were able to classify different samples by using feature selection followed by principal component analysis (PCA). Additionally, high-resolution mass spectrometry allowed us to propose their chemical structures for some of the most discriminating molecules. We conclude that cancerous cells can release a characteristic odor whose constituents may be used as disease markers.

Cordyceps sinensis protects against liver and heart injuries in a rat model of chronic kidney disease: a metabolomic analysis

AIM

To test the hypothesis that the traditional Chinese medicine Cordyceps sinensis could improve the metabolic function of extrarenal organs to achieve its anti-chronic kidney disease (CKD) effects.

METHODS

Male SD rats were divided into CKD rats (with 5/6-nephrectomy), CKD rats treated with Cordyceps sinensis (4 mg•kg-1•d-1, po), and sham-operated rats. After an 8-week treatment, metabolites were extracted from the hearts and livers of the rats, and then subjected to (1)H-NMR-based metabolomic analysis.

RESULTS

Oxidative stress, energy metabolism, amino acid and protein metabolism and choline metabolism were considered as links between CKD and extrarenal organ dysfunction. Within the experimental period of 8 weeks, the metabolic disorders in the liver were more pronounced than in the heart, suggesting that CKD-related extrarenal organ dysfunctions occurred sequentially rather than simultaneously. Oral administration of Cordyceps sinensis exerted statistically significant rescue effects on the liver and heart by reversely regulating levels of those metabolites that are typically perturbed in CKD.

CONCLUSION

Oral administration of Cordyceps sinensis significantly attenuates the liver and heart injuries in CKD rats. The (1)H NMR-based metabolomic approach has provided a systematic view for understanding of CKD and the drug treatment, which can also be used to elucidate the mechanisms of action of other traditional Chinese medicines.

Detection of hepatocellular carcinoma in hepatitis C patients: biomarker discovery by LC-MS

Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n=37) and HCV (n=21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<0.05) and sufficiently (FC≤0.7 or FC≥1.3) showed good performance using p-values alone; the best of these PLS-DA models was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between high-risk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLS-DA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application.

Modern approach of breast cancer diagnostics

In the review have been classified literature data concerning modern instrumental, microscopic and molecular (metabolomics, proteomics, genetics and epigenetics) approaches for early breast cancer diagnostics. The analytical performance and perspectives of their application in clinical practice also have been evaluated.

Global metabolic profiling using ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry

Currently, liquid chromatography-mass spectrometry (LC-MS) is one of the most important analytical technologies for detecting hundreds of metabolites in the field of metabolomics. A recent advance in LC that has impacted metabolomics is the development of UPLC (ultra-performance liquid chromatography). In this chapter, we describe the analytical methodologies for the global metabolic profiling of serum, urine, and tissue samples using UPLC-Q-TOF (quadrupole-time-of-flight)-MS. Aqueous metabolites are extracted after adding methanol/acetonitrile/acetone and then analyzed by UPLC-MS under positive and/or negative ionization mode. With the aid of multivariate statistical analysis, separation between various groups can be observed in the score plots, and biomarkers are screened in the loading/weight/VIP (variable importance in the projection) scatterplots. Furthermore, putative markers can be identified through comparison with the authentic standards based on tandem mass spectrometry (MS/MS) fragmentation pattern and LC retention. We expect that our protocol, with modifications if necessary, can be useful in many metabolomics studies and a wide range of research areas related to small molecules and LC-MS.

Metabolomics of the tumor microenvironment in pediatric acute lymphoblastic leukemia

The tumor microenvironment is emerging as an important therapeutic target. Most studies, however, are focused on the protein components, and relatively little is known of how the microenvironmental metabolome might influence tumor survival. In this study, we examined the metabolic profiles of paired bone marrow (BM) and peripheral blood (PB) samples from 10 children with acute lymphoblastic leukemia (ALL). BM and PB samples from the same patient were collected at the time of diagnosis and after 29 days of induction therapy, at which point all patients were in remission. We employed two analytical platforms, high-resolution magnetic resonance spectroscopy and gas chromatography-mass spectrometry, to identify and quantify 102 metabolites in the BM and PB. Standard ALL therapy, which includes l-asparaginase, completely removed circulating asparagine, but not glutamine. Statistical analyses of metabolite correlations and network reconstructions showed that the untreated BM microenvironment was characterized by a significant network-level signature: a cluster of highly correlated lipids and metabolites involved in lipid metabolism (p<0.006). In contrast, the strongest correlations in the BM upon remission were observed among amino acid metabolites and derivatives (p<9.2 × 10(-10)). This study provides evidence that metabolic characterization of the cancer niche could generate new hypotheses for the development of cancer therapies.

Plasma metabolomic profiles in breast cancer patients and healthy controls: by race and tumor receptor subtypes

BACKGROUND

A few studies in the last several years have shown that metabolomics, the study of metabolites and small intermediate molecules, may help better understand the breast carcinogenesis. However, breast cancer is a heterogeneous disease with different subtypes. Additionally, there is a significant racial difference in terms of breast cancer incidence and mortality. Few, if any, metabolomics studies in breast cancer have considered race and tumor subtypes in the study design.

METHODS

We performed a global metabolomic profiling using mass spectrometry and samples from 60 breast cancer cases and 60 matched controls.

RESULTS

A total of 375 named metabolites were observed, with 117 metabolites whose levels were significantly different between African American and Caucasian American women (P < .05 and q < 0.10) and 78 that differed between breast cancer cases and healthy controls (P < .05 and q < 0.10). Most of those differentiated metabolites belong to amino acids, fatty acids, and lysolipids. In the pathway-based analysis, we found that plasma levels of many amino acids were statistically significantly lower in patients with breast cancer, especially those with triple-negative breast cancer, than healthy controls. However, plasma levels of many FAs related to β-oxidation were statistically significantly higher in patients with breast cancer than healthy controls, suggesting the possibility of altered FA β-oxidation in patients with breast cancer.

CONCLUSIONS

Because of small sample size, the clinical usage of the metabolites from this study is unclear. Further validation of those significant metabolites is warranted, especially with the consideration of racial difference.

Glycerophosphoglycerol, Beta-alanine, and pantothenic Acid as metabolic companions of glycolytic activity and cell migration in breast cancer cell lines

In cancer research, cell lines are used to explore the molecular basis of the disease as a substitute to tissue biopsies. Breast cancer in particular is a very heterogeneous type of cancer, and different subgroups of cell lines have been established according to their genomic profiles and tumor characteristics. We applied GCMS metabolite profiling to five selected breast cancer cell lines and found this heterogeneity reflected on the metabolite level as well. Metabolite profiles of MCF-7 cells belonging to the luminal gene cluster proved to be more different from those of the basal A cell line JIMT-1 and the basal B cell lines MDA-MB-231, MDA-MB-435, and MDA-MB-436 with only slight differences in the intracellular metabolite pattern. Lactate release into the cultivation medium as an indicator of glycolytic activity was correlated to the metabolite profiles and physiological characteristics of each cell line. In conclusion, pantothenic acid, beta-alanine and glycerophosphoglycerol appeared to be related to the glycolytic activity designated through high lactate release. Other physiological parameters coinciding with glycolytic activity were high glyoxalase 1 (Glo1) and lactate dehydrogenase (LDH) enzyme activity as well as cell migration as an additional important characteristic contributing to the aggressiveness of tumor cells. Metabolite profiles of the cell lines are comparatively discussed with respect to known biomarkers of cancer progression.

Low- and high-grade bladder cancer determination via human serum-based metabolomics approach

To address the shortcomings of urine cytology and cystoscopy for probing and grading urinary bladder cancer (BC), we applied (1)H nuclear magnetic resonance (NMR) spectroscopy as a surrogate method for the identification of BC. This study includes 99 serum samples comprising low-grade (LG; n = 36) and high-grade (HG; n = 31) BC as well as healthy controls (HC; n = 32). (1)H NMR-derived serum data were analyzed using orthogonal partial least-squares discriminant analysis (OPLS-DA). OPLS-DA-derived model validity was confirmed using an internal and external cross-validation. Internal validation was performed using the initial samples (n = 99) data set. External validation was performed on a new batch of suspected BC patients (n = 106) through a double-blind study. Receiver operating characteristic (ROC) curve analysis was also performed. OPLS-DA-derived serum metabolomics (six biomarkers, ROC; 0.99) were able to discriminate 95% of BC cases with 96% sensitivity and 94% specificity when compared to HC. Likewise (three biomarkers, ROC; 0.99), 98% of cases of LG were able to differentiate from HG with 97% sensitivity and 99% specificity. External validation reveals comparable results to the internal validation. (1)H NMR-based serum metabolic screening appears to be a promising and less invasive approach for probing and grading BC in contrast to the highly invasive and painful cystoscopic approach for BC detection.

1,25-dihydroxyvitamin D regulation of glucose metabolism in Harvey-ras transformed MCF10A human breast epithelial cells

This study was designed to investigate the impact of 1,25-dihydroxyvitamin D (1,25(OH)2D) on glucose metabolism during early cancer progression. Untransformed and ras-oncogene transfected (ras) MCF10A human breast epithelial cells were employed to model early breast cancer progression. 1,25(OH)2D modified the response of the ras cells to glucose restriction, suggesting 1,25(OH)2D may reduce the ras cell glucose addiction noted in cancer cells. To understand the 1,25(OH)2D regulation of glucose metabolism, following four-day 1,25(OH)2D treatment, metabolite fluxes at the cell membrane were measured by a nanoprobe biosensor, [(13)C6]glucose flux by (13)C-mass isotopomer distribution analysis of media metabolites, intracellular metabolite levels by NMR, and gene expression of related enzymes was assessed. Treatment with 1,25(OH)2D reduced glycolysis as flux of glucose to 3-phosphoglycerate was reduced by 15% (P=0.017) and 32% (P<0.003) in MCF10A and ras cells respectively. In the ras cells, 1,25(OH)2D reduced lactate dehydrogenase activity by 15% (P<0.05) with a concomitant 10% reduction in the flux of glucose to lactate (P=0.006), and reduction in the level of intracellular lactate by 55% (P=0.029). Treatment with 1,25(OH)2D reduced flux of glucose to acetyl-coA 24% (P=0.002) and 41% (P<0.001), and flux to oxaloacetate 33% (P=0.003) and 34% (P=0.027) in the MCF10A and ras cells, respectively, suggesting a reduction in tricarboxylic acid (TCA) cycle activity. The results suggest a novel mechanism involving the regulation of glucose metabolism by which 1,25(OH)2D may prevent breast cancer progression.

Global nutrition research: nutrition and breast cancer prevention as a model

The gene-environment interaction is paramount in light of the worldwide rise in incidence of chronic diseases, with cancers in the pole position. Diet is an environmental factor with potential to influence cancer onset by shaping the epigenome (i.e., the genome organization that controls the differential expression of genes). Yet, there is no consensus regarding how diet might help prevent breast cancer, the second most frequent malignancy globally. The complexity of breast cancers requires working on a global and multidisciplinary scale to further understand the relationship between breast cancer type, diet, and the epigenome. This article describes the International Breast Cancer & Nutrition collaboration as one such approach. A global endeavor brings the diversity necessary to pinpoint important diet-gene relationships. Being developed are models, detection and assessment tools, and funding and public policy frameworks necessary to advance primary prevention research for the benefit of all populations affected by breast cancer. This paradigm can be adapted to understanding diet-gene relationships for other chronic diseases.

Low-mass-ion discriminant equation: a new concept for colorectal cancer screening

Blood metabolites can be detected as low-mass ions (LMIs) by mass spectrometry (MS). These LMIs may reflect the pathological changes in metabolism that occur as part of a disease state, such as cancer. We constructed a LMI discriminant equation (LOME) to investigate whether systematic LMI profiling might be applied to cancer screening. LMI information including m/z and mass peak intensity was obtained by five independent MALDI-MS analyses, using 1,127 sera collected from healthy individuals and cancer patients with colorectal cancer (CRC), breast cancer (BRC), gastric cancer (GC) and other types of cancer. Using a two-stage principal component analysis to determine weighting factors for individual LMIs and a two-stage LMI selection procedure, we selected a total of 104 and 23 major LMIs by the LOME algorithms for separating CRC from control and rest of cancer samples, respectively. CRC LOME demonstrated excellent discriminating power in a validation set (sensitivity/specificity: 93.21%/96.47%). Furthermore, in a fecal occult blood test (FOBT) of available validation samples, the discriminating power of CRC LOME was much stronger (sensitivity/specificity: 94.79%/97.96%) than that of the FOBT (sensitivity/specificity: 50.00%/100.0%), which is the standard CRC screening tool. The robust discriminating power of the LOME scheme was reconfirmed in screens for BRC (sensitivity/specificity: 92.45%/96.57%) and GC (sensitivity/specificity: 93.18%/98.85%). Our study demonstrates that LOMEs might be powerful noninvasive diagnostic tools with high sensitivity/specificity in cancer screening. The use of LOMEs could potentially enable screening for multiple diseases (including different types of cancer) from a single sampling of LMI information.

¹H NMR-based metabolic profiling of human rectal cancer tissue

Background

Rectal cancer is one of the most prevalent tumor types. Understanding the metabolic profile of rectal cancer is important for developing therapeutic approaches and molecular diagnosis.

Methods

Here, we report a metabonomics profiling of tissue samples on a large cohort of human rectal cancer subjects (n = 127) and normal controls (n = 43) using ¹H nuclear magnetic resonance (¹H NMR) based metabonomics assay, which is a highly sensitive and non-destructive method for the biomarker identification in biological systems. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA) were applied to analyze the ¹H-NMR profiling data to identify the distinguishing metabolites of rectal cancer.

Results

Excellent separation was obtained and distinguishing metabolites were observed among the different stages of rectal cancer tissues (stage I = 35; stage II = 37; stage III = 37 and stage IV = 18) and normal controls. A total of 38 differential metabolites were identified, 16 of which were closely correlated with the stage of rectal cancer. The up-regulation of 10 metabolites, including lactate, threonine, acetate, glutathione, uracil, succinate, serine, formate, lysine and tyrosine, were detected in the cancer tissues. On the other hand, 6 metabolites, including myo-inositol, taurine, phosphocreatine, creatine, betaine and dimethylglycine were decreased in cancer tissues. These modified metabolites revealed disturbance of energy, amino acids, ketone body and choline metabolism, which may be correlated with the progression of human rectal cancer.

Conclusion

Our findings firstly identify the distinguishing metabolites in different stages of rectal cancer tissues, indicating possibility of the attribution of metabolites disturbance to the progression of rectal cancer. The altered metabolites may be as potential biomarkers, which would provide a promising molecular diagnostic approach for clinical diagnosis of human rectal cancer. The role and underlying mechanism of metabolites in rectal cancer progression are worth being further investigated.

Review of mass spectrometry-based metabolomics in cancer research

Metabolomics, the systematic investigation of all metabolites present within a biologic system, is used in biomarker development for many human diseases, including cancer. In this review, we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase, and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification, or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including less than 100 patients. Standardization is required especially concerning sample preparation and data analysis. In the second part of this review, we reconstructed a metabolic network of patients with cancer by quantitatively extracting all reports of altered metabolites: Alterations in energy metabolism, membrane, and fatty acid synthesis emerged, with tryptophan levels changed most frequently in various cancers. Metabolomics has the potential to evolve into a standard tool for future applications in epidemiology and translational cancer research, but further, large-scale studies including prospective validation are needed.

Altered glucose metabolism in Harvey-ras transformed MCF10A cells

Metabolic reprogramming that alters the utilization of glucose including the "Warburg effect" is critical in the development of a tumorigenic phenotype. However, the effects of the Harvey-ras (H-ras) oncogene on cellular energy metabolism during mammary carcinogenesis are not known. The purpose of this study was to determine the effect of H-ras transformation on glucose metabolism using the untransformed MCF10A and H-ras oncogene transfected (MCF10A-ras) human breast epithelial cells, a model for early breast cancer progression. We measured the metabolite fluxes at the cell membrane by a selective micro-biosensor, [(13)C6 ]glucose flux by (13)C-mass isotopomer distribution analysis of media metabolites, intracellular metabolite levels by NMR, and gene expression of glucose metabolism enzymes by quantitative PCR. Results from these studies indicated that MCF10A-ras cells exhibited enhanced glycolytic activity and lactate production, decreased glucose flux through the tricarboxylic acid (TCA) cycle, as well as an increase in the utilization of glucose in the pentose phosphate pathway (PPP). These results provide evidence for a role of H-ras oncogene in the metabolic reprogramming of MCF10A cells during early mammary carcinogenesis.

Targeted metabolic profiling of hepatocellular carcinoma and hepatitis C using LC-MS/MS

Hepatitis C virus (HCV) infection of the liver is a global health problem and a major risk factor for the development of hepatocellular carcinoma (HCC). Sensitive methods are needed for the improved and earlier detection of HCC, which would provide better therapy options. Metabolic profiling of the high-risk population (HCV patients) and those with HCC provides insights into the process of liver carcinogenesis and possible biomarkers for earlier cancer detection. Seventy-three blood metabolites were quantitatively profiled in HCC (n = 30) and cirrhotic HCV (n = 22) patients using a targeted approach based on LC-MS/MS. Sixteen of 73 targeted metabolites differed significantly (p < 0.05) and their levels varied up to a factor of 3.3 between HCC and HCV. Four of these 16 metabolites (methionine, 5-hydroxymethyl-2'-deoxyuridine, N2,N2-dimethylguanosine, and uric acid) that showed the lowest p values were used to develop and internally validate a classification model using partial least squares discriminant analysis. The model exhibited high classification accuracy for distinguishing the two groups with sensitivity, specificity, and area under the receiver operating characteristic curve of 97%, 95%, and 0.98, respectively. A number of perturbed metabolic pathways, including amino acid, purine, and nucleotide metabolism, were identified based on the 16 biomarker candidates. These results provide a promising methodology to distinguish cirrhotic HCV patients, who are at high risk to develop HCC, from those who have already progressed to HCC. The results also provide insights into the altered metabolism between HCC and HCV.

Biomarker Discovery and Translation in Metabolomics

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

Metabolome-wide association study of neovascular age-related macular degeneration

PURPOSE

To determine if plasma metabolic profiles can detect differences between patients with neovascular age-related macular degeneration (NVAMD) and similarly-aged controls.

METHODS

Metabolomic analysis using liquid chromatography with Fourier-transform mass spectrometry (LC-FTMS) was performed on plasma samples from 26 NVAMD patients and 19 controls. Data were collected from mass/charge ratio (m/z) 85 to 850 on a Thermo LTQ-FT mass spectrometer, and metabolic features were extracted using an adaptive processing software package. Both non-transformed and log2 transformed data were corrected using Benjamini and Hochberg False Discovery Rate (FDR) to account for multiple testing. Orthogonal Partial Least Squares-Discriminant Analysis was performed to determine metabolic features that distinguished NVAMD patients from controls. Individual m/z features were matched to the Kyoto Encyclopedia of Genes and Genomes database and the Metlin metabolomics database, and metabolic pathways associated with NVAMD were identified using MetScape.

RESULTS

Of the 1680 total m/z features detected by LC-FTMS, 94 unique m/z features were significantly different between NVAMD patients and controls using FDR (q = 0.05). A comparison of these features to those found with log2 transformed data (n = 132, q = 0.2) revealed 40 features in common, reaffirming the involvement of certain metabolites. Such metabolites included di- and tripeptides, covalently modified amino acids, bile acids, and vitamin D-related metabolites. Correlation analysis revealed associations among certain significant features, and pathway analysis demonstrated broader changes in tyrosine metabolism, sulfur amino acid metabolism, and amino acids related to urea metabolism.

CONCLUSIONS

These data suggest that metabolomic analysis can identify a panel of individual metabolites that differ between NVAMD cases and controls. Pathway analysis can assess the involvement of certain metabolic pathways, such as tyrosine and urea metabolism, and can provide further insight into the pathophysiology of AMD.

Measuring the exposome: a powerful basis for evaluating environmental exposures and cancer risk

Advances in laboratory sciences offer much in the challenge to unravel the complex etiology of cancer and to therefore provide an evidence-base for prevention. One area where improved measurements are particularly important to epidemiology is exposure assessment; this requirement has been highlighted through the concept of the exposome. In addition, the ability to observe genetic and epigenetic alterations in individuals exposed to putative risk factors also affords an opportunity to elucidate underlying mechanisms of carcinogenesis, which in turn may allow earlier detection and more refined molecular classification of disease. In this context the application of omics technologies to large population-based studies and their associated biobanks raise exciting new avenues of research. This review considers the areas of genomics, transcriptomics, epigenomics and metabolomics and the evidence to date that people exposed to well-defined factors (for example, tobacco, diet, occupational exposures, environmental pollutants) have specific omics profiles. Although in their early stages of development these approaches show promising evidence of distinct exposure-derived biological effects and indicate molecular pathways that may be particularly relevant to the carcinogenic process subsequent to environmental and lifestyle exposures. Such an interdisciplinary approach is vital if the full benefits of advances in laboratory sciences and investments in large-scale prospective cohort studies are to be realized in relation to cancer prevention.

Tissue metabolite profiling identifies differentiating and prognostic biomarkers for prostate carcinoma

Metabolomic research offers a deeper insight into biochemical changes in cancer metabolism and is a promising tool for identifying novel biomarkers. We aimed to evaluate the diagnostic and prognostic potential of metabolites in prostate cancer (PCa) tissue after radical prostatectomy. In matched malignant and nonmalignant prostatectomy samples from 95 PCa patients, aminoadipic acid, cerebronic acid, gluconic acid, glycerophosphoethanolamine, 2-hydroxybehenic acid, isopentenyl pyrophosphate, maltotriose, 7-methylguanine and tricosanoic acid were determined within a global metabolite profiling study using gas chromatography/liquid chromatography-mass spectrometry. The data were related to clinicopathological variables like prostate volume, tumor stage, Gleason score, preoperative prostate-specific antigen and disease recurrence in the follow-up. All nine metabolites showed higher concentrations in malignant than in nonmalignant samples except for gluconic acid and maltotriose, which had lower levels in tumors. Receiver -operating characteristics analysis demonstrated a significant discrimination for all metabolites between malignant and nonmalignant tissue with a maximal area under the curve of 0.86 for tricosanoic acid, whereas no correlation was observed between the metabolite levels and the Gleason score or tumor stage except for gluconic acid. Univariate Cox regression and Kaplan-Meier analyses showed that levels of aminoadipic acid, gluconic acid and maltotriose were associated with the biochemical tumor recurrence (prostate-specific antigen > 0.2 ng/mL). In multivariate Cox regression analyses, aminoadipic acid together with tumor stage and Gleason score remained in a model as independent marker for prediction of biochemical recurrence. This study proved that metabolites in PCa tissue can be used, in combination with traditional clinicopathological factors, as promising diagnostic and prognostic tools.

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.

Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Although the molecular classification and prognostic assessment of breast tumors based on gene expression profiling is well established, a number of proteomic studies that propose potential breast cancer biomarkers has not yet led to any new diagnostic, prognostic or predictive test in wide clinical use. This review examines the current status of breast cancer biomarkers, discusses sample types (including plasma, tumor tissue, nipple aspirate and ductal lavage, as well as cell culture models) and different electrophoretic and mass spectrometry methods that have been widely used for the discovery of proteomic biomarkers in breast cancer, and also considers several approaches to biomarker validation. The pathway leading from the initial proteomic discovery and validation process to translation into a clinically useful test is also discussed.

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.

Metabolomics in noninvasive breast cancer

Breast cancer remains the most leading cause of death among women worldwide. Common methods for diagnosis and surveillance include mammography, histopathology and blood tests. The major drawback of mammography is the high rate of false reports, aside from the risk from repeated exposure to harmful ionizing radiations; histopathology is time consuming and often prone to subjective interpretations; blood-based tests are attractive, but lack the sensitivity and specificity. Obviously, more sensitive biomarkers for early detection and molecular targets for better treating breast cancer are urgently needed. Fortunately, molecular level 'omics' diagnosis is becoming increasingly popular; metabolomics, diagnosis based on 'metabolic fingerprinting' may provide clinically useful biomarkers applied toward identifying metabolic alterations and has introduced new insights into the pathology of breast cancer. By applying advanced analytical and statistical tools, metabolomics involves the comprehensive profiling of the full complement of low molecular weight compounds in a biological system and could classify the basis of tumor biology of breast cancer, to identify new prognostic and predictive markers and discover new targets for future therapeutic interventions. This advanced bioanalytic methods may now open new avenues for diagnostics in cancer via discovery of biomarkers. In this review we take a closer look at the metabolomics used within the field of breast cancer diagnosis. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples.

Biomarkers in the diagnosis of primary and recurrent breast cancer

Despite the widespread use of mammography for breast cancer screening, breast cancer remains the most common cause of cancer-related mortality among women worldwide. The identification of biomarkers that identify cancers when they are small, localized and most treatable is an important aim of current breast cancer research. Biomarkers need to be sensitive, specific, reproducible and easily collected from patients from readily accessible tissue or body fluids. While conventional biomarker research has focused on soluble proteins, cell markers, proteomics and DNA methylation, much progress has also been made in the field of immunobiomarkers and multiparameter gene arrays. Currently, no one biomarker has demonstrated sufficient sensitivity and reproducibility for independent clinical and commercial use. This review summarizes the current state of breast cancer biomarker research and anticipated future directions.

Modifying metabolically sensitive histone marks by inhibiting glutamine metabolism affects gene expression and alters cancer cell phenotype

The interplay of metabolism and epigenetic regulatory mechanisms has become a focal point for a better understanding of cancer development and progression. In this study, we have acquired data supporting previous observations that demonstrate glutamine metabolism affects histone modifications in human breast cancer cell lines. Treatment of non-invasive epithelial (T-47D and MDA-MB-361) and invasive mesenchymal (MDA-MB-231 and Hs-578T) breast cancer cell lines with the glutaminase inhibitor, Compound 968, resulted in cytotoxicity in all cell lines, with the greatest effect being observed in MDA-MB-231 breast cancer cells. Compound 968-treatment induced significant downregulation of 20 critical cancer-related genes, the majority of which are anti-apoptotic and/or promote metastasis, including AKT, BCL2, BCL2L1, CCND1, CDKN3, ERBB2, ETS1, E2F1, JUN, KITLG, MYB, and MYC. Histone H3K4me3, a mark of transcriptional activation, was reduced at the promoters of all but one of these critical cancer genes. The decrease in histone H3K4me3 at global and gene-specific levels correlated with reduced expression of SETD1 and ASH2L, genes encoding the histone H3K4 methyltransferase complex. Further, the expression of other epigenetic regulatory genes, known to be downregulated during apoptosis (e.g., DNMT1, DNMT3B, SETD1 and SIRT1), was also downregulated by Compound 968. These changes in gene expression and histone modifications were accompanied by the activation of apoptosis, and decreased invasiveness and resistance of MDA-MB-231 cells to chemotherapeutic drug doxorubicin. The results of this study provide evidence to a link between cytotoxicity caused by inhibiting glutamine metabolism with alterations of the epigenome of breast cancer cells and suggest that modification of intracellular metabolism may enhance the efficiency of epigenetic therapy.

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.

Differentiating hepatocellular carcinoma from hepatitis C using metabolite profiling

Hepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC (n=40) and HCV (n=22) was performed by ¹H nuclear magnetic resonance spectroscopy. Multivariate statistical analysis showed a distinct separation of the two patient cohorts, indicating a distinct metabolic difference between HCC and HCV patient groups based on signals from lipids and other individual metabolites. Univariate analysis showed that three metabolites (choline, valine and creatinine) were significantly altered in HCC. A PLS-DA model based on these three metabolites showed a sensitivity of 80%, specificity of 71% and an area under the receiver operating curve of 0.83, outperforming the clinical marker alpha-fetoprotein (AFP). The robustness of the model was tested using Monte-Carlo cross validation (MCCV). This study showed that metabolite profiling could provide an alternative approach for HCC screening in HCV patients, many of whom have high risk for developing liver cancer.

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.