Serum Molecular Signatures of Weight Change during Early Breast Cancer Chemotherapy

Comparison of phenomics and cfDNA in a large breast screening population: the Breast Screening and Monitoring Study (BSMS)

To assess their roles in breast cancer diagnostics, we aimed to compare plasma cell-free DNA (cfDNA) levels with the circulating metabolome in a large breast screening cohort of women recalled for mammography, including healthy women and women with mammographically detected breast diseases, ductal carcinoma in situ and invasive breast cancer: the Breast Screening and Monitoring Study (BSMS). In 999 women, plasma was analyzed by nuclear magnetic resonance (NMR) and Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) and then processed to isolate and quantify total cfDNA. NMR and UPLC-MS results were compared with data for 186 healthy women derived from the AIRWAVE cohort. Results showed no significant differences between groups for all metabolites, whereas invasive cancers had significantly higher plasma cfDNA levels than all other groups. When stratified the supervised OPLS-DA analysis and total cfDNA concentration showed high discrimination accuracy between invasive cancers and the disease/medication-free subjects. Furthermore, comparison of OPLS-DA data for invasive breast cancers with the AIRWAVE cohort showed similar discrimination between breast cancers and healthy controls. This is the first report of agreement between metabolomics and plasma cfDNA levels for discriminating breast cancer from healthy subjects in a true screening population. It also emphasizes the importance of sample standardization. Follow on studies will involve analysis of candidate features in a larger validation series as well as comparing results with serial plasma samples taken at the next routine screening mammography appointment. The findings here help establish the role of plasma analysis in the diagnosis of breast cancer in a large real-world cohort.

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).

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Metabolomics: a promising diagnostic and therapeutic implement for breast cancer

Breast cancer (BC) is the most commonly diagnosed cancer among women and the leading cause of cancer death. Despite the advent of numerous diagnosis and treatment methods in recent years, this heterogeneous disease still presents great challenges in early diagnosis, curative treatments and prognosis monitoring. Thus, finding promising early diagnostic biomarkers and therapeutic targets and approaches is meaningful. Metabolomics, which focuses on the analysis of metabolites that change during metabolism, can reveal even a subtle abnormal change in an individual. In recent decades, the exploration of cancer-related metabolomics has increased. Metabolites can be promising biomarkers for the screening, response evaluation and prognosis of BC. In this review, we summarized the workflow of metabolomics, described metabolite signatures based on molecular subtype as well as reclassification and then discussed the application of metabolomics in the early diagnosis, monitoring and prognosis of BC to offer new insights for clinicians in breast cancer diagnosis and treatment.

Serum Metabolomics Study of Nonsmoking Female Patients with Non-Small Cell Lung Cancer Using Gas Chromatography-Mass Spectrometry

The incidence of nonsmoking female patients with non-small cell lung cancer (NSCLC) has increased in recent decades; however, the pathogenesis of patients is unclear, and early diagnosis biomarkers are in urgent need. In this study, 136 nonsmoking female subjects (65 patients with NSCLC, 6 patients with benign lung tumors, and 65 healthy controls) were enrolled, and their metabolic profiling was investigated by using pseudotargeted gas chromatography-mass spectrometry. A total of 56 annotated metabolites were found and verified to be significantly different in nonsmoking females with NSCLC compared with the control. The metabolic profiling was featured by disturbed energy metabolism, amino acid metabolism, oxidative stress, lipid metabolism, and so on. Cysteine, serine, and 1-monooleoylglycerol were defined as the biomarker panel for the diagnosis of NSCLC patients. 98.5 and 91.4% of subjects were correctly distinguished in the discovery and validation sets, respectively. The biomarker panel was also useful for the diagnosis of in situ malignancy patients, with an accuracy of 97.7 and 97.8% in the discovery and validation sets, respectively. The study provides a biomarker panel for the auxiliary diagnosis of nonsmoking females with NSCLC.

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.

Metabolite and lipoprotein responses and prediction of weight gain during breast cancer treatment

Background

Breast cancer treatment has metabolic side effects, potentially affecting risk of cardiovascular disease (CVD) and recurrence. We aimed to compare alterations in serum metabolites and lipoproteins during treatment between recipients and non-recipients of chemotherapy, and describe metabolite profiles associated with treatment-related weight gain.

Methods

This pilot study includes 60 stage I/II breast cancer patients who underwent surgery and were treated according to national guidelines. Serum sampled pre-surgery and after 6 and 12 months was analysed by MR spectroscopy and mass spectrometry. In all, 170 metabolites and 105 lipoprotein subfractions were quantified.

Results

The metabolite and lipoprotein profiles of chemotherapy recipients and non-recipients changed significantly 6 months after surgery (p < 0.001). Kynurenine, the lipid signal at 1.55–1.60 ppm, ADMA, 2 phosphatidylcholines (PC aa C38:3, PC ae C42:1), alpha-aminoadipic acid, hexoses and sphingolipids were increased in chemotherapy recipients after 6 months. VLDL and small dense LDL increased after 6 months, while HDL decreased, with triglyceride enrichment in HDL and LDL. At baseline, weight gainers had less acylcarnitines, phosphatidylcholines, lyso-phosphatidylcholines and sphingolipids, and showed an inflammatory lipid profile.

Conclusion

Chemotherapy recipients exhibit metabolic changes associated with inflammation, altered immune response and increased risk of CVD. Altered lipid metabolism may predispose for treatment-related weight gain.

Pharmacometabolomics reveals a role for histidine, phenylalanine, and threonine in the development of paclitaxel-induced peripheral neuropathy

PURPOSE

Approximately 25% of breast cancer patients experience treatment delays or discontinuation due to paclitaxel-induced peripheral neuropathy (PN). Currently, there are no predictive biomarkers of PN. Pharmacometabolomics is an informative tool for biomarker discovery of drug toxicity. We conducted a secondary whole blood pharmacometabolomics analysis to assess the association between pretreatment metabolome, early treatment-induced metabolic changes, and the development of PN.

METHODS

Whole blood samples were collected pre-treatment (BL), just before the end of the first paclitaxel infusion (EOI), and 24 h after the first infusion (24H) from sixty patients with breast cancer receiving (80 mg/m²) weekly treatment. Neuropathy was assessed at BL and prior to each infusion using the sensory subscale (CIPN8) of the EORTC CIPN20 questionnaire. Blood metabolites were quantified from 1-D-¹H-nuclear magnetic resonance spectra using Chenomx® software. Metabolite concentrations were normalized in preparation for Pearson correlation and one-way repeated measures ANOVA with multiple comparisons corrected by false discovery rate (FDR).

RESULTS

Pretreatment histidine, phenylalanine, and threonine concentrations were inversely associated with maximum change in CIPN8 (ΔCIPN8) (p < 0.02; FDR ≤ 25%). Paclitaxel caused a significant change in concentrations of 2-hydroxybutyrate, 3-hydroxybutyrate, pyruvate, o-acetylcarnitine, and several amino acids from BL to EOI and/or 24H (p < 0.05; FDR ≤ 25%), although these changes were not associated with ΔCIPN8.

CONCLUSIONS

Whole blood metabolomics is a feasible approach to identify potential biomarker candidates of paclitaxel-induced PN. The findings suggest that pretreatment concentrations of histidine, phenylalanine, and threonine may be predictive of the severity of future PN and paclitaxel-induced metabolic changes may be related to disruption of energy homeostasis.

NMR-based metabolomics of biofluids in cancer

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

Prediction of Clinical Endpoints in Breast Cancer Using NMR Metabolic Profiles

Metabolic profiles reflect biological conditions as a result of biochemical changes within a living system. It is therefore possible to associate metabolic signatures with clinical endpoints of diseases, such as breast cancer. Nuclear magnetic resonance (NMR) spectroscopy is one of the most common techniques used for metabolic profiling, and produces high dimensional datasets from which meaningful biological information can be extracted. Here, we present an overview of data analysis techniques used to achieve this, describing key steps in the procedure. Moreover, examples of clinical endpoints of interest are provided. Although these are specific for breast cancer, the procedures for the analysis of NMR spectra as described here are applicable to any type of cancer and to other diseases.

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare.

Association between weight gain during adjuvant chemotherapy for early-stage breast cancer and survival outcomes

Obese and overweight women have an increased risk of breast cancer and worse outcomes at the time of diagnosis. Women tend to gain weight after breast cancer diagnosis and during chemotherapy for early‐stage disease, which may in turn increase risk for worse outcomes. We examined if weight gained during adjuvant chemotherapy was associated with worse survival outcomes. We queried our database for data on patients who received adjuvant third‐generation chemotherapy for early‐stage breast cancer. Univariate and multivariate analyses by Cox regression were performed for survival outcomes across three categories according to BMI variation from start to end of chemotherapy: >0.5 kg/m² loss or gain and stable BMI (±0.5 kg/m²). We included 1998 patients in this study. Women over 50 years old and postmenopausal were more likely to lose weight during adjuvant chemotherapy, whereas women under 30 years old gained more weight (P < 0.001). At 1 year postchemotherapy, patients tended to return to their original weight (ρ = −0.3, P < 0.001). On multivariate analysis, BMI increase of >0.5 kg/m² compared to maintaining BMI was marginally associated with increased locoregional recurrence risk (HR: 2.53; 95% CI, 1.18–5.45; P = 0.017), adjusting for grade, stage, and radiation delivery. Weight variation during adjuvant chemotherapy for early‐stage breast cancer may occur as both weight gain and weight loss in a balanced manner. Furthermore, this variation seems to be transient in nature and does not appear to significantly influence recurrence rates and overall survival.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

Screening and verifying endometrial carcinoma diagnostic biomarkers based on a urine metabolomic profiling study using UPLC-Q-TOF/MS

BACKGROUND

Endometrial carcinoma (EOC) is a gynecological disease with one of the highest worldwide incidences. Due to the lack of typical clinical symptoms and limited sensitive screening methods used to diagnose endometrial carcinoma, the disease is easily neglected before patients are aware of its presence. Therefore, EOC results in serious impacts on women's lives and health. We screened diagnostic biomarkers of EOC with a noninvasive method that compared healthy individuals and endometrial hyperplasia (EOH) patients.

METHODS

The morning urine of 25 healthy individuals, 25 patients with EOC and 10 patients with EOH were analyzed using an ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) platform. Metabolomics data were used to screen the different metabolites according to principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) analyses. Furthermore, the screened biomarkers of the newly diagnosed EOC and EOH candidates and healthy individuals were verified using the predictive model of the support vector machine (SVM) to obtain EOC diagnostic biomarkers.

RESULTS

An EOC diagnostic biomarker group was found according to the metabolomics method. Five diagnostic biomarkers, including porphobilinogen, acetylcysteine, N-acetylserine, urocanic acid and isobutyrylglycine, were significantly changed in the EOC patients. Among them, porphobilinogen and acetylcysteine were significantly down-regulated, while N-acetylserine, urocanic acid and isobutyrylglycine were significantly up-regulated.

CONCLUSIONS

Disturbances in these biomarkers have negative impacts on the body's metabolic functioning. The EOC diagnostic biomarker group can provide a clinical reference for diagnosing EOC and insight into the diagnosis of other diseases in the clinic.

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.

"Omics" in pharmaceutical research: overview, applications, challenges, and future perspectives

In the post-genomic era, biological studies are characterized by the rapid development and wide application of a series of "omics" technologies, including genomics, proteomics, metabolomics, transcriptomics, lipidomics, cytomics, metallomics, ionomics, interactomics, and phenomics. These "omics" are often based on global analyses of biological samples using high through-put analytical approaches and bioinformatics and may provide new insights into biological phenomena. In this paper, the development and advances in these omics made in the past decades are reviewed, especially genomics, transcriptomics, proteomics and metabolomics; the applications of omics technologies in pharmaceutical research are then summarized in the fields of drug target discovery, toxicity evaluation, personalized medicine, and traditional Chinese medicine; and finally, the limitations of omics are discussed, along with the future challenges associated with the multi-omics data processing, dynamics omics analysis, and analytical approaches, as well as amenable solutions and future prospects.

Pharmacometabonomics in humans: a new tool for personalized medicine

Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. One reason for this is that many diseases, and the way in which the patients respond to drug treatments, have both genetic and environmental elements. Pure genomics is almost blind to the environmental elements. A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area.

Multi-omic signature of body weight change: results from a population-based cohort study

BACKGROUND

Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study.

METHODS

We used data from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits.

RESULTS

Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 × 10(-4) to 1.2 × 10(-24)). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules.

CONCLUSIONS

Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino acid metabolism, insulin sensitivity, mitochondrial function as well as blood cell development and function.

Adjuvant chemotherapy may contribute to an increased risk for metabolic syndrome in patients with breast cancer

PURPOSE

Cytotoxic treatment may cause weight gain and important alterations in the metabolic status of breast cancer (BC) patients. The aim of this study was to investigate the changes in metabolic and anthropometric parameters of patients with BC who received adjuvant chemotherapy.

METHODS

All consecutive women treated with adjuvant TAC (docetaxel 75 mg/m(2), doxorubicine 50 mg/m(2), cyclophosphamide 500 mg/m(2)) chemotherapy for node-positive breast carcinoma at our Institution between 2008 and 2010 were included.

RESULTS

Among 104 patients, 84 of them were stage II and 20 of them were stage III. When we compared the measurements between 1(st) and 6(th) adjuvant chemotherapy, we observed statistically significant increases in weight and serum triglyceride levels, and decreases in high density lipoprotein, apolipoprotein A-1, transferrin, albumin and prealbumin levels. An elevation of follicle stimulating hormone, luteinizing hormone together with the decrease of estradiol was detected. Waist-to-hip ratio has also increased significantly. In subgroup analyses, we observed dramatic changes in body mass index in pre-menopausal women whereas no significant change was seen in the post-menopausal group.

CONCLUSIONS

Adjuvant chemotherapy may contribute to an increased risk for metabolic syndrome in patients with BC and these changes are more profound in pre-menopausal patients.

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data.

Metabolomic fingerprinting: challenges and opportunities

Systems biology has primarily focused on studying genomics, transcriptomics, and proteomics and their dynamic interactions. These, however, represent only the potential for a biological outcome since the ultimate phenotype at the level of the eventually produced metabolites is not taken into consideration. The emerging field of metabolomics provides complementary guidance toward an integrated approach to this problem: It allows global profiling of the metabolites of a cell, tissue, or host and presents information on the actual end points of a response. A wide range of data collection methods are currently used and allow the extraction of global or tissue-specific metabolic profiles. The great amount and complexity of data that are collected require multivariate analysis techniques, but the increasing amount of work in this field has made easy-to-use analysis programs readily available. Metabolomics has already shown great potential in drug toxicity studies, disease modeling, and diagnostics and may be integrated with genomic and proteomic data in the future to provide in-depth understanding of systems, pathways, and their functionally dynamic interactions. In this review we discuss the current state of the art of metabolomics, its applications, and future potential.

Metabolomic characterization of nipple aspirate fluid by (1)H NMR spectroscopy and GC-MS

Nipple aspirate fluid (NAF) is a noninvasively obtained biofluid from the duct openings of the breast. NAF components are constantly secreted, metabolized, and reabsorbed by the epithelial lining of the lactiferous ducts of the breast. NAF has been studied as a potential breast tissue surrogate for the discovery of novel breast cancer risk, early detection, and treatment response biomarkers. We report the first unsupervised metabolite characterization of nipple aspirate fluid using NMR and GC-MS using convenience samples previously collected from four premenopausal and four postmenopausal women. A total of 38 metabolites were identified using the two analytical techniques, including amino acids, organic acids, fatty acids, and carbohydrates. Analytical reproducibility of metabolites in NAF by GC-MS was high across different extraction and analysis days. Overall, 31 metabolites had a coefficient of variation below 20%. By GC-MS, there were eight metabolites unique to NAF, 19 unique to plasma, and 24 shared metabolites. Correlative analysis of shared metabolites between matched NAF and plasma samples from pre- and postmenopausal women shows almost no correlations, with the exception being lactic acid, which was significantly negatively correlated (R(2) = 0.57; P = 0.03). These results suggest that NAF is metabolically distinct from plasma and that the application of metabolomic strategies may be useful for future studies investigating breast cancer risk and intervention response biomarkers.

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.

Pharmacometabonomics and personalized medicine

BACKGROUND

Pharmacometabonomics is a new branch of science, first described in 2006 and defined as 'the prediction of the effects of a drug on the basis of a mathematical model of pre-dose metabolite profiles'. Pharmacometabonomics has been used to predict drug metabolism, pharmacokinetics (PK), drug safety and drug efficacy in both animals and humans and is complementary to both pharmacogenomics (PGx) and pharmacoproteomics.

METHODS

A literature review using the search terms pharmacometabonomics, pharmacometabolomics, pharmaco-metabonomics, pharmaco-metabolomics and the singular form of all those terms was conducted in October 2012 using PubMed and Web of Science. The review was updated until mid April 2013.

RESULTS

Since the original description of pharmacometabonomics in 2006, 21 original publications and eight reviews have emerged, covering a broad range of applications from the prediction of PK to the prediction of drug metabolism, efficacy and safety in humans and animals.

CONCLUSIONS

Pharmacometabonomics promises to be an important new approach to the delivery of personalized medicine to improve both drug efficacy and safety for patients in the future. Pharmacometabonomics is particularly powerful as it is sensitive to both genetic and environmental factors such as diet, drug intake and most importantly, a person's microbiome. PGx is now over 50 years old and although it has not achieved as much as some hoped, it is starting to have important applications in personalized medicine. We predict that pharmacometabonomics will be equally important in the next few decades and will be both valuable in its own right and complementary to pharmacoproteomics and PGx.

Changes in plasma ghrelin and serum leptin levels after Cisplatin-based transcatheter arterial infusion chemotherapy for hepatocellular carcinoma

Background and Objective. Cisplatin-based chemotherapy is widely recognized to cause severe gastrointestinal disorders like nausea, vomiting, and appetite loss. The aim of this study was to assess whether cisplatin-based transcatheter arterial infusion (TAI) chemotherapy reduces plasma ghrelin levels and food intake in hepatocellular carcinoma (HCC) patients. Methods. Seventeen patients with HCC who underwent cisplatin-based TAI chemotherapy (80-100 mg/body) were enrolled in this study. Changes in peptide hormones, including ghrelin and leptin, as well as cytokines, were measured before and after chemotherapy. Appetite was evaluated by visual analog scale (VAS) and food intake was scored by eleven stages (0-10). Results. Appetite and food intake were significantly decreased after chemotherapy (P < 0.05). Plasma acylated ghrelin levels before therapy and at day 1, day 7, and day 14 after chemotherapy were 10.4 ± 7.2, 4.7 ± 4.7, 11.7 ± 8.9, and 9.3 ± 6.6 fmol/mL, respectively. The level on day 1 was decreased significantly (P < 0.05). In contrast, the levels of leptin, granulocyte colony-stimulating factor (G-CSF), and monocyte chemotactic protein-1 (MCP-1) on day 1 were increased significantly (P < 0.05). Conclusions. TAI for HCC reduced plasma acylated ghrelin levels, appetite, and food intake significantly. In addition, it increased serum leptin levels.

A metabolic phenotyping approach to understanding relationships between metabolic syndrome and breast tumour responses to chemotherapy

PURPOSE

Breast cancer is associated with adverse outcomes in patients with the metabolic syndrome phenotype. To study this further, we examined the relationship between serum metabolite levels and the components of metabolic syndrome with treatment outcomes in breast cancer.

METHODS

A total of 88 women with measurable breast cancer were studied; their serum metabolites as assessed by (1)H nuclear magnetic resonance spectroscopy, blood pressure, lipids, glucose, body mass index and waist circumference were recorded and correlated with treatment response.

RESULTS

We identified metabolic syndrome in approximately half of our cohort (42 patients) and observed a significant trend (P = 0.03) of increased incidence of metabolic syndrome in partial response (33.3%), stable disease (42.9%) and progressive disease groups (66.1%). High blood sugar predicted a poor response (P < 0.001). Logistic regression of metabonomic data demonstrated that high lactate (P = 0.03) and low alanine (P = 0.01) combined with high glucose (P = 0.01) were associated with disease progression.

CONCLUSIONS

Metabolic syndrome is commonly observed in metastatic breast cancer and these patients have poorer outcomes. These data, which support our previous findings, suggest that high blood glucose as part of metabolic syndrome is associated with a poor response in breast cancer. They also validate new therapeutic approaches that focus on metabolism.

Importance of metabolic changes induced by chemotherapy on prognosis of early-stage breast cancer patients: a review of potential mechanisms

Weight variation has been reported as a side effect of chemotherapy treatment in early breast cancer patients and has been identified as a factor of poor prognosis. Causes of weight variation during chemotherapy and mechanisms involved in the poor prognosis have been little studied. Here is reviewed the current knowledge about the main causes and mechanisms involved in body weight change. Special emphasis is placed on factors associated with weight variation which could potentially be involved in the risk of relapse in breast cancer survivors. In recent decades, some studies have investigated the causes of weight variation by studying energy balance of breast cancer patients during chemotherapy. Weight gain or loss may be the consequence of energy imbalance through different factors linked with chemotherapy, such as poor treatment tolerance, decreased muscle mass and function, or hormonal alterations. This results in body composition modifications in favour of fat gain and/or lean body mass loss. Increased adipose tissue, especially in the abdominal region, could induce metabolic disturbances such as insulin resistance, through various pathways involving adipokines. These molecules have growth properties and could therefore play a role in cancer relapse. Understanding such mechanisms is key to developing preventive strategies for improving the prognosis of early-stage breast cancer patients.

Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma

In this work, the variations in the metabolic profile of blood plasma from lung cancer patients and healthy controls were investigated through NMR-based metabonomics, to assess the potential of this approach for lung cancer screening and diagnosis. PLS-DA modeling of CPMG spectra from plasma, subjected to Monte Carlo Cross Validation, allowed cancer patients to be discriminated from controls with sensitivity and specificity levels of about 90%. Relatively lower HDL and higher VLDL + LDL in the patients' plasma, together with increased lactate and pyruvate and decreased levels of glucose, citrate, formate, acetate, several amino acids (alanine, glutamine, histidine, tyrosine, valine), and methanol, could be detected. These changes were found to be present at initial disease stages and could be related to known cancer biochemical hallmarks, such as enhanced glycolysis, glutaminolysis, and gluconeogenesis, together with suppressed Krebs cycle and reduced lipid catabolism, thus supporting the hypothesis of a systemic metabolic signature for lung cancer. Despite the possible confounding influence of age, smoking habits, and other uncontrolled factors, these results indicate that NMR-based metabonomics of blood plasma can be useful as a screening tool to identify suspicious cases for subsequent, more specific radiological tests, thus contributing to improved disease management.

Bladder cancer determination via two urinary metabolites: a biomarker pattern approach

The purpose of this study was to use metabonomic profiling to identify a potential specific biomarker pattern in urine as a noninvasive bladder cancer (BC) detection strategy. A liquid chromatography-mass spectrometry based method, which utilized both reversed phase liquid chromatography and hydrophilic interaction chromatography separations, was performed, followed by multivariate data analysis to discriminate the global urine profiles of 27 BC patients and 32 healthy controls. Data from both columns were combined, and this combination proved to be effective and reliable for partial least squares-discriminant analysis. Following a critical selection criterion, several metabolites showing significant differences in expression levels were detected. Receiver operating characteristic analysis was used for the evaluation of potential biomarkers. Carnitine C9:1 and component I, were combined as a biomarker pattern, with a sensitivity and specificity up to 92.6% and 96.9%, respectively, for all patients and 90.5% and 96.9%, respectively for low-grade BC patients. Metabolic pathways of component I and carnitine C9:1 are discussed. These results indicate that metabonomics is a practicable tool for BC diagnosis given its high efficacy and economization. The combined biomarker pattern showed better performance than single metabolite in discriminating bladder cancer patients, especially low-grade BC patients, from healthy controls.

Weight gain in breast cancer survivors: prevalence, pattern and health consequences

Weight gain is a common and persistent problem for many breast cancer survivors and is associated with adverse health consequences. A comprehensive review of the English language literature was conducted to investigate the frequency, magnitude and pattern of weight gain among breast cancer survivors, to identify factors that are associated with these changes and to review the clinical significance of weight gain on disease free survival and overall health. While there appears to be a general trend toward a reduction in the magnitude of weight gain in recent years, as many as 50-96% of women experience weight gain during treatment and many, including some women who remain weight stable during treatment, report progressive weight gain in the months and years after diagnosis. Weight gain is more common in women receiving adjuvant chemotherapy, especially for women receiving longer duration treatments and seems to be especially pronounced in premenopausal women. With or without weight gain, unfavourable changes in body composition including fat gain and loss of lean tissue are prevalent. This unique pattern of weight gain and change in body composition is distressing for most women, poses significant risk for the development of co-morbid conditions and may impact on long term disease-free survival.

Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

Resistance to chemotherapy drugs in cancer sufferers is very common. Using a panel of 59 cell lines obtained from different types of cancer we study the links between the genes and metabolites measured in these cells and the resistance the cells show to common cancer drugs containing platinum. In order to combine the information given by the genes and metabolites we introduce a new pathway-based approach, which allows us to explore synergy between the different types of data. We then extend the procedure to look at a wider panel of drugs and show that the pathways we found were associated with platinum are not just the pathways which are frequently selected for a large number of drugs. Given the increasing use of multiple sets of measurements (genes, metabolites, proteins etc.) in biological studies, we demonstrate a powerful, yet straightforward method for dealing with the resulting large datasets and integrating their knowledge. We believe that this work could contribute to developing a personalised medicine approach to treating tumours, where the genetic and metabolic changes in the tumour are measured and then used for prediction of the optimal treatment regime.

Pharmacometabonomic profiling as a predictor of toxicity in patients with inoperable colorectal cancer treated with capecitabine

PURPOSE

Endogenous metabolic profiles have been shown to predict the fate and toxicity of drugs such as acetaminophen in healthy individuals. However, the clinical utility of metabonomics in oncology remains to be defined. We aimed to evaluate the effect of pretreatment serum metabolic profiles generated by (1)H NMR spectroscopy on toxicity in patients with inoperable colorectal cancer receiving single agent capecitabine.

EXPERIMENTAL DESIGN

Serum was collected from 54 patients with a diagnosis of locally advanced or metastatic colorectal cancer prior to treatment with single agent capecitabine. (1)H NMR spectroscopy was used to generate metabolic profile data for each patient. Toxicities were graded according to National Cancer Institute Common Toxicity Criteria version 2.0.

RESULTS

Higher levels of low-density lipoprotein-derived lipids, including polyunsaturated fatty acids and choline phospholipids predicted for higher grade toxicity over the treatment period. Statistical analyses revealed a "pharmacometabonomic" lipid profile that correlated with severity of toxicity.

CONCLUSIONS

This study suggests that metabolic profiles can delineate subpopulations susceptible to adverse events and have a potential role in the assessment of treatment viability for cancer patients prior to commencing chemotherapy.

Pharmacometabonomics as an effector for personalized medicine

This article introduces and reviews the concept of pharmacometabonomics, with recent experimental exemplifications of the approach being described and discussed. Pharmacometabonomics seeks to predict the response of an individual to a stimulus (e.g., drug, toxin, surgery, nutrition and so on) prior to the stimulus or other perturbation. It is an integral part of top-down systems biology which aims to improve understanding of phenotypic differences and the impact of beneficial and pathological interventions. The pharmacometabonomic concept is also integral to the understanding of mammalian-gut microbiome cometabolic interactions and their consequences, including the impact on disease and therapy. Although the subject is only at an early stage and requires further exemplification and validation, the approach has major implications for improved efficiency in drug discovery efforts, for example, by enabling more careful selection of animals in preclinical studies, for better stratification of patients in drug clinical trials and for individualized therapies. It could also find application in population-wide large cohort studies and in studies of nutrition where it would allow the elucidation of health risk factors and provide easily measured surrogate biomarkers.

Bioactive food components and cancer-specific metabonomic profiles

Cancer cells possess unique metabolic signatures compared to normal cells, including shifts in aerobic glycolysis, glutaminolysis, and de novo biosynthesis of macromolecules. Targeting these changes with agents (drugs and dietary components) has been employed as strategies to reduce the complications associated with tumorigenesis. This paper highlights the ability of several food components to suppress tumor-specific metabolic pathways, including increased expression of glucose transporters, oncogenic tyrosine kinase, tumor-specific M2-type pyruvate kinase, and fatty acid synthase, and the detection of such effects using various metabonomic technologies, including liquid chromatography/mass spectrometry (LC/MS) and stable isotope-labeled MS. Stable isotope-mediated tracing technologies offer exciting opportunities for defining specific target(s) for food components. Exposures, especially during the early transition phase from normal to cancer, are critical for the translation of knowledge about food components into effective prevention strategies. Although appropriate dietary exposures needed to alter cellular metabolism remain inconsistent and/or ill-defined, validated metabonomic biomarkers for dietary components hold promise for establishing effective strategies for cancer prevention.

Integrated cytokine and metabolic analysis of pathological responses to parasite exposure in rodents

Parasitic infections cause a myriad of responses in their mammalian hosts, on immune as well as on metabolic level. A multiplex panel of cytokines and metabolites derived from four parasite-rodent models, namely, Plasmodium berghei−mouse, Trypanosoma brucei brucei−mouse, Schistosoma mansoni−mouse, and Fasciola hepatica−rat were statistically coanalyzed. ¹H NMR spectroscopy and multivariate statistical analysis were used to characterize the urine and plasma metabolite profiles in infected and noninfected animals. Each parasite generated a unique metabolic signature in the host. Plasma cytokine concentrations were obtained using the ‘Meso Scale Discovery’ multi cytokine assay platform. Multivariate data integration methods were subsequently used to elucidate the component of the metabolic signature which is associated with inflammation and to determine specific metabolic correlates with parasite-induced changes in plasma cytokine levels. For example, the relative levels of acetyl glycoproteins extracted from the plasma metabolite profile in the P. berghei-infected mice were statistically correlated with IFN-γ, whereas the same cytokine was anticorrelated with glucose levels. Both the metabolic and the cytokine data showed a similar spatial distribution in principal component analysis scores plots constructed for the combined murine data, with samples from all infected animals clustering according to the parasite species and whereby the protozoan infections (P. berghei and T. b. brucei) grouped separately from the helminth infection (S. mansoni). For S. mansoni, the main infection-responsive cytokines were IL-4 and IL-5, which covaried with lactate, choline, and d-3-hydroxybutyrate. This study demonstrates that the inherently differential immune response to single- and multicellular parasites not only manifests in the cytokine expression, but also consequently imprints on the metabolic signature, and calls for in-depth analysis to further explore direct links between immune features and biochemical pathways.

Parasitic infections cause a myriad of responses in their mammalian hosts, including a range of immune reactions and metabolic perturbations. Here, a multiplex panel of cytokines and metabolites derived from four parasite-rodent models, namely, Plasmodium berghei−mouse, Trypanosoma brucei brucei−mouse, Schistosoma mansoni−mouse, and Fasciola hepatica−rat were statistically coanalyzed. ¹H NMR spectroscopy and multivariate statistical analysis were used to characterize the urine and plasma metabolite profiles in infected and noninfected control animals.

Metabolic signatures of malignant progression in prostate epithelial cells

Prognostic markers that can distinguish indolent from aggressive prostate cancer could have substantial patient benefit, helping to target patients most in need of radical intervention, while avoiding overtreatment of a highly prevalent condition. The search for novel cancer biomarkers has been facilitated by the development of technologies for "global" biomolecular profiling, used in the sciences of transcriptomics, proteomics and metabolic profiling (metabonomics/metabolomics). Using an NMR-based approach we compared intracellular and extracellular metabolic profiles from the immortalised, non-tumourigenic prostate epithelial cell line, RWPE-1 and two tumourigenic sublines with increasing malignant phenotypes, WPE1-NB14 and WPE1-NB11, generated by N-methyl-N-nitrosourea (MNU) mutagenesis. Collectively, these cell lines present an in vitro model of prostate cancer progression and disease aggression. We observed progressive alterations to intracellular levels of multiple metabolites from choline and branched chain amino acid metabolic pathways from RWPE-1 to WPE1-NB14 to WPE1-NB11 cells. In addition specific perturbations to intracellular glycine and lactate and extracellular lactate and alanine were observed relative to the parent line. The pathways implicated by comparative metabolic profiling in this model are known to be altered in human prostate cancer, and potentially represent a source of biomarkers for prostate cancer aggression.

Plasma metabolic profiling reveals age-dependency of systemic effects of green tea polyphenols in mice with and without prostate cancer

Green tea polyphenols (GTP) have been widely investigated for their potential to prevent prostate cancer. However, results from epidemiological and clinical studies are equivocal. Studies in the TRAMP (TRansgenic Adenocarcinoma of the Mouse Prostate) mouse suggest that the chemopreventive efficacy of GTP is higher in young animals with early stages of carcinogenesis than in old ones. Here, effects of GTP on prostate carcinogenesis in TRAMP mice were assessed by comparing pathological changes with (1)H-NMR metabolic profiling of plasma and extracts of prostate tissue. Mice received 0.05% GTP in their drinking water for 4 or 25 weeks after weaning. Age-matched wild-type mice were included in the study in order to establish differences in GTP effects between normal and TRAMP mice. Dietary GTP did not markedly alter prostate carcinogenesis as reflected by pathology and prostate tissue metabolic profile. However, a systemic effect of GTP consumption was observed in young mice, regardless of genotype. Plasma lipid signals were decreased in 8 week old mice which received GTP compared to age-matched controls by 19, 61, 27, 34 and 15% (p <or= 0.05) in the CH(2)CH(2)C[double bond, length as m-dash]C (m 2.00 ppm), CH(2)CH(2)CO (m 1.58 ppm), CH(2) (m 1.26 ppm), CH(3) (m 0.88 ppm) and CH(3) fatty acid resonances (m 0.84 ppm), respectively. GTP consumption did not affect the plasma metabolic profile in 29 week old mice. These results suggest that age rather than disease state determines systemic effects of GTP. More studies are required to investigate factors, such as age or metabolic make-up, inherent to a population or an individual, which may modulate the chemopreventive efficacy of GTP.