Applications of metabolomics in cancer research

Glutamine Starvation Affects Cell Cycle, Oxidative Homeostasis and Metabolism in Colorectal Cancer Cells

Cancer cells adjust their metabolism to meet energy demands. In particular, glutamine addiction represents a distinctive feature of several types of tumors, including colorectal cancer. In this study, four colorectal cancer cell lines (Caco-2, HCT116, HT29 and SW480) were cultured with or without glutamine. The growth and proliferation rate, colony-forming capacity, apoptosis, cell cycle, redox homeostasis and metabolomic analysis were evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide test (MTT), flow cytometry, high-performance liquid chromatography and gas chromatography/mass spectrometry techniques. The results show that glutamine represents an important metabolite for cell growth and that its deprivation reduces the proliferation of colorectal cancer cells. Glutamine depletion induces cell death and cell cycle arrest in the GO/G1 phase by modulating energy metabolism, the amino acid content and antioxidant defenses. Moreover, the combined glutamine starvation with the glycolysis inhibitor 2-deoxy-D-glucose exerted a stronger cytotoxic effect. This study offers a strong rationale for targeting glutamine metabolism alone or in combination with glucose metabolism to achieve a therapeutic benefit in the treatment of colon cancer.

Focus on the molecular mechanisms of cisplatin resistance based on multi-omics approaches

Cisplatin is commonly used in combination with other cytotoxic agents as a standard treatment regimen for a variety of solid tumors, such as lung, ovarian, testicular, and head and neck cancers. However, the effectiveness of cisplatin is accompanied by toxic side effects, for instance, nephrotoxicity and neurotoxicity. The response of tumors to cisplatin treatment involves multiple physiological processes, and the efficacy of chemotherapy is limited by the intrinsic and acquired resistance of tumor cells. Although enormous efforts have been made toward molecular mechanisms of cisplatin resistance, the development of omics provides new insights into the understanding of cisplatin resistance at genome, transcriptome, proteome, metabolome and epigenome levels. Mechanism studies using different omics approaches revealed the necessity of multi-omics applications, which provide information at different cellular function levels and expand our recognition of the peculiar genetic and phenotypic heterogeneity of cancer. The present work systematically describes the underlying mechanisms of cisplatin resistance in different tumor types using multi-omics approaches. In addition to the classical mechanisms such as enhanced drug efflux, increased DNA damage repair and changes in the cell cycle and apoptotic pathways, other changes like increased protein damage clearance, increased protein glycosylation, enhanced glycolytic process, dysregulation of the oxidative phosphorylation pathway, ferroptosis suppression and mRNA m6A methylation modification can also induce cisplatin resistance. Therefore, utilizing the integrated omics to identify key signaling pathways, target genes and biomarkers that regulate chemoresistance are essential for the development of new drugs or strategies to restore tumor sensitivity to cisplatin.

Machine Learning Using Neural Networks for Metabolomic Pathway Analyses

Elucidating the mechanisms of metabolic pathways helps us understand the cascade of enzyme-catalyzed reactions that lead to the conversion of substances into final products. This has implications for predicting how newly synthesized compounds will affect a person's metabolism and, hence, the development of novel treatments to improve one's health. The study of metabolomic pathways, together with protein engineering, may also aid in the extraction, at a scale, of natural products to be used as drugs and drug precursors. Several approaches have been used to correlate protein annotations to metabolic pathways in order to derive pathways directly related to specific organisms. These could range from association rule-mining techniques to machine learning methods such as decision trees, naïve Bayes, logistic regression, and ensemble methods.In this chapter, we will be reviewing the use of machine learning for metabolic pathway analyses, with a step-by-step focus on the use of deep learning to predict the association of compounds (metabolites) to their respective metabolomic pathway classes. This prediction could help explain interactions of small molecules in organisms. Inspired by the work of Baranwal et al. (2019), we demonstrate how to build and train a deep learning neural network model to perform a multi-label prediction. We considered two different types of fingerprints as features (inputs to the model). The output of the model is the set of metabolic pathway classes (from the KEGG dataset) in which the input molecule participates. We will walk through the various steps of this process, including data collection, feature engineering, model selection, training, and evaluation. This model-building and evaluation process may be easily transferred to other domains of interest. All the source code used in this chapter is made publicly available at https://github.com/jp-um/machine_learning_for_metabolomic_pathway_analyses .

CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics

Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M - H]^-, 155 [M + H]⁺, and 138 [M + Na]⁺ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a Δ_CCS threshold of 2.8% and a mass error of 10 ppm.

Cellular landscaping of cisplatin resistance in cervical cancer

Cervical cancer (CC) caused by human papillomavirus (HPV) is one of the largest causes of malignancies in women worldwide. Cisplatin is one of the widely used drugs for the treatment of CC is rendered ineffective owing to drug resistance. This review highlights the cause of resistance and the mechanism of cisplatin resistance cells in CC to develop therapeutic ventures and strategies that could be utilized to overcome the aforementioned issue. These strategies would include the application of nanocarries, miRNA, CRIPSR/Cas system, and chemotherapeutics in synergy with cisplatin to not only overcome the issues of drug resistance but also enhance its anti-cancer efficiency. Moreover, we have also discussed the signaling network of cisplatin resistance cells in CC that would provide insights to develop therapeutic target sites and inhibitors. Furthermore, we have discussed the role of CC metabolism on cisplatin resistance cells and the physical and biological factors affecting the tumor microenvironments.

Integrated Transcriptome and Metabolomic Analysis Reveal Anti-Angiogenic Properties of Disarib, a Novel Bcl2-Specific Inhibitor

Transcriptomic profiling of several drugs in cancer cell lines has been utilised to obtain drug-specific signatures and guided combination therapy to combat drug resistance and toxicity. Global metabolomics reflects changes due to altered activity of enzymes, environmental factors, etc. Integrating transcriptomics and metabolomics can provide genotype-phenotype correlation, providing meaningful insights into alterations in gene expression and its outcome to understand differential metabolism and guide therapy. This study uses a multi-omics approach to understand the global gene expression and metabolite changes induced by Disarib, a novel Bcl2-specific inhibitor in the Ehrlich adenocarcinoma (EAC) breast cancer mouse model. RNAseq analysis was performed on EAC mouse tumours treated with Disarib and compared to the controls. The expression of 6 oncogenes and 101 tumour suppressor genes interacting with Bcl2 and Bak were modulated upon Disarib treatment. Cancer hallmark pathways like DNA repair, Cell cycle, angiogenesis, and mitochondrial metabolism were downregulated, and programmed cell death platelet-related pathways were upregulated. Global metabolomic profiling using LC-MS revealed that Oncometabolites like carnitine, oleic acid, glycine, and arginine were elevated in tumour mice compared to normal and were downregulated upon Disarib treatment. Integrated transcriptomic and metabolomic profiles identified arginine metabolism, histidine, and purine metabolism to be altered upon Disarib treatment. Pro-angiogenic metabolites, arginine, palmitic acid, oleic acid, and myristoleic acid were downregulated in Disarib-treated mice. We further validated the effect of Disarib on angiogenesis by qRT-PCR analysis of genes in the VEGF pathway. Disarib treatment led to the downregulation of pro-angiogenic markers. Furthermore, the chorioallantoic membrane assay displayed a reduction in the formation of the number of secondary blood vessels upon Disarib treatment. Disarib reduces tumours by reducing oncometabolite and activating apoptosis and downregulating angiogenesis.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Targeted metabolomics analysis of serum and Mycobacterium tuberculosis antigen-stimulated blood cultures of pediatric patients with active and latent tuberculosis

Profound tuberculosis (TB)-induced metabolic changes reflected in the blood metabolomic profile may provide an opportunity to identify specific markers of Mycobacterium tuberculosis infection. Using targeted liquid chromatography tandem mass spectrometry, we compared the levels of 30 small metabolites, including amino acids and derivatives, and small organic compounds in serum and M.tb antigen-stimulated whole blood cultures of active TB children, latent TB (LTBI) children, nonmycobacterial pneumonia (NMP) children, and healthy controls (HCs) to assess their potential as biomarkers of childhood TB. We found elevated levels of leucine and kynurenine combined with reduced concentrations of citrulline and glutamine in serum and blood cultures of TB and LTBI groups. LTBI status was additionally associated with a decrease in valine levels in blood cultures. The NMP metabolite profile was characterized by an increase in citrulline and glutamine and a decrease in leucine, kynurenine and valine concentrations. The highest discriminatory potential for identifying M.tb infection was observed for leucine detected in serum and kynurenine in stimulated blood cultures. The use of targeted metabolomics may reveal metabolic changes in M.tb-infected children, and the obtained results are a proof of principle of the usefulness of metabolites in the auxiliary diagnosis of TB in children.

Metabolomics Analysis of Cell-Free Expression Systems Using Gas Chromatography-Mass Spectrometry

Metabolomics is the systems-scale study of the biochemical intermediates of metabolism. This approach has great potential to uncover how metabolic intermediates are used and generated in cell-free expression systems, something that is to date not well understood. Here, we present a detailed metabolomics protocol for characterization of the small molecules in cell-free systems. We specifically focus on the analysis of Escherichia coli lysate-based cell-free systems using gas chromatography coupled to mass spectrometry. Measuring and monitoring the metabolic changes in cell-free systems can provide insight into the ways that metabolites affect the productivity of cell-free reactions, ultimately allowing for more informed engineering and optimization efforts for cell-free systems.

Platinum-resistant ovarian cancer: From drug resistance mechanisms to liquid biopsy-based biomarkers for disease management

Resistance to platinum-based chemotherapy is a major clinical challenge in ovarian cancer, contributing to the high mortality-to-incidence ratio. Management of the platinum-resistant disease has been difficult due to diverse underlying molecular mechanisms. Over the past several years, research has revealed several novel molecular targets that are being explored as biomarkers for treatment planning and monitoring of response. The therapeutic landscape of ovarian cancer is also rapidly evolving, and alternative therapies are becoming available for the recurrent platinum-resistant disease. This review provides a snapshot of platinum resistance mechanisms and discusses liquid-based biomarkers and their potential utility in effective management of platinum-resistant ovarian cancer.

Metabonomics study of the effects of single copy mutant KRAS in the presence or absence of WT allele using human HCT116 isogenic cell lines

INTRODUCTION

KRAS was one of the earliest human oncogenes to be described and is one of the most commonly mutated genes in different human cancers, including colorectal cancer. Despite KRAS mutants being known driver mutations, KRAS has proved difficult to target therapeutically, necessitating a comprehensive understanding of the molecular mechanisms underlying KRAS-driven cellular transformation.

OBJECTIVES

To investigate the metabolic signatures associated with single copy mutant KRAS in isogenic human colorectal cancer cells and to determine what metabolic pathways are affected.

METHODS

Using NMR-based metabonomics, we compared wildtype (WT)-KRAS and mutant KRAS effects on cancer cell metabolism using metabolic profiling of the parental KRAS ^G13D/+ HCT116 cell line and its isogenic, derivative cell lines KRAS ^+/- and KRAS ^G13D/-.

RESULTS

Mutation in the KRAS oncogene leads to a general metabolic remodelling to sustain growth and counter stress, including alterations in the metabolism of amino acids and enhanced glutathione biosynthesis. Additionally, we show that KRAS^G13D/+ and KRAS^G13D/- cells have a distinct metabolic profile characterized by dysregulation of TCA cycle, up-regulation of glycolysis and glutathione metabolism pathway as well as increased glutamine uptake and acetate utilization.

CONCLUSIONS

Our study showed the effect of a single point mutation in one KRAS allele and KRAS allele loss in an isogenic genetic background, hence avoiding confounding genetic factors. Metabolic differences among different KRAS mutations might play a role in their different responses to anticancer treatments and hence could be exploited as novel metabolic vulnerabilities to develop more effective therapies against oncogenic KRAS.

Grading of endometrial cancer using 1H HR-MAS NMR-based metabolomics

The tissue metabolomic characteristics associated with endometrial cancer (EC) at different grades were studied using high resolution (400 MHz) magic angle spinning (HR-MAS) proton spectroscopy. The metabolic profiles were obtained from 64 patients (14 with grade 1 (G1), 33 with grade 2 (G2) and 17 with grade 3 (G3) tumors) and compared with the profile acquired from 10 patients with the benign disorders. OPLS-DA revealed increased valine, isoleucine, leucine, hypotaurine, serine, lysine, ethanolamine, choline and decreased creatine, creatinine, glutathione, ascorbate, glutamate, phosphoethanolamine and scyllo-inositol in all EC grades in reference to the non-transformed tissue. The increased levels of taurine was additionally detected in the G1 and G2 tumors in comparison to the control tissue, while the elevated glycine, N-acetyl compound and lactate—in the G1 and G3 tumors. The metabolic features typical for the G1 tumors are the increased dimethyl sulfone, phosphocholine, and decreased glycerophosphocholine and glutamine levels, while the decreased myo-inositol level is characteristic for the G2 and G3 tumors. The elevated 3-hydroxybutyrate, alanine and betaine levels were observed in the G3 tumors. The differences between the grade G1 and G3 malignances were mainly related to the perturbations of phosphoethanolamine and phosphocholine biosynthesis, inositol, betaine, serine and glycine metabolism. The statistical significance of the OPLS-DA modeling was also verified by an univariate analysis. HR-MAS NMR based metabolomics provides an useful insight into the metabolic reprogramming in endometrial cancer.

Biomarkers in Pancreatic Cancer as Analytic Targets for Nanomediated Imaging and Therapy

As the increase in therapeutic and imaging technologies is swiftly improving survival chances for cancer patients, pancreatic cancer (PC) still has a grim prognosis and a rising incidence. Practically everything distinguishing for this type of malignancy makes it challenging to treat: no approved method for early detection, extended asymptomatic state, limited treatment options, poor chemotherapy response and dense tumor stroma that impedes drug delivery. We provide a narrative review of our main findings in the field of nanoparticle directed treatment for PC, with a focus on biomarker targeted delivery. By reducing drug toxicity, increasing their tumor accumulation, ability to modulate tumor microenvironment and even improve imaging contrast, it seems that nanotechnology may one day give hope for better outcome in pancreatic cancer. Further conjugating nanoparticles with biomarkers that are overexpressed amplifies the benefits mentioned, with potential increase in survival and treatment response.

Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models

Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results.

Specific activation of glycolytic enzyme enolase 2 in BRAF V600E-mutated colorectal cancer

The BRAF V600E mutation occurs in approximately 10% of patients with metastatic colorectal cancer (CRC) and constitutes a distinct subtype of the disease with extremely poor prognosis. To address this refractory disease, we investigated the unique metabolic gene profile of BRAF V600E‐mutated tumors via in silico analysis using a large‐scale clinical database. We found that BRAF V600E‐mutated tumors exhibited a specific metabolic gene expression signature, including some genes that are associated with poor prognosis in CRC. We discovered that BRAF V600E‐mutated tumors expressed high levels of glycolytic enzyme enolase 2 (ENO2), which is mainly expressed in neuronal tissues under physiological conditions. In vitro experiments using CRC cells demonstrated that BRAF V600E‐mutated cells exhibited enhanced dependency on ENO2 compared to BRAF wild‐type cancer cells and that knockdown of ENO2 led to the inhibition of proliferation and migration of BRAF V600E‐mutated cancer cells. Moreover, inhibition of ENO2 resulted in enhanced sensitivity to vemurafenib, a selective inhibitor of BRAF V600E. We identified AP‐1 transcription factor subunit (FOSL1) as being involved in the transcription of ENO2 in CRC cells. In addition, both MAPK and PI3K/Akt signaling were suppressed upon inhibition of ENO2, implying an additional oncogenic role of ENO2. These results suggest the crucial role of ENO2 in the progression of BRAF V600E‐mutated CRC and indicate the therapeutic implications of targeting this gene.

The mediation effect of serum metabolites on the relationship between long-term smoking exposure and esophageal squamous cell carcinoma

BACKGROUND

Long-term smoking exposure will increase the risk of esophageal squamous cell carcinoma (ESCC), whereas the mechanism is still unclear. We conducted a cross-sectional study to explore whether serum metabolites mediate the occurrence of ESCC caused by cigarette smoking.

METHODS

Serum metabolic profiles and lifestyle information of 464 participants were analyzed. Multiple logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of smoking exposure to ESCC risk. High-dimensional mediation analysis and univariate mediation analysis were performed to screen potential intermediate metabolites of smoking exposure for ESCC.

RESULTS

Ever smoking was associated with a 3.11-fold increase of ESCC risk (OR = 3.11, 95% CI 1.63-6.05), and for each cigarette-years increase in smoking index, ESCC risk increased by 56% (OR = 1.56, 95% CI 1.18-2.13). A total of 5 metabolites were screened as mediators by high-dimensional mediation analysis. In addition, glutamine, histidine, and cholic acid were further proved existing mediation effects according to univariate mediation analysis. And the proportions of mediation of histidine and glutamine were 40.47 and 30.00%, respectively. The mediation effect of cholic acid was 8.98% according to the analysis of smoking index.

CONCLUSIONS

Our findings suggest that cigarette smoking contributed to incident ESCC, which may be mediated by glutamine, histidine and cholic acid.

Metabolomics of Gastric Cancer

Gastric cancer is the fourth most common malignancy worldwide and the third leading cause of cancer deaths. Recent metabolomics research has advanced our understanding of the relationship between metabolic reprogramming and gastric cancer progression and led to the discovery of metabolic targets for potential clinical applications and therapeutic interventions. As a powerful tool for metabolite and flux measurement, metabolomics not only allows a comprehensive analysis of metabolites and related metabolic pathways but also can investigate the interactions between gastric cancer cells and tumour microenvironment as well as between the cancer cells and gastric microbiome. In this chapter, we aim to summarize the recent advances in gastric cancer metabolism and discuss the applications of metabolomics for target discovery in gastric cancer.

Metabolomic Analysis to Elucidate Mechanisms of Sunitinib Resistance in Renal Cell Carcinoma

Metabolomics analysis possibly identifies new therapeutic targets in treatment resistance by measuring changes in metabolites accompanying cancer progression. We previously conducted a global metabolomics (G-Met) study of renal cell carcinoma (RCC) and identified metabolites that may be involved in sunitinib resistance in RCC. Here, we aimed to elucidate possible mechanisms of sunitinib resistance in RCC through intracellular metabolites. We established sunitinib-resistant and control RCC cell lines from tumor tissues of RCC cell (786-O)-injected mice. We also quantified characteristic metabolites identified in our G-Met study to compare intracellular metabolism between the two cell lines using liquid chromatography-mass spectrometry. The established sunitinib-resistant RCC cell line demonstrated significantly desuppressed protein kinase B (Akt) and mesenchymal-to-epithelial transition (MET) phosphorylation compared with the control RCC cell line under sunitinib exposure. Among identified metabolites, glutamine, glutamic acid, and α-KG (involved in glutamine uptake into the tricarboxylic acid (TCA) cycle for energy metabolism); fructose 6-phosphate, D-sedoheptulose 7-phosphate, and glucose 1-phosphate (involved in increased glycolysis and its intermediate metabolites); and glutathione and myoinositol (antioxidant effects) were significantly increased in the sunitinib-resistant RCC cell line. Particularly, glutamine transporter (SLC1A5) expression was significantly increased in sunitinib-resistant RCC cells compared with control cells. In this study, we demonstrated energy metabolism with glutamine uptake and glycolysis upregulation, as well as antioxidant activity, was also associated with sunitinib resistance in RCC cells.

Cutaneous Thermal Injury Modulates Blood and Skin Metabolomes Differently in a Murine Model

As the field of metabolomics develops further, investigations of how the metabolome is affected following thermal injury may be helpful to inform diagnostics and guide treatments. In this study, changes to the metabolome were tested and validated in a murine burn injury model. After a 30% total body surface scald injury or sham procedure sera and skin biopsies were collected at 1, 2, 6, or 24 hr.

Burn-specific changes in the metabolome were detected compared to sham animals. The sera metabolome exhibited a more rapid response to burn injury than that of the skin and it peaked more proximal to injury (6 vs 24 hr). Progression of metabolic response in the skin was less synchronous and showed a higher overlap of the significantly modified metabolites (SMMs) among tested time-points. Top affected pathways identified by SMMs of skin included inositol phosphate metabolism, ascorbate and alderate metabolism, caffeine metabolism, and the pentose phosphate pathway. Future research is warranted in human and larger animal models to further elucidate the role of metabolomic perturbations and the pathophysiology following burn injury.

Metabolomics in the study of spontaneous animal diseases

Using analytical chemistry techniques such as nuclear magnetic resonance (NMR) spectroscopy and liquid or gas chromatography-mass spectrometry (LC/GC-MS), metabolomics allows detection of most endogenous and exogenous metabolites in a biological sample. Metabolomics has a wide range of applications, and has been employed in nutrition science, toxicology, environmental studies, and systems biology. Metabolomics is particularly useful in biomedical science, and has been used for diagnostic laboratory testing, identifying targets for drug development, and monitoring drug metabolism, mode of action, and toxicity. Despite its immense potential, metabolomics remains underutilized in the study of spontaneous animal diseases. Our aim was to comprehensively review the existing literature on the use of metabolomics in spontaneous veterinary diseases. Three databases were used to find journal articles that applied metabolomics in veterinary medicine. A screening process was then conducted to eliminate references that did not meet the eligibility criteria; only primary research studies investigating spontaneous animal disease were included; 38 studies met the inclusion criteria. The main techniques used were NMR and MS. All studies detected metabolite alterations in diseased animals compared with non-diseased animals. Metabolomics was mainly used to study diseases of the digestive, reproductive, and musculoskeletal systems. Inflammatory conditions made up the largest proportion of studies when articles were categorized by disease process. Following a comprehensive analysis of the literature on metabolomics in spontaneous veterinary diseases, we concluded that metabolomics, although in its early stages in veterinary research, is a promising tool regarding diagnosis, biomarker discovery, and in uncovering new insights into disease pathophysiology.

Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer

Genomics and transcriptomics have led to the widely-used molecular classification of breast cancer (BC). However, heterogeneous biological behaviors persist within breast cancer subtypes. Metabolomics is a rapidly-expanding field of study dedicated to cellular metabolisms affected by the environment. The aim of this study was to compare metabolomic signatures of BC obtained by 5 different unsupervised machine learning (ML) methods. Fifty-two consecutive patients with BC with an indication for adjuvant chemotherapy between 2013 and 2016 were retrospectively included. We performed metabolomic profiling of tumor resection samples using liquid chromatography-mass spectrometry. Here, four hundred and forty-nine identified metabolites were selected for further analysis. Clusters obtained using 5 unsupervised ML methods (PCA k-means, sparse k-means, spectral clustering, SIMLR and k-sparse) were compared in terms of clinical and biological characteristics. With an optimal partitioning parameter k = 3, the five methods identified three prognosis groups of patients (favorable, intermediate, unfavorable) with different clinical and biological profiles. SIMLR and K-sparse methods were the most effective techniques in terms of clustering. In-silico survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.

Evaluation of MDA-MB-468 Cell Culture Media Analysis in Predicting Triple-Negative Breast Cancer Patient Sera Metabolic Profiles

Triple-negative breast cancer (TNBC) is characterized by limited survival, poor prognosis, and high recurrence. Understanding the metabolic adaptations of TNBC could help reveal improved treatment regiments. Here we performed a comprehensive ¹H NMR metabolic characterization of the MDA-MB-468 cell line, a commonly used model of TNBC, followed by an analysis of serum samples obtained from TNBC patients and healthy controls. MDA-MB-468 cells were cultured, and changes in the metabolic composition of the medium were monitored for 72 h. Based on time courses, metabolites were categorized as being consumed, being produced, or showing a mixed behavior. When comparing TNBC and control samples (HC), and by using multivariate and univariate analyses, we identified nine metabolites with differing profiles). The serum of TNBC patients was characterized by higher levels of glucose, glutamine, citrate, and acetoacetate and by lower levels of lactate, alanine, tyrosine, glutamate, and acetone. A comparative analysis between MDA-MB-468 cell culture media and TNBC patients' serum identified a potential systemic response to the carcinogenesis-associated processes, highlighting that MDA-MB-468 cells footprint does not reflect metabolic changes observed in studied TNBC serum fingerprint.

The effect of polysaccharides from Cibotium barometz on enhancing temozolomide-induced glutathione exhausted in human glioblastoma U87 cells, as revealed by 1H NMR metabolomics analysis

Glioblastoma (GBM) is the most malignant central nervous system tumor, with poor prognosis. Temozolomide (TMZ) has been used as a first-line drug for the treatment of GBM for over a decade, but its treatment benefits are limited by acquired resistance. Polysaccharides from Cibotium barometz (CBPs) are polysaccharides purified from the root of Cibotium barometz (L.) J. Sm., possessing sensitizing activity. The purpose of this study was to investigate the anti-cancer effect of CBP from different processing methods on U87 cells using a ¹H NMR-based metabolic approach, complemented with qRT-PCR and flow cytometry, to identify potential markers and discover the targets to explore the underlying mechanism. Cibotium barometz is usually processed under sand heating in clinical applications. Polysaccharides from both the processed (PCBP) and raw (RCBP) C. barometz were prepared, and the effect on enhancing the sensitivity to TMZ was investigated in vitro. CBP can significantly increase the toxicity of TMZ to the U87 cell line, promote apoptosis, enhance cell cycle changes, and arrest cells in S phase, and RCBP demonstrated better activity. Multivariate statistical analyses, such as principal component analysis (PCA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA), were used to identify metabolic biomarkers, and 12 metabolites in the cell extract samples were clearly identified as altered after RCBP exposure. NMR-based cell metabolomics provided a holistic method for the identification of CBP's apoptosis-enhancing mechanisms and the exploration of its potential applications in preclinical and clinical studies.

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

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

The Many Facets of Tumor Heterogeneity: Is Metabolism Lagging Behind?

Tumor functional heterogeneity has been recognized for decades, and technological advancements are fueling renewed interest in uncovering the cell-intrinsic and extrinsic factors that influence tumor development and therapeutic response. Intratumoral heterogeneity is now arguably one of the most-studied topics in tumor biology, leading to the discovery of new paradigms and reinterpretation of old ones, as we aim to understand the profound implications that genomic, epigenomic, and functional heterogeneity hold with regard to clinical outcomes. In spite of our improved understanding of the biological complexity of cancer, characterization of tumor metabolic heterogeneity has lagged behind, lost in a century-old controversy debating whether glycolysis or mitochondrial respiration is more influential. But is tumor metabolism really so simple? Here, we review historical and current views of intratumoral heterogeneity, with an emphasis on summarizing the emerging data that begin to illuminate just how vast the spectrum of metabolic strategies a tumor can employ may be, and what this means for how we might interpret other tumor characteristics, such as mutational landscape, contribution of microenvironmental influences, and treatment resistance.

Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data

Analytical techniques such as NMR and mass spectrometry can generate large metabolomics data sets containing thousands of spectral features derived from numerous biological observations. Multivariate data analysis is routinely used to uncover the underlying biological information contained within these large metabolomics data sets. This is typically accomplished by classifying the observations into groups (e.g., control versus treated) and by identifying associated discriminating features. There are a variety of classification models to select from, which include some well-established techniques (e.g., principal component analysis [PCA], orthogonal projection to latent structure [OPLS], or partial least-squares projection to latent structures [PLS]) and newly emerging machine learning algorithms (e.g., support vector machines or random forests). However, it is unclear which classification model, if any, is an optimal choice for the analysis of metabolomics data. Herein, we present a comprehensive evaluation of five common classification models routinely employed in the metabolomics field and that are also currently available in our MVAPACK metabolomics software package. Simulated and experimental NMR data sets with various levels of group separation were used to evaluate each model. Model performance was assessed by classification accuracy rate, by the area under a receiver operating characteristic (AUROC) curve, and by the identification of true discriminating features. Our findings suggest that the five classification models perform equally well with robust data sets. Only when the models are stressed with subtle data set differences does OPLS emerge as the best-performing model. OPLS maintained a high-prediction accuracy rate and a large area under the ROC curve while yielding loadings closest to the true loadings with limited group separations.

1H HR-MAS NMR-Based Metabolomics of Cancer Cells in Response to Treatment with the Diruthenium Trithiolato Complex [(p-MeC6H4iPr)2Ru2(SC6H4-p-But)3]+ (DiRu-1)

The trithiolato bridged diruthenium complex DiRu-1 [(p-MeC₆H₄ⁱPr)₂Ru₂(SC₆H₄-p-Bu^t)₃]⁺ is highly cytotoxic against various cancer cell lines, but its exact mode of action remains unknown. The present ¹H HR-MAS NMR-based metabolomic study was performed on ovarian cancer cell line A2780, on its cis-Pt resistant variant A2780cisR, and on the cell line HEK-293 treated with 0.03 µM and 0.015 µM of DiRu-1 corresponding to full and half IC₅₀ doses, respectively, to investigate the mode of action of this ruthenium complex. The resulting changes in the metabolic profile of the cell lines were studied using HR-MAS NMR of cell lysates and a subsequent statistical analysis. We show that DiRu-1 in a 0.03 µM dose has significant impact on the levels of a number of metabolites, such as glutamine, glutamate, glutathione, cysteine, lipid, creatine, lactate, and acetate, especially pronounced in the A2780cisR cell line. The IC₅₀/2 dose shows some significant changes, but full IC₅₀ appears to be necessary to observe the full effect. Overall, the metabolic changes observed suggest that redox homeostasis, the Warburg effect, and the lipid metabolism are affected by DiRu-1.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

Preoperative Metabolic Signatures of Prostate Cancer Recurrence Following Radical Prostatectomy

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

Metabolomic Studies of Live Single Cancer Stem Cells Using Mass Spectrometry

Cancer stem cells (CSCs) are rare types of cells responsible for tumor development, relapse, and metastasis. However, current research in CSC biology is largely limited by the difficulty of obtaining sufficient CSCs. Single-cell analysis techniques are promising tools for CSC-related studies. Here, we used the Single-probe mass spectrometry (MS) technique to investigate the metabolic features of live colorectal CSCs at the single-cell level. Experimental data were analyzed using statistical analysis methods, including the t-test and partial least squares discriminant analysis. Our results indicate that the overall metabolic profiles of CSCs are distinct from non-stem cancer cells (NSCCs). Specifically, we demonstrated that tricarboxylic acid (TCA) cycle metabolites are more abundant in CSCs compared to NSCCs, indicating their major energy production pathways are different. Moreover, CSCs have relatively higher levels of unsaturated lipids. Inhibiting the activities of stearoyl-CoA desaturase-1 (SCD1), nuclear factor κB (NF-κB), and aldehyde dehydrogenases (ALDH1A1) in CSCs significantly reduced the abundances of unsaturated lipids and hindered the formation of spheroids, resulting in reduced stemness of CSCs. Our techniques and experimental protocols can be potentially used for metabolomic studies of other CSCs and rare types of cells and provide a new approach to discovering functional biomarkers as therapeutic targets.

5'-Methylthioadenosine and Cancer: old molecules, new understanding

While the metabolic changes in cancer tissues were first observed by Warburg Otto almost a century ago, altered metabolism has recently returned as a focus of cancer research. 5'-Methylthioadenosine (MTA) is a naturally occurring sulfur-containing nucleoside found in numerous species. While MTA was first isolated several decades ago, a lack of sensitive and specific analytical methodologies designed for its direct quantification has hampered the study of its physiological and pathophysiological features. Many studies indicate that MTA suppresses tumors by inhibiting tumor cell proliferation, invasion, and the induction of apoptosis while controlling the inflammatory micro-environments of tumor tissue. In this review, we assessed the effects of MTA and of related materials on the growth and functions of normal and malignant cells.

Assessment of Metabolic Signature for Cancer Diagnosis Using Desorption Electrospray Ionization Mass Spectrometric Imaging

Metabolic reprogramming is a hallmark of tumor development. A technique that can map this complex biochemical shift by taking a snapshot of various metabolites in a tissue specimen (biopsy) is of high utility in the context of cancer diagnosis. Desorption electrospray ionization mass spectrometric imaging (DESI-MSI) is such a powerful and emerging analytical technique to simultaneously visualize the distributions of hundreds of metabolites, lipids, and other small molecules in the biological tissue. In DESI-MSI, a fine spray of high-velocity charged microdroplets rapidly extracts molecular species from the tissue surface and subsequently transfers them to the mass spectrometer, while the sample is continuously moved in two dimensions under the impinging spray of microdroplets. This allows a detailed multiplex molecular mapping of the tissue. DESI-MSI enables simultaneous examination of hundreds of putative metabolic biomarkers, an approach that lends much more predictive power than simply evaluating one or a few candidate biomarkers. The speed, versatility, lack of complicated sample preparation, and operation at ambient conditions make DESI-MSI extremely promising as a rapid diagnostic and prognostic tool.

Mass Spectrometry: A Guide for the Clinician

Metabolic profiling, metabonomics and metabolomics are terms coined in the late 1990s as they emerged as the newest 'omics' technology at the time. This line of research enquiry uses spectroscopic analytical platforms, which are mainly nuclear magnetic resonance spectroscopy and mass spectrometry (MS), to acquire a snapshot of metabolites, the end products of a complex biological system. Metabolic profiling enables the detection, quantification and characterisation of metabolites in biofluids, cells and tissues. The source of these compounds can be of endogenous, microbial or exogenous origin, such as dietary or xenobiotic. This results in generating extensive, multivariate spectroscopic data that require specific statistical manipulation, typically performed using chemometric and pattern recognition techniques to reduce its dimensions, facilitate its biological interpretation and allow sample classification and biomarker discovery. Consequently, it is possible to study the dynamic metabolic changes in response to disease, intervention or environmental conditions. In this review, we describe the fundamentals of MS so that clinicians can be literate in the field and are able to interrogate the right scientific questions.

Association between bivariate expression of key oncogenes and metabolic phenotypes of patients with prostate cancer

BACKGROUND

AKT and MYC are two of the most prevalent oncogenes associated with prostate cancer. The precise effects of overexpression of these two key oncogenes on the regulation of metabolic pathways in prostate cancer are under active investigation; however, few studies have investigated their bivariate oncogene-pair expressions in metabolic prostate cancer phenotypes. This is primarily due to the lack of a suitable statistical method to analyze the data in the presence of oncogene interactions and within-metabolite-set correlations.

METHODS

We analyzed data on the expressions of phosphorylated AKT1 and MYC and the concentrations of 228 metabolites from 60 human prostate tumor samples and 16 normal tissue samples. The metabolomic data allowed us to study not only the measurement of individual metabolites, which can exhibit a dynamic range, but the enriched phenotypes in terms of "metabolite sets" that come from known metabolic pathways. We studied 71 metabolite sets defined by KEGG annotation. We used a modification of linear combination test (LCT) for multiple continuous outcomes to find associations between metabolite sets and oncogenic expressions, after accounting for the correlation between AKT1 and MYC expressions and the correlation between metabolites in a metabolite set. The LCT performance was evaluated using a simulation study.

RESULTS

Through a comprehensive analytical method, our study linked oncogenomics and metabolomics data from patients to improve our understanding of the interconnected mechanisms underlying prostate cancer. This study showed that dysregulations of AKT1 and MYC significantly alter the metabolic pathways activated by nonglucose nutrient sources and their downstream targets. Our findings highlighted the role of MYC as the leading, but not the only, oncogene in prostate oncogenesis. In our simulation study, the LCT performed better than the known alternative method, gene-set enrichment analysis (GSEA).

CONCLUSIONS

Our study offers a solution for linking genomics and metabolomics, working directly with multiple continuous and correlated measurements.

Investigation of Metabolomic Changes in Sunitinib-Resistant Human Renal Carcinoma 786-O Cells by Capillary Electrophoresis-Time of Flight Mass Spectrometry

Acquired resistance to sunitinib is a challenge in the treatment of renal cell carcinoma (RCC). The dysregulation of cellular metabolism is prevalent during resistance acquisition. It is known that in sunitinib-resistant RCC 786-O (786-O Res) cells sunitinib is mainly sequestered in the intracellular lysosomes. However, the relevance between sunitinib resistance and cellular metabolism has not been examined. In this study, we examined the metabolic changes in 786-O Res by using capillary electrophoresis-time of flight mass spectrometry. The cell line 786-O Res was established via persistent treatment with sunitinib, where increase in intracellular sunitinib, and sizes of lysosomes and nuclei were enhanced as compared with those in the parental 786-O (786-O Par) cells. Metabolic analyses revealed that out of the 110 metabolites examined, 13 were up-regulated and 4 were down-regulated in the 786-O Res cells. The glycolysis, tricarboxylic acid cycle and pentose phosphate pathway (PPP) were identified as being altered in the sunitinib-resistant cells, which resulted in the enhanced metabolisms of energy, nucleic acids, and glutathione redox cycle. As sunitinib was sequestered in the enlarged lysosomes in 786-O Res, the enriched energy metabolism might contribute to the maintenance of luminal pH in lysosomes via the H+ ATPase. The changes in the PPP could contribute to nuclei enlargement through up-regulation of nucleic acid biosynthesis and protect 786-O Res from cytotoxicity induced by sunitinib through up-regulation of reduced glutathione. Though the direct link between sunitinib resistance and metabolic alternation remains to be elucidated, this metabolomics study provides fundamental insights into acquisition of sunitinib resistance.

Deciphering hepatocellular carcinoma through metabolomics: from biomarker discovery to therapy evaluation

Hepatocellular carcinoma (HCC) is the third most common cause of death from cancer, with increasing prevalence worldwide. The mortality rate of HCC is similar to its incidence rate, which reflects its poor prognosis. At present, the diagnosis of HCC is still mostly dependent on invasive biopsy, imaging methods, and serum α-fetoprotein (AFP) testing. Because of the asymptomatic nature of early HCC, biopsy and imaging methods usually detect HCC at the middle–late stages. AFP has limited sensitivity and specificity, as many other nonmalignant liver diseases can also result in a very high serum level of AFP. Therefore, better biomarkers with higher sensitivity and specificity at earlier stages are greatly needed. Since metabolic reprogramming is an essential hallmark of cancer and the liver is the metabolic hub of living systems, it is useful to investigate HCC from a metabolic perspective. As a noninvasive and nondestructive approach, metabolomics provides holistic information on dynamically metabolic responses of living systems to both endogenous and exogenous factors. Therefore, it would be conducive to apply metabolomics in investigating HCC. In this review, we summarize recent metabolomic studies on HCC cellular, animal, and clinicopathologic models with attention to metabolomics as a biomarker in cancer diagnosis. Recent applications of metabolomics with respect to therapeutic and prognostic evaluation of HCC are also covered, with emphasis on the potential of treatment by drugs from natural products. In the last section, the current challenges and trends of future development of metabolomics on HCC are discussed. Overall, metabolomics provides us with novel insight into the diagnosis, prognosis, and therapeutic evaluation of HCC.

Expanding the Coverage of the Metabolome with Nitrogen-Based NMR

Isotopically labeling a metabolite and tracing its metabolic fate has provided invaluable insights about the role of metabolism in human diseases in addition to a variety of other issues. ¹³C-labeled metabolite tracers or unlabeled ¹H-based NMR experiments are currently the most common application of NMR to metabolomics studies. Unfortunately, the coverage of the metabolome has been consequently limited to the most abundant carbon-containing metabolites. To expand the coverage of the metabolome and enhance the impact of metabolomics studies, we present a protocol for ¹⁵N-labeled metabolite tracer experiments that may also be combined with routine ¹³C tracer experiments to simultaneously detect both ¹⁵N- and ¹³C-labeled metabolites in metabolic samples. A database consisting of 2D ¹H-¹⁵N HSQC natural-abundance spectra of 50 nitrogen-containing metabolites are also presented to facilitate the assignment of ¹⁵N-labeled metabolites. The methodology is demonstrated by labeling Escherichia coli and Staphylococcus aureus metabolomes with ¹⁵N₁-ammonium chloride, ¹⁵N₄-arginine, and ¹³C₂-acetate. Efficient ¹⁵N and ¹³C metabolite labeling and identification were achieved utilizing standard cell culture and sample preparation protocols.

Circulating biomarkers for early diagnosis of pancreatic cancer: facts and hopes

Pancreatic cancer (PC) is characterized by extremely high mortality and poor prognosis, which are largely ascribed to difficulties in early diagnosis and limited therapeutics. Although there is a sufficient window for intervention before preneoplastic lesions progress to invasive disease, effective early detection of PC remains difficult using current biomarkers and imaging techniques. Biomarkers with satisfactory diagnostic efficacy and convenient analysis methods are urgently required. In this review, we summarized recent advances in the identification of biomarkers in circulation for early detection of PC. A number of novel circulating biomarkers, such as metabolites, cell-free DNA (cfDNA), noncoding RNA, and exosomes, that show promising diagnostic value have been discovered using advances in sequencing techniques and "omics" analyses. Panels comprising several biomarkers may also exhibit better diagnostic performance. In the future, we need more efficient circulating biomarkers for the identification of noninvasive precursor lesions and early disease. Collaborative large-scale studies are also required to show the clinical validity and applicability of potential biomarkers.

Plasma sample based analysis of gastric cancer progression using targeted metabolomics

Gastric carcinogenesis is a multifactorial process described as a stepwise progression from non-active gastritis (NAG), chronic active gastritis (CAG), precursor lesions of gastric cancer (PLGC) and gastric adenocarcinoma. Gastric cancer (GC) 5-year survival rate is highly dependent upon stage of disease at diagnosis, which is based on endoscopy, biopsy and pathological examinations. Non-invasive GC biomarkers would facilitate its diagnosis at early stages leading to improved GC prognosis. We analyzed plasma samples collected from 80 patients diagnosed with NAG without H. pylori infection (NAG−), CAG with H. pylori infection (CAG+), PLGC and GC. A panel of 208 metabolites including acylcarnitines, amino acids and biogenic amines, sphingolipids, glycerophospholipids, hexoses, and tryptophan and phenylalanine metabolites were quantified using two complementary quantitative approaches: Biocrates AbsoluteIDQ®p180 kit and a LC-MS method designed for the analysis of 29 tryptophan pathway and phenylalanine metabolites. Significantly altered metabolic profiles were found in GC patients that allowing discrimination from NAG−, CAG+ and PLGC patients. Pathway analysis showed significantly altered tryptophan and nitrogen metabolic pathways (FDR P < 0.01). Three metabolites (histidine, tryprophan and phenylacetylglutamine) discriminated between non-GC and GC groups. These metabolic signatures open new possibilities to improve surveillance of PLGC patients using a minimally invasive blood analysis.

Metabolite Profiling Reveals the Glutathione Biosynthetic Pathway as a Therapeutic Target in Triple-Negative Breast Cancer

Cancer cells can exhibit altered dependency on specific metabolic pathways and targeting these dependencies is a promising therapeutic strategy. Triple-negative breast cancer (TNBC) is an aggressive and genomically heterogeneous subset of breast cancer that is resistant to existing targeted therapies. To identify metabolic pathway dependencies in TNBC, we first conducted mass spectrometry-based metabolomics of TNBC and control cells. Relative levels of intracellular metabolites distinguished TNBC from nontransformed breast epithelia and revealed two metabolic subtypes within TNBC that correlate with markers of basal-like versus non-basal-like status. Among the distinguishing metabolites, levels of the cellular redox buffer glutathione were lower in TNBC cell lines compared to controls and markedly lower in non-basal-like TNBC. Significantly, these cell lines showed enhanced sensitivity to pharmacologic inhibition of glutathione biosynthesis that was rescued by N-acetylcysteine, demonstrating a dependence on glutathione production to suppress ROS and support tumor cell survival. Consistent with this, patients whose tumors express elevated levels of γ-glutamylcysteine ligase, the rate-limiting enzyme in glutathione biosynthesis, had significantly poorer survival. We find, further, that agents that limit the availability of glutathione precursors enhance both glutathione depletion and TNBC cell killing by γ-glutamylcysteine ligase inhibitors in vitro Importantly, we demonstrate the ability to this approach to suppress glutathione levels and TNBC xenograft growth in vivo Overall, these findings support the potential of targeting the glutathione biosynthetic pathway as a therapeutic strategy in TNBC and identify the non-basal-like subset as most likely to respond. Mol Cancer Ther; 17(1); 264-75. ©2017 AACR.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area.

Current and Emerging Technologies for Probing Molecular Signatures of Traumatic Brain Injury

Traumatic brain injury (TBI) is understood as an interplay between the initial injury, subsequent secondary injuries, and a complex host response all of which are highly heterogeneous. An understanding of the underlying biology suggests a number of windows where mechanistically inspired interventions could be targeted. Unfortunately, biologically plausible therapies have to-date failed to translate into clinical practice. While a number of stereotypical pathways are now understood to be involved, current clinical characterization is too crude for it to be possible to characterize the biological phenotype in a truly mechanistically meaningful way. In this review, we examine current and emerging technologies for fuller biochemical characterization by the simultaneous measurement of multiple, diverse biomarkers. We describe how clinically available techniques such as cerebral microdialysis can be leveraged to give mechanistic insights into TBI pathobiology and how multiplex proteomic and metabolomic techniques can give a more complete description of the underlying biology. We also describe spatially resolved label-free multiplex techniques capable of probing structural differences in chemical signatures. Finally, we touch on the bioinformatics challenges that result from the acquisition of such large amounts of chemical data in the search for a more mechanistically complete description of the TBI phenotype.

Unique and Novel Urinary Metabolomic Features in Malignant versus Benign Adrenal Neoplasms

Purpose: Adrenal incidentalomas must be differentiated from adrenocortical cancer (ACC). Currently, size, growth, and imaging characteristics determine the potential for malignancy but are imperfect. The aim was to evaluate whether urinary small molecules (<800 Da) are associated with ACC.Experimental Design: Preoperative fasting urine specimens from patients with ACC (n = 19) and benign adrenal tumors (n = 46) were analyzed by unbiased ultraperformance liquid chromatography/mass spectrometry. Creatinine-normalized features were analyzed by Progenesis, SIMCA, and unpaired t test adjusted by FDR. Features with an AUC >0.8 were identified through fragmentation patterns and database searches. All lead features were assessed in an independent set from patients with ACC (n = 11) and benign adrenal tumors (n = 46) and in a subset of tissue samples from patients with ACC (n = 15) and benign adrenal tumors (n = 15) in the training set.Results: Sixty-nine features were discovered and four known metabolites identified. Urinary creatine riboside was elevated 2.1-fold (P = 0.0001) in patients with ACC. L-tryptophan, Nε,Nε,Nε-trimethyl-L-lysine, and 3-methylhistidine were lower 0.33-fold (P < 0.0001), 0.56-fold (P < 0.0001), and 0.33-fold (P = 0.0003) in patients with ACC, respectively. Combined multivariate analysis of the four biomarkers showed an AUC of 0.89 [sensitivity 94.7% (confidence interval {CI}, 73.9%-99.1%), specificity 82.6% (CI, 68.6%-92.2%), PPV 69.2% (CI, 48.2%-85.6%), and NPV 97.4% (CI, 86.5%-99.6%)] for distinguishing ACC from benign tumors. Of the four, creatine riboside and four unknown features were validated. Creatine riboside, Nε,Nε,Nε-trimethyl-L-lysine, and two unknown features were elevated in ACC tumors.Conclusions: There are unique urinary metabolic features in patients with ACC with some metabolites present in patient tumor samples. Urinary creatine riboside can differentiate benign adrenal neoplasms from ACC. Clin Cancer Res; 23(17); 5302-10. ©2017 AACR.

Prognostic Metabolite Biomarkers for Soft Tissue Sarcomas Discovered by Mass Spectrometry Imaging

Metabolites can be an important read-out of disease. The identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients is one of the main current research aspects. Mass spectrometry has become the technique of choice for metabolomics studies, and mass spectrometry imaging (MSI) enables their visualization in patient tissues. In this study, we used MSI to identify prognostic metabolite biomarkers in high grade sarcomas; 33 high grade sarcoma patients, comprising osteosarcoma, leiomyosarcoma, myxofibrosarcoma, and undifferentiated pleomorphic sarcoma were analyzed. Metabolite MSI data were obtained from sections of fresh frozen tissue specimens with matrix-assisted laser/desorption ionization (MALDI) MSI in negative polarity using 9-aminoarcridine as matrix. Subsequent annotation of tumor regions by expert pathologists resulted in tumor-specific metabolite signatures, which were then tested for association with patient survival. Metabolite signals with significant clinical value were further validated and identified by high mass resolution Fourier transform ion cyclotron resonance (FTICR) MSI. Three metabolite signals were found to correlate with overall survival (m/z 180.9436 and 241.0118) and metastasis-free survival (m/z 160.8417). FTICR-MSI identified m/z 241.0118 as inositol cyclic phosphate and m/z 160.8417 as carnitine. Graphical Abstract ᅟ.