Analytical metabolomics-based approaches to pancreatic cancer

Metabolic profiling of induced acute pancreatitis and pancreatic cancer progression in a mutant Kras mouse model

Untargeted Nuclear Magnetic Resonance (NMR) metabolomics of polar extracts from the pancreata of a caerulin-induced mouse model of pancreatitis (Pt) and of a transgenic mouse model of pancreatic cancer (PCa) were used to find metabolic markers of Pt and to characterize the metabolic changes accompanying PCa progression. Using multivariate analysis a 10-metabolite metabolic signature specific to Pt tissue was found to distinguish the benign condition from both normal tissue and precancerous tissue (low grade pancreatic intraepithelial neoplasia, PanIN, lesions). The mice pancreata showed significant changes in the progression from normal tissue, through low-grade and high-grade PanIN lesions to pancreatic ductal adenocarcinoma (PDA). These included increased lactate production, amino acid changes consistent with enhanced anaplerosis, decreased concentrations of intermediates in membrane biosynthesis (phosphocholine and phosphoethanolamine) and decreased glycosylated uridine phosphates, reflecting activation of the hexosamine biosynthesis pathway and protein glycosylation.

Investigation of pathogenesis and therapeutic targets of acute myeloid leukemia based on untargeted plasma metabolomics and network pharmacology approach

Acute myeloid leukemia (AML) is a malignant disease originating from bone marrow hematopoietic stem cells, characterized by anemia, hemorrhage, fever, and infection, with low survival rate. However, the pathogenesis of AML is not fully understood at present. In this work, an integrated approach based untargeted metabolomics and network pharmacology was adopted to elucidate the pathogenesis of AML. Metabolic profiling of plasma samples from 14 patients and 16 healthy individuals were performed based on UHPLC-MS platform. As a result, 23 metabolites were identified by using the human metabolite database based on PLS-DA (partial least squares discriminant analysis) and independent sample test. And metabolic pathways related to AML mainly included fatty acid metabolism, amino acid metabolism, energy metabolism and lipid metabolism. Meanwhile, biomarkers-targets-pathways-disease network was constructed, 75 biomarker targets and 122 disease targets were identified. Furthermore, 30 pathways were predicted, some of which were consistent with these in metabolomics. This is the first time that metabolomics and network pharmacology approach have been combined to investigate the pathogenesis and therapeutic targets of AML. ALDH, CYP2E1 and CYP3A4 were potential therapeutic targets for AML, which provide available way to elucidate the pathogenesis and treatment of AML.

A unique urinary metabolomic signature for the detection of pancreatic ductal adenocarcinoma

Our study aimed to identify a urinary metabolite panel for the detection/diagnosis of pancreatic ductal adenocarcinoma (PDAC). PDAC continues to have poor survival outcomes. One of the major reasons for poor prognosis is the advanced stage of the disease at diagnosis. Hence, identification of a novel and cost-effective biomarker signature for early detection/diagnosis of PDAC could lead to better survival outcomes. Untargeted metabolomics was employed to identify a novel metabolite-based biomarker signature for PDAC diagnosis. Urinary metabolites from 92 PDAC patients (56 discovery cohort and 36 validation cohort) were compared with 56 healthy volunteers using ¹ H nuclear magnetic resonance spectroscopy. Multivariate (partial-least squares discriminate analysis) and univariate (Mann-Whitney's U-test) analyses were performed to identify a metabolite panel which can be used to detect PDAC. The selected metabolites were further validated for their diagnostic potential using the area under the receiver operating characteristic (AUROC) curve. Statistical analysis identified a six-metabolite panel (trigonelline, glycolate, hippurate, creatine, myoinositol and hydroxyacetone), which demonstrated high potential to diagnose PDAC, with AUROC of 0.933 and 0.864 in the discovery and validation cohort, respectively. Notably, the identified panel also demonstrated very high potential to diagnose early-stage (I and II) PDAC patients with AUROC of 0.897. These results demonstrate that the selected metabolite signature could be used to detect PDAC and will pave the way for the development of a urinary test for detection/diagnosis of PDAC.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

INTRODUCTION

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

OBJECTIVES

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

METHODS

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Metabolomic profile in pancreatic cancer patients: a consensus-based approach to identify highly discriminating metabolites

Purpose

pancreatic adenocarcinoma is the fourth leading cause of cancer related deaths due to its aggressive behavior and poor clinical outcome. There is a considerable variability in the frequency of serum tumor markers in cancer' patients. We performed a metabolomics screening in patients diagnosed with pancreatic cancer.

Experimental Design

Two targeted metabolomic assays were conducted on 40 serum samples of patients diagnosed with pancreatic cancer and 40 healthy controls. Multivariate methods and classification trees were performed.

Materials and Methods

Sparse partial least squares discriminant analysis (SPLS-DA) was used to reduce the high dimensionality of a pancreatic cancer metabolomic dataset, differentiating between pancreatic cancer (PC) patients and healthy subjects. Using Random Forest analysis palmitic acid, 1,2-dioleoyl-sn-glycero-3-phospho-rac-glycerol, lanosterol, lignoceric acid, 1-monooleoyl-rac-glycerol, cholesterol 5α,6α epoxide, erucic acid and taurolithocholic acid (T-LCA), oleoyl-L-carnitine, oleanolic acid were identified among 206 metabolites as highly discriminating between disease states. Comparison between Receiver Operating Characteristic (ROC) curves for palmitic acid and CA 19-9 showed that the area under the ROC curve (AUC) of palmitic acid (AUC=1.000; 95% confidence interval) is significantly higher than CA 19-9 (AUC=0.963; 95% confidence interval: 0.896-1.000).

Conclusion

Mass spectrometry-based metabolomic profiling of sera from pancreatic cancer patients and normal subjects showed significant alterations in the profiles of the metabolome of PC patients as compared to controls. These findings offer an information-rich matrix for discovering novel candidate biomarkers with diagnostic or prognostic potentials.

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

NMR metabolomics of renal cancer: an overview

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

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

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

Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery

Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics.

Modeling interactions between Human Equilibrative Nucleoside Transporter-1 and other factors involved in the response to gemcitabine treatment to predict clinical outcomes in pancreatic ductal adenocarcinoma patients

Background

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive malignancy, characterized by largely unsatisfactory responses to the currently available therapeutic strategies. In this study we evaluated the expression of genes involved in gemcitabine uptake in a selected cohort of patients with PDAC, with well-defined clinical-pathological features.

Methods

mRNA levels of hENT1, CHOP, MRP1 and DCK were evaluated by means of qRT-PCR in matched pairs of tumor and adjacent normal tissue samples collected from PDAC patients treated with gemcitabine after surgical tumor resection. To detect possible interaction between gene expression levels and to identify subgroups of patients at different mortality/progression risk, the RECursive Partitioning and Amalgamation (RECPAM) method was used.

Results

RECPAM analysis showed that DCK and CHOP were most relevant variables for the identification of patients with different mortality risk, while hENT1 and CHOP were able to identify subgroups of patients with different disease progression risk. Conclusion: hENT1, CHOP, MRP1 and DCK appear correlated to PDAC, and this interaction might influence disease behavior.