Discrimination of metabolic profiles of pancreatic cancer from chronic pancreatitis by high-resolution magic angle spinning 1H nuclear magnetic resonance and principal components analysis

High-sensitivity deuterium metabolic MRI differentiates acute pancreatitis from pancreatic cancers in murine models

Deuterium metabolic imaging (DMI) is a promising tool for investigating a tumor's biology, and eventually contribute in cancer diagnosis and prognosis. In DMI, [6,6'-2H2]-glucose is taken up and metabolized by different tissues, resulting in the formation of HDO but also in an enhanced formation of [3,3'-2H2]-lactate at the tumor site as a result of the Warburg effect. Recent studies have shown DMI's suitability to highlight pancreatic cancer in murine models by [3,3'-2H2]-lactate formation; an important question is whether DMI can also differentiate between these tumors and pancreatitis. This differentiation is critical, as these two diseases are hard to distinguish today radiologically, but have very different prognoses requiring distinctive treatments. Recent studies have shown the limitations that hyperpolarized MRI faces when trying to distinguish these pancreatic diseases by monitoring the [1-13C1]-pyruvate→[1-13C1]-lactate conversion. In this work, we explore DMI's capability to achieve such differentiation. Initial tests used a multi-echo (ME) SSFP sequence, to identify any metabolic differences between tumor and acute pancreatitis models that had been previously elicited very similar [1-13C1]-pyruvate→[1-13C1]-lactate conversion rates. Although ME-SSFP provides approximately 5 times greater signal-to-noise ratio (SNR) than the standard chemical shift imaging (CSI) experiment used in DMI, no lactate signal was observed in the pancreatitis model. To enhance lactate sensitivity further, we developed a new, weighted-average, CSI-SSFP approach for DMI. Weighted-average CSI-SSFP improved DMI's SNR by another factor of 4 over ME-SSFP-a sensitivity enhancement that sufficed to evidence natural abundance 2H fat in abdominal images, something that had escaped the previous approaches even at ultrahigh (15.2 T) MRI fields. Despite these efforts to enhance DMI's sensitivity, no lactate signal could be detected in acute pancreatitis models (n = 10; [3,3'-2H2]-lactate limit of detection < 100 µM; 15.2 T). This leads to the conclusion that pancreatic tumors and acute pancreatitis may be clearly distinguished by DMI, based on their different abilities to generate deuterated lactate.

Unveiling the intricate causal nexus between pancreatic cancer and peripheral metabolites through a comprehensive bidirectional two-sample Mendelian randomization analysis

Aim: Pancreatic cancer (PC) is a devastating malignancy characterized by its aggressive nature and poor prognosis. However, the relationship of PC with peripheral metabolites remains not fully investigated. The study aimed to explore the causal linkage between PC and peripheral metabolite profiles. Methods: Employing publicly accessible genome-wide association studies (GWAS) data, we conducted a bidirectional two-sample Mendelian randomization (MR) analysis. The primary analysis employed the inverse-variance weighted (IVW) method. To address potential concerns about horizontal pleiotropy, we also employed supplementary methods such as maximum likelihood, weighted median, MR-Egger regression, and MR pleiotropy residual sum and outlier (MR-PRESSO). Results: We ascertained 20 genetically determined peripheral metabolites with causal linkages to PC while high-density lipoprotein (HDL) and very low-density lipoprotein (VLDL) particles accounted for the vast majority. Specifically, HDL particles exhibited an elevated PC risk while VLDL particles displayed an opposing pattern. The converse MR analysis underscored a notable alteration in 17 peripheral metabolites due to PC, including branch chain amino acids and derivatives of glycerophospholipid. Cross-referencing the bidirectional MR results revealed a reciprocal causation of PC and X-02269 which might form a self-perpetuating loop in PC development. Additionally, 1-arachidonoylglycerophosphocholine indicated a reduced PC risk and an increase under PC influence, possibly serving as a negative feedback regulator. Conclusion: Our findings suggest a complex interplay between pancreatic cancer and peripheral metabolites, with potential implications for understanding the etiology of pancreatic cancer and identifying novel early diagnosis and therapeutic targets. Moreover, X-02269 may hold a pivotal role in PC onset and progression.

Diagnostic Value of Salivary Amino Acid Levels in Cancer

This review analyzed 21 scientific papers on the determination of amino acids in various types of cancer in saliva. Most of the studies are on oral cancer (8/21), breast cancer (4/21), gastric cancer (3/21), lung cancer (2/21), glioblastoma (2/21) and one study on colorectal, pancreatic, thyroid and liver cancer. The amino acids alanine, valine, phenylalanine, leucine and isoleucine play a leading role in the diagnosis of cancer via the saliva. In an independent version, amino acids are rarely used; the authors combine either amino acids with each other or with other metabolites, which makes it possible to obtain high values of sensitivity and specificity. Nevertheless, a logical and complete substantiation of the changes in saliva occurring in cancer, including changes in salivary amino acid levels, has not yet been formed, which makes it important to continue research in this direction.

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.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part II: Applications to the Diagnosis and Prognostic Monitoring of Oral and Systemic Cancers

Human saliva offers many advantages over other biofluids regarding its use and value as a bioanalytical medium for the identification and prognostic monitoring of human diseases, mainly because its collection is largely non-invasive, is relatively cheap, and does not require any major clinical supervision, nor supervisory input. Indeed, participants donating this biofluid for such purposes, including the identification, validation and quantification of surrogate biomarkers, may easily self-collect such samples in their homes following the provision of full collection details to them by researchers. In this report, the authors have focused on the applications of metabolomics technologies to the diagnosis and progressive severity monitoring of human cancer conditions, firstly oral cancers (e.g., oral cavity squamous cell carcinoma), and secondly extra-oral (systemic) cancers such as lung, breast and prostate cancers. For each publication reviewed, the authors provide a detailed evaluation and critical appraisal of the experimental design, sample size, ease of sample collection (usually but not exclusively as whole mouth saliva (WMS)), their transport, length of storage and preparation for analysis. Moreover, recommended protocols for the optimisation of NMR pulse sequences for analysis, along with the application of methods and techniques for verifying and resonance assignments and validating the quantification of biomolecules responsible, are critically considered. In view of the authors’ specialisms and research interests, the majority of these investigations were conducted using NMR-based metabolomics techniques. The extension of these studies to determinations of metabolic pathways which have been pathologically disturbed in these diseases is also assessed here and reviewed. Where available, data for the monitoring of patients’ responses to chemotherapeutic treatments, and in one case, radiotherapy, are also evaluated herein. Additionally, a novel case study featured evaluates the molecular nature, levels and diagnostic potential of ¹H NMR-detectable salivary ‘acute-phase’ glycoprotein carbohydrate side chains, and/or their monomeric saccharide derivatives, as biomarkers for cancer and inflammatory conditions.

Cellular metabolism in pancreatic cancer as a tool for prognosis and treatment (Review)

Pancreatic cancer (PC) has one of the highest fatality rates and the currently available therapeutic options are not sufficient to improve its overall poor prognosis. In addition to insufficient effectiveness of anticancer treatments, the lack of clear early symptoms and early metastatic spread maintain the PC survival rates at a low level. Metabolic reprogramming is among the hallmarks of cancer and could be exploited for the diagnosis and treatment of PC. PC is characterized by its heterogeneity and, apart from molecular subtypes, the identification of metabolic subtypes in PC could aid in the development of more individualized therapeutic approaches and may lead to improved clinical outcomes. In addition to the deregulated utilization of glucose in aerobic glycolysis, PC cells can use a wide range of substrates, including branched‑chain amino acids, glutamine and lipids to fulfil their energy requirements, as well as biosynthetic needs. The tumor microenvironment in PC supports tumor growth, metastatic spread, treatment resistance and the suppression of the host immune response. Moreover, reciprocal interactions between cancer and stromal cells enhance their metabolic reprogramming. PC stem cells (PCSCs) with an increased resistance and distinct metabolic properties are associated with disease relapses and cancer spread, and represent another significant candidate for therapeutic targeting. The present review discusses the metabolic signatures observed in PC, a disease with a multifaceted and often transient metabolic landscape. In addition, the metabolic pathways utilized by PC cells, as well as stromal cells are discussed, providing examples of how they could present novel targets for therapeutic interventions and elaborating on how interactions between the various cell types affect their metabolism. Furthermore, the importance of PCSCs is discussed, focusing specifically on their metabolic adaptations.

Alterations in Serum-Free Amino Acid Profiles in Childhood Asthma

Asthma often begins in childhood, although making an early diagnosis is difficult. Clinical manifestations, the exclusion of other causes of bronchial obstruction, and responsiveness to anti-inflammatory therapy are the main tool of diagnosis. However, novel, precise, and functional biochemical markers are needed in the differentiation of asthma phenotypes, endotypes, and creating personalized therapy. The aim of the study was to search for metabolomic-based asthma biomarkers among free amino acids (AAs). A wide panel of serum-free AAs in asthmatic children, covering both proteinogenic and non-proteinogenic AAs, were analyzed. The examination included two groups of individuals between 3 and 18 years old: asthmatic children and the control group consisted of children with neither asthma nor allergies. High-performance liquid chromatography combined with tandem mass spectrometry (LC-MS/MS technique) was used for AA measurements. The data were analyzed by applying uni- and multivariate statistical tests. The obtained results indicate the decreased serum concentration of taurine, L-valine, DL-β-aminoisobutyric acid, and increased levels of ƴ-amino-n-butyric acid and L-arginine in asthmatic children when compared to controls. The altered concentration of these AAs can testify to their role in the pathogenesis of childhood asthma. The authors' results should contribute to the future introduction of new diagnostic markers into clinical practice.

Novel Metabolomics Serum Biomarkers for Pancreatic Ductal Adenocarcinoma by the Comparison of Pre-, Postoperative and Normal Samples

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive human malignancies. The metabolomic approaches are developed to discover the novel biomarkers of PDAC. Methods: 550 preoperative, postoperative PDAC and normal controls (NCs) serums were employed to characterize metabolic alterations in training and validation sets by LC-MS. Results: The results of PLS-DA analysis indicated that three groups could be distinguished clearly and the post-PDAC group is adjacent to a normal group as compared with pre-PDAC group. Further results showed that histidinyl-lysine significantly increased whereas docosahexaenoic acid and LysoPC (14:0) decreased in pre-PDAC patients as compared with NCs. And these three markers had a significant tendency to recover after tumor resection. The validation set results revealed that for CA19-9 negative patients, 92.3% (12/13) of them can be screened using these three metabolites. The combination of these markers could significantly improve the diagnostic performance for PDAC, with higher sensitivity (0.93), specificity (0.92) and AUC (0.97). Moreover, network and pathways analyses explored the latent relationship among differential metabolites. The glycerolipid metabolism and primary bile acid synthesis showed variation in network and pathway analysis. Conclusions: These three markers combined with CA199 displayed high sensitivity and specificity for detecting PDAC patients from NCs. The results indicated that these three metabolites could be regarded as potential biomarkers to distinguish PDAC from NCs.

UPLC‑MS/MS‑based metabolomic characterization and comparison of pancreatic adenocarcinoma tissues using formalin‑fixed, paraffin‑embedded and optimal cutting temperature‑embedded materials

The purpose of the present study was to compare metabolites from formalin-fixed and paraffin-embedded (FFPE) pancreatic tissue blocks with those identified in optimal cutting temperature (OCT)-embedded pancreatic tissue blocks. Thus, ultra-performance liquid chromatograph-mass spectrometry/mass spectrometry-based metabolic profiling was performed in paired frozen (n=13) and FFPE (n=13) human pancreatic adenocarcinoma tissue samples, in addition to their benign counterparts. A total of 206 metabolites were identified in both OCT-embedded and FFPE tissue samples. The method feasibility was confirmed through reproducibility and a consistency assessment. Partial least-squares discriminant analysis and heatmap analysis reliably distinguished tumor and normal tissue phenotypes. The expression of 10 compounds, including N-acetylaspartate and creatinine, was significantly different in both OCT-embedded and FFPE tumor samples. These ten compounds may be viable candidate biomarkers of malignant pancreatic tissues. The super-categories to which they belonged exhibited no significant differences between FFPE and OCT-embedded samples. Furthermore, purine, arginine and proline, and pyrimidine metabolism used a shared pathway found in both OCT-embedded and FFPE tissue samples. These results supported the notion that metabolomic data acquired from FFPE pancreatic cancer specimens are reliable for use in retrospective and clinical studies.

Disruption of Broad Epigenetic Domains in PDAC Cells by HAT Inhibitors

The spreading of epigenetic domains has emerged as a distinguishing epigenomic phenotype for diverse cell types. In particular, clusters of H3K27ac- and H3K4me3-marked elements, referred to as super-enhancers, and broad H3K4me3 domains, respectively, have been linked to cell identity and disease states. Here, we characterized the broad domains from different pancreatic ductal adenocarcinoma (PDAC) cell lines that represent distinct histological grades. Our integrative genomic analysis found that human derived cell line models for distinct PDAC grades exhibit characteristic broad epigenetic features associated with gene expression patterns that are predictive of patient prognosis and provide insight into pancreatic cancer cell identity. In particular, we find that genes marked by overlapping Low-Grade broad domains correspond to an epithelial phenotype and hold potential as markers for patient stratification. We further utilize ChIP-seq to compare the effects of histone acetyltransferase (HAT) inhibitors to detect global changes in histone acetylation and methylation levels. We found that HAT inhibitors impact certain broad domains of pancreatic cancer cells. Overall, our results reveal potential roles for broad domains in cells from distinct PDAC grades and demonstrate the plasticity of particular broad epigenomic domains to epigenetic inhibitors.

NMR-based metabolic profiling of urine, serum, fecal, and pancreatic tissue samples from the Ptf1a-Cre; LSL-KrasG12D transgenic mouse model of pancreatic cancer

Pancreatic cancer is the third leading cause of cancer deaths in the United States with more than 53,000 expected to be diagnosed with the disease in 2018. The median survival time after diagnosis is four to six months. The poor survival statistics are due in part to the fact that pancreatic cancer is typically asymptomatic until it reaches advanced stages of the disease. Although surgical resection provides the best chance of survival, pancreatic cancer is rarely detected when surgery is still possible due, in part, to lack of effective biomarkers for early detection. The goal of the research reported here was to determine if it was possible to identify metabolic biomarkers for detection of pre-cancerous pancreatic intraepithelial neoplasia (PanIN) that precede pancreatic adenocarcinoma. The transgenic Ptf1a-Cre; LSL-KrasG12D mouse strain was used as a model of pancreatic cancer progression. Nuclear magnetic resonance (NMR) spectroscopy was employed to compare metabolic profiles of urine, sera, fecal extracts, and pancreatic tissue extracts collected from control and study mice aged 5, 11, and 15 months, including 47 mice with tumors. We were able to identify the following potential biomarkers: decreased 3-indoxylsulfate, benzoate and citrate in urine, decreased glucose, choline, and lactate in blood, and decreased phenylalanine and benzoate and increased acetoin in fecal extracts. Potential biomarkers were validated by p-values, PLS-DA VIP scores, and accuracies based on area under ROC curve analyses. Essentially, all of the metabolic profiling changes could be explained as being associated with the consequences of bicarbonate wasting caused by a complete substitution of the normal pancreatic acinar tissue by tissue entirely composed of PanIN. Given the nature of the mouse model used here, our results indicate that it may be possible to use NMR-based metabolic profiling to identify biomarkers for detection of precancerous PanIN that immediately precede pancreatic cancer.

Integrative analysis of indirect calorimetry and metabolomics profiling reveals alterations in energy metabolism between fed and fasted pigs

Background

Fasting is a simple metabolic strategy that is used to estimate the maintenance energy requirement where the energy supply for basic physiological functions is provided by the mobilization of body reserves. However, the underlying metabolic components of maintenance energy expenditure are not clear. This study investigated the differences in heat production (HP), respiratory quotient (RQ) and plasma metabolites in pigs in the fed and fasted state, using the techniques of indirect calorimetry and metabolomics.

Methods

Nine barrows (45.2 ± 1.7 kg BW) were fed corn-soybean based meal diets and were kept in metabolism crates for a period of 14 d. After 7 d adaptation, pigs were transferred to respiratory chambers to determine HP and RQ based on indirect calorimetry. Pigs were fed the diet at 2,400 kJ ME/(kg BW^0.6·d) during d 8 to 12. The last 2 d were divided into 24 h fasting and 48 h fasting treatment, respectively. Plasma samples of each pig were collected from the anterior vena cava during the last 3 d (1 d while pigs were fed and 2 d during which they were fasted). The metabolites of plasma were determined by high-resolution mass spectrometry using a metabolomics approach.

Results

Indirect calorimetry analysis revealed that HP and RQ were no significant difference between 24 h fasting and 48 h fasting, which were lower than those of fed state (P <  0.01). The nitrogen concentration of urine tended to decrease with fasting (P = 0.054). Metabolomics analysis between the fed and fasted state revealed differences in 15 compounds, most of which were not significantly different between 24 h fasting and 48 h fasting. Identified compounds were enriched in metabolic pathways related to linoleic acid metabolism, amino acid metabolism, sphingolipid metabolism, and pantothenate and CoA biosynthesis.

Conclusion

These results suggest that the decreases in HP and RQ of growing pigs under fasting conditions were associated with the alterations of linoleic acid metabolism and amino acid metabolism. The integrative analysis also revealed that growing pigs under a 24-h fasting were more appropriate than a 48-h fasting to investigate the metabolic components of maintenance energy expenditure.

Non-invasively predicting differentiation of pancreatic cancer through comparative serum metabonomic profiling

BACKGROUND

The differentiation of pancreatic ductal adenocarcinoma (PDAC) could be associated with prognosis and may influence the choices of clinical management. No applicable methods could reliably predict the tumor differentiation preoperatively. Thus, the aim of this study was to compare the metabonomic profiling of pancreatic ductal adenocarcinoma with different differentiations and assess the feasibility of predicting tumor differentiations through metabonomic strategy based on nuclear magnetic resonance spectroscopy.

METHODS

By implanting pancreatic cancer cell strains Panc-1, Bxpc-3 and SW1990 in nude mice in situ, we successfully established the orthotopic xenograft models of PDAC with different differentiations. The metabonomic profiling of serum from different PDAC was achieved and analyzed by using 1H nuclear magnetic resonance (NMR) spectroscopy combined with the multivariate statistical analysis. Then, the differential metabolites acquired were used for enrichment analysis of metabolic pathways to get a deep insight.

RESULTS

An obvious metabonomic difference was demonstrated between all groups and the pattern recognition models were established successfully. The higher concentrations of amino acids, glycolytic and glutaminolytic participators in SW1990 and choline-contain metabolites in Panc-1 relative to other PDAC cells were demonstrated, which may be served as potential indicators for tumor differentiation. The metabolic pathways and differential metabolites identified in current study may be associated with specific pathways such as serine-glycine-one-carbon and glutaminolytic pathways, which can regulate tumorous proliferation and epigenetic regulation.

CONCLUSION

The NMR-based metabonomic strategy may be served as a non-invasive detection method for predicting tumor differentiation preoperatively.

Serum Metabolomic Profiles for Human Pancreatic Cancer Discrimination

This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresis−mass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel–Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946–0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650–1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.

Distinguishing Benign from Malignant Pancreatic and Periampullary Lesions Using Combined Use of ¹H-NMR Spectroscopy and Gas Chromatography-Mass Spectrometry

Previous work demonstrated that serum metabolomics can distinguish pancreatic cancer from benign disease. However, in the clinic, non-pancreatic periampullary cancers are difficult to distinguish from pancreatic cancer. Therefore, to test the clinical utility of this technology, we determined whether any pancreatic and periampullary adenocarcinoma could be distinguished from benign masses and biliary strictures. Sera from 157 patients with malignant and benign pancreatic and periampullary lesions were analyzed using proton nuclear magnetic resonance (¹H-NMR) spectroscopy and gas chromatography–mass spectrometry (GC-MS). Multivariate projection modeling using SIMCA-P+ software in training datasets (n = 80) was used to generate the best models to differentiate disease states. Models were validated in test datasets (n = 77). The final ¹H-NMR spectroscopy and GC-MS metabolomic profiles consisted of 14 and 18 compounds, with AUROC values of 0.74 (SE 0.06) and 0.62 (SE 0.08), respectively. The combination of ¹H-NMR spectroscopy and GC-MS metabolites did not substantially improve this performance (AUROC 0.66, SE 0.08). In patients with adenocarcinoma, glutamate levels were consistently higher, while glutamine and alanine levels were consistently lower. Pancreatic and periampullary adenocarcinomas can be distinguished from benign lesions. To further enhance the discriminatory power of metabolomics in this setting, it will be important to identify the metabolomic changes that characterize each of the subclasses of this heterogeneous group of cancers.

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.

Metabonomic changes from pancreatic intraepithelial neoplasia to pancreatic ductal adenocarcinoma in tissues from rats

Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors and is difficult to diagnose in the early phase. This study was aimed at obtaining the metabolic profiles and characteristic metabolites of pancreatic intraepithelial neoplasia (PanIN) and PDAC tissues from Sprague-Dawley (SD) rats to establish metabonomic methods used in the early diagnosis of PDAC. In the present study, the animal models were established by embedding 7,12-dimethylbenzanthracene (DMBA) in the pancreas of SD rats to obtain PanIN and PDAC tissues. After the preprocessing of tissues, (1) H nuclear magnetic resonance (NMR) spectroscopy combined with multivariate and univariate statistical analysis was applied to identify the potential metabolic signatures and the corresponding metabolic pathways. Pattern recognition models were successfully established and differential metabolites, including glucose, amino acids, carboxylic acids and coenzymes, were screened out. Compared with the control, the trends in the variation of several metabolites were similar in both PanIN and PDAC. Kynurenate and methionine levels were elevated in PanIN but decreased in PDAC, thus, could served as biomarkers to distinguish PanIN from PDAC. Our results suggest that NMR-based techniques combined with multivariate statistical analysis can distinguish the metabolic differences among PanIN, PDAC and normal tissues, and, therefore, present a promising approach for physiopathologic metabolism investigations and early diagnoses of PDAC.

Pancreatic cancer screening using a multiplatform human serum metabolomics system

Aim: To examine a novel screening method for pancreatic cancer involving gas chromatography/mass spectrometry and liquid chromatography/mass spectrometry-based metabolomics analysis. Materials & methods: Sera from pancreatic cancer patients (n = 59) and healthy volunteers (n = 59) were allocated to the training set or validation set. Serum metabolome analysis was carried out using our multiplatform metabolomics system. A diagnostic model was constructed using a two-phase screening method that was newly advocated. Results: When the training set was used, the constructed diagnostic model exhibited high sensitivity (100%) and specificity (80%) for pancreatic cancer. When the validation set was used, the model displayed high sensitivity (84.1%) and specificity (84.1%). Conclusion: We successfully developed a diagnostic model for pancreatic cancer using a multiplatform serum metabolomics system.

MRI with hyperpolarised [1-13C]pyruvate detects advanced pancreatic preneoplasia prior to invasive disease in a mouse model

OBJECTIVES

Pancreatic cancer (PCa) is treatable by surgery when detected at an early stage. Non-invasive imaging methods able to detect both established tumours and their precursor lesions are needed to select patients for surgery. We investigated here whether pancreatic preneoplasia could be detected prior to the development of invasive cancers in genetically engineered mouse models of PCa using metabolic imaging.

DESIGN

The concentrations of alanine and lactate and the activities of lactate dehydrogenase (LDH) and alanine aminotransferase (ALT) were measured in extracts prepared from the pancreas of animals at different stages of disease progression; from pancreatitis, through tissue with predominantly low-grade and then high-grade pancreatic intraepithelial neoplasia and then tumour. (13)C magnetic resonance spectroscopic imaging ((13)C-MRSI) was used to measure non-invasively changes in (13)C labelling of alanine and lactate with disease progression, following injection of hyperpolarised [1-(13)C]pyruvate.

RESULTS

Progressive decreases in the alanine/lactate concentration ratio and ALT/LDH activity ratio with disease progression were accompanied by a corresponding decrease in the [1-(13)C]alanine/[1-(13)C]lactate signal ratio observed in (13)C-MRSI images of the pancreas.

CONCLUSIONS

Metabolic imaging with hyperpolarised [1-(13)C]pyruvate enables detection and monitoring of the progression of PCa precursor lesions. Translation of this MRI technique to the clinic has the potential to improve the management of patients at high risk of developing PCa.

Distinct serum metabolomics profiles associated with malignant progression in the KrasG12D mouse model of pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer deaths worldwide with less than a 6% 5-year survival rate. PDAC is associated with poor prognosis based on the late stage diagnosis of the disease. Current diagnostic tests lack the sensitivity and specificity to identify markers of early staging. Metabolomics has provided biomarkers for various diseases, stressors, and environmental exposures. In this study we utilized the p48-Cre/LSL-Kras^G12D mouse model with age-matched wild type mice. This model shows malignant progression to PDAC analogous to the human disease stages via early and late pancreatic intra-epithelial neoplasia (PanIN) lesions.

Results

Serum was collected from mice with early PanIN lesions (at 3-5 months) and with late PanIN or invasive PDAC lesions (13-16 months), as determined by histopathology. Metabolomics analysis of the serum samples was conducted through UPLC-TOFMS (Ultra Performance Liquid Chromatography coupled to Time-of-flight Mass Spectrometry). Multivariate data analysis revealed distinct metabolic patterns in serum samples collected during malignant progression towards invasive PDAC. Animals with early or late stage lesions were distinguished from their respective controls with 82.1% and 81.5% accuracy, respectively. This also held up for randomly selected subgroups in the late stage lesion group that showed less variability between animals. One of the metabolites, citrate, was validated through tandem mass spectrometry and showed increased levels in serum with disease progression. Furthermore, serum metabolite signatures from animals with early stage lesions identified controls and animals with late stage lesions with 81.5% accuracy (p<0.01) and vice-versa with 73.2% accuracy (p<0.01).

Conclusions

We conclude that metabolomics analysis of serum samples can identify the presence of early and late stage pancreatic cancer.

Plasma metabolite biomarkers for the detection of pancreatic cancer

Patients with pancreatic cancer (PC) are usually diagnosed at late stages, when the disease is nearly incurable. Sensitive and specific markers are critical for supporting diagnostic and therapeutic strategies. The aim of this study was to use a metabonomics approach to identify potential plasma biomarkers that can be further developed for early detection of PC. In this study, plasma metabolites of newly diagnosed PC patients (n = 100) and age- and gender-matched controls (n = 100) from Connecticut (CT), USA, and the same number of cases and controls from Shanghai (SH), China, were profiled using combined gas and liquid chromatography mass spectrometry. The metabolites consistently expressed in both CT and SH samples were used to identify potential markers, and the diagnostic performance of the candidate markers was tested in two sample sets. A diagnostic model was constructed using a panel of five metabolites including glutamate, choline, 1,5-anhydro-d-glucitol, betaine, and methylguanidine, which robustly distinguished PC patients in CT from controls with high sensitivity (97.7%) and specificity (83.1%) (area under the receiver operating characteristic curve [AUC] = 0.943, 95% confidence interval [CI] = 0.908–0.977). This panel of metabolites was then tested with the SH data set, yielding satisfactory accuracy (AUC = 0.835; 95% CI = 0.777–0.893), with a sensitivity of 77.4% and specificity of 75.8%. This model achieved a sensitivity of 84.8% in the PC patients at stages 0, 1, and 2 in CT and 77.4% in the PC patients at stages 1 and 2 in SH. Plasma metabolic signatures show promise as biomarkers for early detection of PC.

HR-MAS MRS of the pancreas reveals reduced lipid and elevated lactate and taurine associated with early pancreatic cancer

The prognosis for patients with pancreatic cancer is extremely poor, as evidenced by the disease's five-year survival rate of ~5%. New approaches are therefore urgently needed to improve detection, treatment, and monitoring of pancreatic cancer. MRS-detectable metabolic changes provide useful biomarkers for tumor detection and response-monitoring in other cancers. The goal of this study was to identify MRS-detectable biomarkers of pancreatic cancer that could enhance currently available imaging approaches. We used (1) H high-resolution magic angle spinning MRS to probe metabolite levels in pancreatic tissue samples from mouse models and patients. In mice, the levels of lipids dropped significantly in pancreata with lipopolysaccharide-induced inflammation, in pancreata with pre-cancerous metaplasia (4 week old p48-Cre;LSL-Kras(G12D) mice), and in pancreata with pancreatic intraepithelial neoplasia, which precedes invasive pancreatic cancer (8 week old p48-Cre LSL-Kras(G12D) mice), to 26 ± 19% (p = 0.03), 19 ± 16% (p = 0.04), and 26 ± 10% (p = 0.05) of controls, respectively. Lactate and taurine remained unchanged in inflammation and in pre-cancerous metaplasia but increased significantly in pancreatic intraepithelial neoplasia to 266 ± 61% (p = 0.0001) and 999 ± 174% (p < 0.00001) of controls, respectively. Importantly, analysis of patient biopsies was consistent with the mouse findings. Lipids dropped in pancreatitis and in invasive cancer biopsies to 29 ± 15% (p = 0.01) and 26 ± 38% (p = 0.02) of normal tissue. In addition, lactate and taurine levels remained unchanged in inflammation but rose in tumor samples to 244 ± 155% (p = 0.02) and 188 ± 67% (p = 0.02), respectively, compared with normal tissue. Based on these findings, we propose that a drop in lipid levels could serve to inform on pancreatitis and cancer-associated inflammation, whereas elevated lactate and taurine could serve to identify the presence of pancreatic intraepithelial neoplasia and invasive tumor. Our findings may help enhance current imaging methods to improve early pancreatic cancer detection and monitoring.

Analysis of metabolic characteristics in a rat model of chronic pancreatitis using high-resolution magic-angle spinning nuclear magnetic resonance spectroscopy

Pathological and metabolic alterations co-exist and co-develop in the progression of chronic pancreatitis (CP). The aim of the present study was to investigate the metabolic characteristics and disease severity of a rat model of CP in order to determine associations in the observed pathology and the metabolites of CP using high-resolution magic-angle spinning nuclear magnetic resonance spectroscopy (HR-MAS NMR). Wistar rats (n=36) were randomly assigned into 6 groups (n=6 per group). CP was established by administering dibutyltin dichloride solution into the tail vein. After 0, 7, 14, 21, 28 and 35 days, the pancreatic tissues were collected for pathological scoring or for HR-MAS NMR. Correlation analyses between the major pathological scores and the integral areas of the major metabolites were determined. The most representative metabolites, aspartate, betaine and fatty acids, were identified as possessing the greatest discriminatory significance. The Spearman’s rank correlation coefficients between the pathology and metabolites of the pancreatic tissues were as follows: Betaine and fibrosis, 0.454 (P=0.044); betaine and inflammatory cell infiltration, 0.716 (P=0.0001); aspartate and fibrosis, −0.768 (P=0.0001); aspartate and inflammatory cell infiltration, −0.394 (P=0.085); fatty acid and fibrosis, −0.764 (P=0.0001); and fatty acid and inflammatory cell infiltration, −0.619 (P=0.004). The metabolite betaine positively correlated with fibrosis and inflammatory cell infiltration in CP. In addition, aspartate negatively correlated with fibrosis, but exhibited no significant correlation with inflammatory cell infiltration. Furthermore, the presence of fatty acids negatively correlated with fibrosis and inflammatory cell infiltration in CP. HR-MAS NMR may be used to analyze metabolic characteristics in a rat model of different degrees of chronic pancreatitis.

1H NMR global metabolic phenotyping of acute pancreatitis in the emergency unit

We have investigated the urinary and plasma metabolic phenotype of acute pancreatitis (AP) patients presenting to the emergency room at a single center London teaching hospital with acute abdominal pain using (1)H NMR spectroscopy and multivariate modeling. Patients were allocated to either the AP (n = 15) or non-AP patients group (all other causes of abdominal pain, n = 21) on the basis of the national guidelines. Patients were assessed for three clinical outcomes: (1) diagnosis of AP, (2) etiology of AP caused by alcohol consumption and cholelithiasis, and (3) AP severity based on the Glasgow score. Samples from AP patients were characterized by high levels of urinary ketone bodies, glucose, plasma choline and lipid, and relatively low levels of urinary hippurate, creatine and plasma-branched chain amino acids. AP could be reliably identified with a high degree of sensitivity and specificity (OPLS-DA model R(2) = 0.76 and Q(2)Y = 0.59) using panel of discriminatory biomarkers consisting of guanine, hippurate and creatine (urine), and valine, alanine and lipoproteins (plasma). Metabolic phenotyping was also able to distinguish between cholelithiasis and colonic inflammation among the heterogeneous non-AP group. This work has demonstrated that combinatorial biomarkers have a strong diagnostic and prognostic potential in AP with relevance to clinical decision making in the emergency unit.

Mass spectrometry-based metabolic profiling of gemcitabine-sensitive and gemcitabine-resistant pancreatic cancer cells

OBJECTIVES

Gemcitabine resistance (GR) is one of the critical issues for therapy for pancreatic cancer, but the mechanism still remains unclear. Our aim was to increase the understanding of GR by metabolic profiling approach.

METHODS

To establish GR cells, 2 human pancreatic cancer cell lines, SUIT-2 and CAPAN-1, were exposed to increasing concentration of gemcitabine. Both parental and chemoresistant cells obtained by this treatment were subjected to metabolic profiling based on liquid chromatography-mass spectrometry.

RESULTS

Multivariate statistical analyses, both principal component analysis and orthogonal partial least squares discriminant analysis, distinguished metabolic signature of responsiveness and resistance to gemcitabine in both SUIT-2 and CAPAN-1 cells. Among significantly different (P < 0.005) metabolite peaks between parental and GR cells, we identified metabolites related to several metabolic pathways such as amino acid, nucleotide, energy, cofactor, and vitamin pathways. Decreases in glutamine and proline levels as well as increases in aspartate, hydroxyproline, creatine, and creatinine levels were observed in chemoresistant cells from both cell lines.

CONCLUSIONS

These results suggest that metabolic profiling can isolate distinct features of pancreatic cancer in the metabolome of gemcitabine-sensitive and GR cells. These findings may contribute to the biomarker discovery and an enhanced understanding of GR in pancreatic cancer.

Metabolome analysis for discovering biomarkers of gastroenterological cancer

Improvements in analytical technologies have made it possible to rapidly determine the concentrations of thousands of metabolites in any biological sample, which has resulted in metabolome analysis being applied to various types of research, such as clinical, cell biology, and plant/food science studies. The metabolome represents all of the end products and by-products of the numerous complex metabolic pathways operating in a biological system. Thus, metabolome analysis allows one to survey the global changes in an organism's metabolic profile and gain a holistic understanding of the changes that occur in organisms during various biological processes, e.g., during disease development. In clinical metabolomic studies, there is a strong possibility that differences in the metabolic profiles of human specimens reflect disease-specific states. Recently, metabolome analysis of biofluids, e.g., blood, urine, or saliva, has been increasingly used for biomarker discovery and disease diagnosis. Mass spectrometry-based techniques have been extensively used for metabolome analysis because they exhibit high selectivity and sensitivity during the identification and quantification of metabolites. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Furthermore, the findings of studies that attempted to discover biomarkers of gastroenterological cancer are also outlined. Finally, we discuss metabolome analysis-based disease diagnosis.

MRS-based Metabolomics in Cancer Research

Metabolomics is a relatively new technique that is gaining importance very rapidly. MRS-based metabolomics, in particular, is becoming a useful tool in the study of body fluids, tissue biopsies and whole organisms. Advances in analytical techniques and data analysis methods have opened a new opportunity for such technology to contribute in the field of diagnostics. In the MRS approach to the diagnosis of disease, it is important that the analysis utilizes all the essential information in the spectra, is robust, and is non-subjective. Although some of the data analytic methods widely used in chemical and biological sciences are sketched, a more extensive discussion is given of a 5-stage Statistical Classification Strategy. This proposes powerful feature selection methods, based on, for example, genetic algorithms and novel projection techniques. The applications of MRS-based metabolomics in breast cancer, prostate cancer, colorectal cancer, pancreatic cancer, hepatobiliary cancers, gastric cancer, and brain cancer have been reviewed. While the majority of these applications relate to body fluids and tissue biopsies, some in vivo applications have also been included. It should be emphasized that the number of subjects studied must be sufficiently large to ensure a robust diagnostic classification. Before MRS-based metabolomics can become a widely used clinical tool, however, certain challenges need to be overcome. These include manufacturing user-friendly commercial instruments with all the essential features, and educating physicians and medical technologists in the acquisition, analysis, and interpretation of metabolomics data.

High-resolution magic angle spinning (1)H magnetic resonance spectroscopy detects choline as a biomarker in a swine obstructive chronic pancreatitis model at an early stage

Chronic pancreatitis (CP) is a progressive inflammatory and fibrotic disease of the pancreas which encompasses a variety of clinical syndromes ranging from mild to life-threatening complications. Metabolomics has increasingly been applied to identify biomarkers for disease diagnosis with particular interest in diseases at an early stage. In this study, we tested a swine obstructive CP model by subtotal ligation of the main pancreatic duct, and the metabolic profiles of the Bama miniature swine pancreas were investigated using high-resolution magic angle spinning proton magnetic resonance spectroscopy (HR MAS (1)H MRS) combined with principal components analysis (PCA). Increases in lactate and choline for mild CP and decreases in glycerophosphocholine, phosphocholine, betaine and glycine were observed from normal pancreas to mild, moderate and severe CP. PCA results showed visual separations among the groups. The increase of choline at an early stage of CP and the decrease of glycerophosphocholine, phosphocholine, betaine and glycine reveal the pathogenesis of CP at a molecular level. The MRS results presented here demonstrate the potential of metabolic profiles in discriminating a normal pancreas from different stages of CP, which may be used to achieve CP early diagnosis and timely intervention to prevent irreversible destruction of the pancreas.

Metabolic system alterations in pancreatic cancer patient serum: potential for early detection

Background

The prognosis of pancreatic cancer (PC) is one of the poorest among all cancers, due largely to the lack of methods for screening and early detection. New biomarkers for identifying high-risk or early-stage subjects could significantly impact PC mortality. The goal of this study was to find metabolic biomarkers associated with PC by using a comprehensive metabolomics technology to compare serum profiles of PC patients to healthy control subjects.

Methods

A non-targeted metabolomics approach based on high-resolution, flow-injection Fourier transform ion cyclotron resonance mass spectrometry (FI-FTICR-MS) was used to generate comprehensive metabolomic profiles containing 2478 accurate mass measurements from the serum of Japanese PC patients (n=40) and disease-free subjects (n=50). Targeted flow-injection tandem mass spectrometry (FI-MS/MS) assays for specific metabolic systems were developed and used to validate the FI-FTICR-MS results. A FI-MS/MS assay for the most discriminating metabolite discovered by FI-FTICR-MS (PC-594) was further validated in two USA Caucasian populations; one comprised 14 PCs, six intraductal papillary mucinous neoplasims (IPMN) and 40 controls, and a second comprised 1000 reference subjects aged 30 to 80, which was used to create a distribution of PC-594 levels among the general population.

Results

FI-FTICR-MS metabolomic analysis showed significant reductions in the serum levels of metabolites belonging to five systems in PC patients compared to controls (all p<0.000025). The metabolic systems included 36-carbon ultra long-chain fatty acids, multiple choline-related systems including phosphatidylcholines, lysophosphatidylcholines and sphingomyelins, as well as vinyl ether-containing plasmalogen ethanolamines. ROC-AUCs based on FI-MS/MS of selected markers from each system ranged between 0.93 ±0.03 and 0.97 ±0.02. No significant correlations between any of the systems and disease-stage, gender, or treatment were observed. Biomarker PC-594 (an ultra long-chain fatty acid), was further validated using an independently-collected US Caucasian population (blinded analysis, n=60, p=9.9E-14, AUC=0.97 ±0.02). PC-594 levels across 1000 reference subjects showed an inverse correlation with age, resulting in a drop in the AUC from 0.99 ±0.01 to 0.90 ±0.02 for subjects aged 30 to 80, respectively. A PC-594 test positivity rate of 5.0% in low-risk reference subjects resulted in a PC sensitivity of 87% and a significant improvement in net clinical benefit based on decision curve analysis.

Conclusions

The serum metabolome of PC patients is significantly altered. The utility of serum metabolite biomarkers, particularly PC-594, for identifying subjects with elevated risk of PC should be further investigated.

A novel serum metabolomics-based diagnostic approach to pancreatic cancer

BACKGROUND

To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer.

METHODS

Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study.

RESULTS

In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers.

CONCLUSIONS

Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis.

IMPACT

It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis.

Urine metabonomic profiling of a female adolescent with PIT-1 mutation before and during growth hormone therapy: insights into the metabolic effects of growth hormone

OBJECTIVE

Growth hormone (GH) is a protein hormone with important roles in growth and metabolism. The objective of this study was to investigate the metabolism of a human subject with severe GH deficiency (GHD) due to a PIT-1 gene mutation and the metabolic effects of GH therapy using Nuclear Magnetic Resonance (NMR)-based metabonomics. NMR-based metabonomics is a platform that allows the metabolic profile of biological fluids such as urine to be recorded, and any alterations in the profile modulated by GH can potentially be detected.

DESIGN

Urine samples were collected from a female subject with severe GHD before, during and after GH therapy, and from healthy age- and sex-matched controls and analysed with NMR-based metabonomics.

SETTING

The samples were collected at a hospital and the study was performed at a research facility.

PARTICIPANTS

We studied a 17 year old female adolescent with severe GHD secondary to PIT-1 gene mutation who had reached final adult height and who had ceased GH therapy for over 3 years. The subject was subsequently followed for 5 years with and without GH therapy. Twelve healthy age-matched female subjects acted as control subjects.

INTERVENTION

The GH-deficient subject re-commenced GH therapy at a dose of 1 mg/day to normalise serum IGF-1 levels.

MAIN OUTCOME MEASURES

Urine metabolic profiles were recorded using NMR spectroscopy and analysed with multivariate statistics to distinguish the profiles at different time points and identify significant metabolites affected by GH therapy.

RESULTS

NMR-based metabonomics revealed that the metabolic profile of the GH-deficient subject altered with GH therapy and that her profile was different from healthy controls before, and during withdrawal of GH therapy.

CONCLUSION

This study illustrates the potential use of NMR-based metabonomics for monitoring the effects of GH therapy on metabolism by profiling the urine of GH-deficient subjects. Further controlled studies in larger numbers of GH-deficient subjects are required to determine the clinical benefits of NMR-based metabonomics in subjects receiving GH therapy.

Pancreatic ductal adenocarcinoma is associated with a distinct urinary metabolomic signature

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with poor prognosis in part due to the lack of early detection and screening methods. Metabolomics provides a means for noninvasive screening of tumor-associated perturbations in cellular metabolism.

METHODS

Urine samples of PDAC patients (n = 32), healthy age and gender-matched controls (n = 32), and patients with benign pancreatic conditions (n = 25) were examined using (1)H-NMR spectroscopy. Targeted profiling of spectra permitted quantification of 66 metabolites. Unsupervised (principal component analysis, PCA) and supervised (orthogonal partial-least squares discriminant analysis, OPLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra using SIMCA-P(+) (version 12, Umetrics, Sweden).

RESULTS

Clear distinction between PDAC and controls was noted when using OPLS-DA. Significant differences in metabolite concentrations between cancers and controls (p < 0.001) were noted. Model parameters for both goodness of fit, and predictive capability were high (R (2) = 0.85; Q (2) = 0.59, respectively). Internal validation methods were used to confirm model validity. Sensitivity and specificity of the multivariate OPLS-DA model were summarized using a receiver operating characteristics (ROC) curve, with an area under the curve (AUROC) = 0.988, indicating strong predictive power. Preliminary analysis revealed an AUROC = 0.958 for the model of benign pancreatic disease compared with PDAC, and suggest that the cancer-associated metabolomic signature dissipates following RO resection.

CONCLUSIONS

Urinary metabolomics detected distinct differences in the metabolic profiles of pancreatic cancer compared with healthy controls and benign pancreatic disease. These preliminary results suggest that metabolomic approaches may facilitate discovery of novel pancreatic cancer biomarkers.

The cancer exome generated by alternative mRNA splicing dilutes predicted HLA class I epitope density

Several studies have shown that cancers actively regulate alternative splicing. Altered splicing mechanisms in cancer lead to cancer-specific transcripts different from the pool of transcripts occurring only in healthy tissue. At the same time, altered presentation of HLA class I epitopes is frequently observed in various types of cancer. Down-regulation of genes related to HLA class I antigen processing has been observed in several cancer types, leading to fewer HLA class I antigens on the cell surface. Here, we use a peptidome wide analysis of predicted alternative splice forms, based on a publicly available database, to show that peptides over-represented in cancer splice variants comprise significantly fewer predicted HLA class I epitopes compared to peptides from normal transcripts. Peptides over-represented in cancer transcripts are in the case of the three most common HLA class I supertype representatives consistently found to contain fewer predicted epitopes compared to normal tissue. We observed a significant difference in amino acid composition between protein sequences associated with normal versus cancer tissue, as transcripts found in cancer are enriched with hydrophilic amino acids. This variation contributes to the observed significant lower likelihood of cancer-specific peptides to be predicted epitopes compared to peptides found in normal tissue.

An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer

OBJECTIVE

The present study was designed to search for potential diagnostic biomarkers in the serum of colorectal cancer (CRC).

BACKGROUND

CRC is the third most common cancer worldwide, and its prognosis is poor at early stages. A panel of novel biomarkers is urgently needed for early diagnosis of CRC.

METHODS

An integrated proteomics and metabolomics approach was performed to define oncofetal biomarkers in CRC by protein and metabolite profiling of serum samples from CRC patients, healthy control adults, and fetus. The differentially expressed proteins were identified by a 2-D DIGE (2-Dimensional Difference Gel Electrophoresis) coupled with a Finnigan LTQ-based proteomics approach. Meanwhile, the serum metabolome was analyzed using gas chromatography-mass spectrometry integrated with a commercial mass spectral library for peak identification.

RESULTS

Of the 28 identified proteins and the 34 analyzed metabolites, only 5 protein spots and 6 metabolites were significantly increased or decreased in both CRC and fetal serum groups compared with the healthy adult group. Data from supervised predictive models allowed a separation of 93.5% of CRC patients from the healthy controls using the 6 metabolites. Finally, correlation analysis was applied to establish quantitative linkages between the 5 individual metabolite 3-hydroxybutyric acid, L-valine, L-threonine, 1-deoxyglucose, and glycine and the 5 individual proteins MACF1, APOH, A2M, IGL@, and VDB. Furthermore, 10 potential oncofetal biomarkers were characterized and their potential for CRC diagnosis was validated.

CONCLUSION

The integrated approach we developed will promote the translation of biomarkers with clinical value into routine clinical practice.

Distinguishing pancreatic cancer from chronic pancreatitis and healthy individuals by (1)H nuclear magnetic resonance-based metabonomic profiles

OBJECTIVES

To develop a noninvasive and accessible diagnostic method for pancreatic cancer (PC).

DESIGN AND METHODS

We presented a metabolomic method, pattern recognition techniques applied to (1)H nuclear magnetic resonance ((1)H NMR) spectra, to investigate the plasma metabolites obtained from 19 patients with PC, 20 patients with chronic pancreatitis (CP) and 20 healthy individuals.

RESULTS

Metabolic changes associated with PC included abnormal amino acid and lipid metabolism, and possible multiple metabolic syndrome. PC elevated plasma levels of N-acetyl glycoprotein (NAG), dimethylamine (DMA), very low density lipoprotein (VLDL), and acetone, and reduced levels of 3-hydroxybutyrate, lactate, high density lipoprotein (HDL), low density lipoprotein (LDL), citrate, alanine, glutamate, glutamine, histidine, isoleucine, lysine, and valine. These metabolites could be a biomarker group for PC that distinguishes between PC and CP patients and healthy individuals.

CONCLUSIONS

NMR-based metabonomic strategy appears as a promising approach for distinguishing pancreatic cancer and identifying new strategies for prevention or therapy in the clinical practice.

Utility of nuclear magnetic resonance spectroscopy for pancreatic cancer studies

OBJECTIVES

The aims of this study were (1) to determine nuclear magnetic resonance spectroscopic characteristics and metabolite profiles of serum samples from patients with pancreatic cancer compared with noncancerous control samples and (2) to ascertain if the accuracy of metabolite identification by 1D spectra can be improved upon by confirmation of spin-system assignment using more sophisticated experiments.

METHODS

Nuclear magnetic resonance spectra, including 1D, total correlation spectroscopy, and heteronuclear multiple/single quantum coherence, were obtained from serum samples from patients with pancreatic cancer and control subjects and used to determine serum levels of a range of metabolites.

RESULTS

The data show that total choline (P = 0.03), taurine (P = 0.03), and glucose plus triglycerides (P = 0.01) are significantly higher in cancer versus control samples. Also detected were species that could not be individually identified and that were designated UCM (unresolved complex matter). Levels of UCM are significantly higher in subjects with cancer, being almost double those of control samples.

CONCLUSIONS

Although metabolites such as lactate, taurine, glucose, choline, and triglycerides can be determined from 1D spectra, accuracy is improved by confirmation of spin-system assignment with total correlation spectroscopy and heteronuclear multiple/single quantum coherence spectral analysis. In addition, we introduce a new metric, UCM, which is at higher concentrations in cancer compared with control samples.

Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data

MOTIVATION

Metabolomics is a rapidly evolving field that holds promise to provide insights into genotype-phenotype relationships in cancers, diabetes and other complex diseases. One of the major informatics challenges is providing tools that link metabolite data with other types of high-throughput molecular data (e.g. transcriptomics, proteomics), and incorporate prior knowledge of pathways and molecular interactions.

RESULTS

We describe a new, substantially redesigned version of our tool Metscape that allows users to enter experimental data for metabolites, genes and pathways and display them in the context of relevant metabolic networks. Metscape 2 uses an internal relational database that integrates data from KEGG and EHMN databases. The new version of the tool allows users to identify enriched pathways from expression profiling data, build and analyze the networks of genes and metabolites, and visualize changes in the gene/metabolite data. We demonstrate the applications of Metscape to annotate molecular pathways for human and mouse metabolites implicated in the pathogenesis of sepsis-induced acute lung injury, for the analysis of gene expression and metabolite data from pancreatic ductal adenocarcinoma, and for identification of the candidate metabolites involved in cancer and inflammation.

AVAILABILITY

Metscape is part of the National Institutes of Health-supported National Center for Integrative Biomedical Informatics (NCIBI) suite of tools, freely available at http://metscape.ncibi.org. It can be downloaded from http://cytoscape.org or installed via Cytoscape plugin manager.

CONTACT

metscape-help@umich.edu; akarnovs@umich.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The metabolic features of normal pancreas and pancreatic adenocarcinoma: preliminary result of in vivo proton magnetic resonance spectroscopy at 3.0 T

OBJECTIVE

The objective of the study was to analyze the metabolic features and distribution of normal pancreas and pancreatic adenocarcinoma while determining the biomarker of pancreatic cancerous process.

METHODS

Twenty-seven control and 29 pancreatic adenocarcinoma patients underwent breath-hold 3-T proton magnetic resonance spectroscopy. The ratios of lipid (lipid/InW), choline-containing compounds (CCCs/InW), and fatty acids (FAs/InW) to nonsaturated internal water (InW) of the normal pancreas head and body-tail region, with cancerous and noncancerous regions in pancreatic adenocarcinoma, were calculated.

RESULTS

In normal pancreas, there were no statistical difference in the ratios of FAs to InW and lipid to InW of different regions, but CCCs/InW of body-tail area was greater than that of head (7.28 × 10⁻⁴ vs 3.23 × 10⁻⁴). In pancreatic cancer, FAs/InW and lipid/InW between cancerous and noncancerous region were different (3.44 × 10⁻⁴ vs 16.3 × 10⁻⁴ and 7.78 × 10⁻⁴ vs 36.3 × 10⁻⁴, respectively). Choline-containing compounds/InW in cancerous region was smaller than that in noncancerous region of pancreatic head cancer (1.62 × 10⁻⁴ vs 5.69 × 10⁻⁴) but similar to such region in body-tail cancer. Lipid/InW dropped in noncancerous regions (from 0.67 to 0.36), whereas there were no differences in FAs/InW and CCCs/InW between normal pancreas regions and noncancerous regions in pancreatic cancer.

CONCLUSIONS

In normal pancreas, CCCs of body-tail region was greater than that of head. Whereas in pancreatic adenocarcinoma, CCCs, FAs, and lipid were all decreased in cancerous region, lipid in the noncancerous region was also decreased compared with normal pancreas. Lipid may be the potential sensitive biomarker for pancreatic cancer.

Urine metabolic signature of pancreatic ductal adenocarcinoma by (1)h nuclear magnetic resonance: identification, mapping, and evolution

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis and is highly chemoresistant. Early detection is the only means to impact long-term survival, but screening methods are lacking. Given the complex and heterogeneous nature of pancreatic cancer, unbiased analytical methods such as metabolomics by nuclear magnetic resonance (NMR) spectroscopy show promise to identify disease-specific molecular fingerprints. NMR profiles constitute a fingerprint of the biofluid, reporting quantitatively on all detectable small biomolecules. NMR spectroscopy was applied to investigate the urine metabolome of PDAC patients (n = 33) and to detect altered metabolic profiles in comparison with healthy matched controls (n = 54). The spectral data were analyzed using multivariate statistical techniques. Statistically significant differences were found between urine metabolomic profiles of PDAC and control individuals (p < 10(-5)). Group discrimination was possible due to average concentration differences of several metabolite signals, pointing to a multimolecular signature of the disease. The robustness of the determined statistical model is confirmed by its predictive performance (sensitivity = 75.8%, specificity = 90.7%). Additionally, the method allowed for a neat separation between spectral profiles of individuals with intermediate and advanced pathologic staging, as well as for the discrimination of samples based on tumor localization. NMR spectroscopy analysis of urinary metabolic profiles proved successful in identifying a complex molecular signature of PDAC. Furthermore, results of a descriptive-level analysis show the possibility to follow disease evolution and to carry out tumor site mapping. Given the high reproducibility and the noninvasive nature of the analytical procedure, the described method bears potential to impact large-scale screening programs.

Metabolite detection of pancreatic carcinoma by in vivo proton MR spectroscopy at 3T: initial results

PURPOSE

The authors sought to identify metabolic features of pancreatic carcinoma by in vivo proton magnetic resonance (MR) spectroscopy at 3 Tesla.

MATERIALS AND METHODS

Forty healthy volunteers and 40 patients with pancreatic carcinoma confirmed by histopathology underwent T2-weighted imaging for localisation of the single voxel. Respiration-triggered (1)H MR spectroscopy was used to detect metabolites in normal pancreas and cancerous tissue. All spectral data were processed with SAGE software. Unsuppressed water at 4.7 ppm was used as an internal reference to determine metabolite concentrations. Each ratio among the different peak areas was statistically evaluated between normal pancreas and pancreatic carcinoma.

RESULTS

The following five groups of spectra were detected: unsaturated fatty acids (-CH = CH-) at 5.4 ppm; residual water at 4.7 ppm; choline metabolites at 3.2 ppm; unsaturated fatty acids (-CH2-CH = CH-) or a combination of N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), glutamine, glutamate, macromolecules and unsaturated fatty acids (-CH2-CH = CH-) at 2.0 ppm and lipids at 1.3 ppm. Ratio of lipids to unsuppressed water in normal pancreas was statistically greater than that in pancreatic cancer (p=0.004). Ratio of choline to unsuppressed water in normal pancreas was statistically greater than that in pancreatic cancer (p=0.0001). Ratio of fatty acids (-CH = CH-) to lipids in normal pancreas was statistically lower than that in pancreatic cancer (p=0.006).

CONCLUSIONS

Compared with normal pancreas, pancreatic carcinoma has a higher ratio of fatty acids (-CH = CH-) to lipids and lower ratios of lipids to unsuppressed water and choline to unsuppressed water at 3T.

Feasibility of identifying pancreatic cancer based on serum metabolomics

BACKGROUND

We postulated that the abundance of various metabolites in blood would facilitate the diagnosis of pancreatic and biliary lesions, which could potentially prevent unnecessary surgery.

METHODS

Serum samples from patients with benign hepatobiliary disease (n = 43) and from patients with pancreatic cancer (n = 56) were examined by ¹H NMR spectroscopy to quantify 58 unique metabolites. Data were analyzed by "targeted profiling" followed by supervised pattern recognition and orthogonal partial least-squares discriminant analysis (O-PLS-DA) of the most significant metabolites, which enables comparison of the whole sample spectrum between groups.

RESULTS

The metabolomic profile of patients with pancreatic cancer was significantly different from that of patients with benign disease (AUROC, area under the ROC curve, = 0.8372). Overt diabetes mellitus (DM) was identified as a possible confounding factor in the pancreatic cancer group. Thus, diabetics were excluded from further analysis. In this more homogeneous pancreatic cancer group, compared with benign cases, serum concentrations of glutamate and glucose were most elevated on multivariate analysis. In benign cases, creatine and glutamine were most abundant. To examine the usefulness of this test, a comparison was made to age- and gender-matched controls with benign lesions that mimic cancer, controlling also for presence of jaundice and diabetes (n = 14 per group). The metabolic profile in patients with pancreatic cancer remained distinguishable from patients with benign pancreatic lesions (AUROC = 0.8308).

CONCLUSIONS

The serum metabolomic profile may be useful for distinguishing benign from malignant pancreatic lesions.

IMPACT

Further studies will be required to study the effects of jaundice and diabetes. A more comprehensive metabolomic profile will be evaluated using mass spectrometry.

Human bile as a rich source of biomarkers for hepatopancreatobiliary cancers

Metabolic profiling of biofluids is emerging as an important area with a promising number of applications in clinical medicine, including early diagnosis of numerous diseases that normally remain silent until late in the progress of disease. While blood and urine are more often used to explore biomarkers that distinguish he healthy from disease conditions, human bile is emerging as a rich source of biomarkers specifically for the cancers of the liver (hepatocellular carcinoma), bile ducts (cholangiocarcinoma), gallbladder and pancreas. This is owing to the fact that metabolites linked to the pathways of tumor cell metabolism are rich in bile by virtue of its association or proximity to the pathological source. Recent methodological developments have enabled the identification of a number of bile metabolites that have links with hepatopancreatobiliary diseases. Investigations of human bile are also considered to help the biomarker discovery process in vitro and provide avenues for translational research in detecting and following dynamic variations of biomarkers in clinical settings using noninvasive approaches, such as in vivo magnetic resonance spectroscopy. This article reviews the current status and potential applications of human bile as a source of biomarkers, with emphasis on metabolites, for early detection of cancers associated with the hepatopancreatobiliary system.

Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer

Poor outcome of pancreatic cancer necessitates development of an early diagnostic method to reduce mortality. No reliable early diagnostic test for pancreatic cancer detection has been developed and validated to date. In the current study, metabolic profiling of plasma samples from selected cancer patients and noncancerous controls was performed to seek novel metabolic biomarkers of pancreatic cancer. A comprehensive mass spectrometry based analytical platform established at the Metabolomics Core of the UC Davis Genome Center allowed detection of multiple compounds previously unreported in plasma from pancreatic cancer patients. It was found that selective amino acids, bile acids, and polar lipids were detected with increased or decreased levels in pancreatic cancer samples compared to controls. These findings on blood plasma levels of the relevant metabolites might be very useful clinical parameters for early diagnosis of pancreatic cancer.

Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles

Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.

Statistical significance analysis of nuclear magnetic resonance-based metabonomics data

Use of nuclear magnetic resonance (NMR)-based metabonomics to search for human disease biomarkers is becoming increasingly common. For many researchers, the ultimate goal is translation from biomarker discovery to clinical application. Studies typically involve investigators from diverse educational and training backgrounds, including physicians, academic researchers, and clinical staff. In evaluating potential biomarkers, clinicians routinely use statistical significance testing language, whereas academicians typically use multivariate statistical analysis techniques that do not perform statistical significance evaluation. In this article, we outline an approach to integrate statistical significance testing with conventional principal components analysis data representation. A decision tree algorithm is introduced to select and apply appropriate statistical tests to loadings plot data, which are then heat map color-coded according to P score, enabling direct visual assessment of statistical significance. A multiple comparisons correction must be applied to determine P scores from which reliable inferences can be made. Knowledge of means and standard deviations of statistically significant buckets enabled computation of effect sizes and study sizes for a given statistical power. Methods were demonstrated using data from a previous study. Integrated metabonomics data assessment methodology should facilitate translation of NMR-based metabonomics discovery of human disease biomarkers to clinical use.

NMR-spectroscopy for nontargeted screening and simultaneous quantification of health-relevant compounds in foods: the example of melamine

The recent melamine crisis in China has pointed out a serious deficiency in current food control systems, namely, they specifically focus on selected known compounds. This targeted approach allowed the presence of melamine in milk products to be overlooked for a considerable time. To avoid such crises in the future, we propose that nontargeted screening methods need to be developed and applied. To this end, NMR has an extraordinary potential that just started to be recognized and exploited. Our research shows that, from the very same set of spectra, (1)H NMR at 400 MHz can distinguish between melamine-contaminated and melamine-free infant formulas and can provide quantitative information by integration of individual lines after identification. For contaminated Chinese infant formulas or candy, identical results were obtained when comparing NMR with SPE-LC/MS/MS. NMR was found to be suitable for routine nontargeted and targeted analyses of foods, and its use will significantly increase food safety.

Nuclear magnetic resonance spectroscopy-based metabolomics of the fatty pancreas: implicating fat in pancreatic pathology

BACKGROUND

Obesity is a worldwide epidemic and a significant risk factor for pancreatic diseases including pancreatitis and pancreatic cancer; the mechanisms underlying this association are unknown. Metabolomics is a powerful new analytical approach for describing the metabolome (compliment of small molecules) of cells, tissue or biofluids at any given time. Our aim was to analyze pancreatic fat content in lean and congenitally obese mice using both metabolomic analysis and conventional chromatography.

METHODS

The pancreatic fat content of 12 lean (C57BL/6J), 12 obese leptin-deficient (Lep(ob)) and 12 obese hyperleptinemic (Lep(db)) mice was evaluated by metabolomic analysis, thin-layer and gas chromatography.

RESULTS

Pancreata of congenitally obese mice had significantly more total pancreatic fat, triglycerides and free fatty acids, but significantly less phospholipids and cholesterol than those of lean mice. Metabolomic analysis showed excellent correlation with thin-layer and gas chromatography in measuring total fat, triglycerides and phospholipids.

CONCLUSIONS

Differences in pancreatic fat content and character may have important implications when considering the local pancreatic proinflammatory milieu in obesity. Metabolomic analysis is a valid, powerful tool with which to further define the mechanisms by which fat impacts pancreatic disease.