Metabolomic biomarkers of pancreatic cancer: a meta-analysis study

Understanding the Conundrum of Pancreatic Cancer in the Omics Sciences Era

Pancreatic cancer (PC) is an increasing cause of cancer-related death, with a dismal prognosis caused by its aggressive biology, the lack of clinical symptoms in the early phases of the disease, and the inefficacy of treatments. PC is characterized by a complex tumor microenvironment. The interaction of its cellular components plays a crucial role in tumor development and progression, contributing to the alteration of metabolism and cellular hyperproliferation, as well as to metastatic evolution and abnormal tumor-associated immunity. Furthermore, in response to intrinsic oncogenic alterations and the influence of the tumor microenvironment, cancer cells undergo a complex oncogene-directed metabolic reprogramming that includes changes in glucose utilization, lipid and amino acid metabolism, redox balance, and activation of recycling and scavenging pathways. The advent of omics sciences is revolutionizing the comprehension of the pathogenetic conundrum of pancreatic carcinogenesis. In particular, metabolomics and genomics has led to a more precise classification of PC into subtypes that show different biological behaviors and responses to treatments. The identification of molecular targets through the pharmacogenomic approach may help to personalize treatments. Novel specific biomarkers have been discovered using proteomics and metabolomics analyses. Radiomics allows for an earlier diagnosis through the computational analysis of imaging. However, the complexity, high expertise required, and costs of the omics approach are the main limitations for its use in clinical practice at present. In addition, the studies of extracellular vesicles (EVs), the use of organoids, the understanding of host-microbiota interactions, and more recently the advent of artificial intelligence are helping to make further steps towards precision and personalized medicine. This present review summarizes the main evidence for the application of omics sciences to the study of PC and the identification of future perspectives.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Lipidomics and pancreatic cancer risk in two prospective studies

Pancreatic ductal carcinoma (PDAC) is highly fatal with limited understanding of mechanisms underlying its carcinogenesis. We comprehensively investigated whether lipidomic measures were associated with PDAC in two prospective studies. We measured 904 lipid species and 252 fatty acids across 15 lipid classes in pre-diagnostic serum (up to 24 years) in a PDAC nested-case control study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, NCT00002540) with 332 matched case-control sets including 272 having serial blood samples and Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC, NCT00342992) with 374 matched case-control sets. Controls were matched to cases by cohort, age, sex, race, and date at blood draw. We used conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) per one-standard deviation increase in log-lipid concentrations within each cohort, and combined ORs using fixed-effects meta-analyses. Forty-three lipid species were associated with PDAC (false discovery rate, FDR ≤ 0.10), including lysophosphatidylcholines (LPC, n = 2), phosphatidylethanolamines (PE, n = 17), triacylglycerols (n = 13), phosphatidylcholines (PC, n = 3), diacylglycerols (n = 4), monoacylglycerols (MAG, n = 2), cholesteryl esters (CE, n = 1), and sphingomyelins (n = 1). LPC(18:2) and PE(O-16:0/18:2) showed significant inverse associations with PDAC at the Bonferroni threshold (P value < 5.5 × 10-5). The fatty acids LPC[18:2], LPC[16:0], PC[15:0], MAG[18:1] and CE[22:0] were significantly associated with PDAC (FDR < 0.10). Similar associations were observed in both cohorts. There was no significant association for the differences between PLCO serial lipidomic measures or heterogeneity by follow-up time overall. Results support that the pre-diagnostic serum lipidome, including 43 lipid species from 8 lipid classes and 5 fatty acids, is associated with PDAC.

Plasma Metabolomics Predicts Chemotherapy Response in Advanced Pancreatic Cancer

Pancreatic cancer (PC) is one of the deadliest cancers. Developing biomarkers for chemotherapeutic response prediction is crucial for improving the dismal prognosis of advanced-PC patients (pts). To evaluate the potential of plasma metabolites as predictors of the response to chemotherapy for PC patients, we analyzed plasma metabolites using high-performance liquid chromatography-mass spectrometry from 31 cachectic, advanced-PC subjects enrolled into the PANCAX-1 (NCT02400398) prospective trial to receive a jejunal tube peptide-based diet for 12 weeks and who were planned for palliative chemotherapy. Overall, there were statistically significant differences in the levels of intermediates of multiple metabolic pathways in pts with a partial response (PR)/stable disease (SD) vs. progressive disease (PD) to chemotherapy. When stratified by the chemotherapy regimen, PD after 5-fluorouracil-based chemotherapy (e.g., FOLFIRINOX) was associated with decreased levels of amino acids (AAs). For gemcitabine-based chemotherapy (e.g., gemcitabine/nab-paclitaxel), PD was associated with increased levels of intermediates of glycolysis, the TCA cycle, nucleoside synthesis, and bile acid metabolism. These results demonstrate the feasibility of plasma metabolomics in a prospective cohort of advanced-PC patients for assessing the effect of enteral feeding as their primary source of nutrition. Metabolic signatures unique to FOLFIRINOX or gemcitabine/nab-paclitaxel may be predictive of a patient's response and warrant further study.

Early Diagnosis of Pancreatic Cancer: Clinical Premonitions, Timely Precursor Detection and Increased Curative-Intent Surgery

BACKGROUND

The overall poor prognosis in pancreatic cancer is related to late clinical detection. Early diagnosis remains a considerable challenge in pancreatic cancer. Unfortunately, the onset of clinical symptoms in patients usually indicate advanced disease or presence of metastasis.

ANALYSIS AND RESULTS

Currently, there are no designated diagnostic or screening tests for pancreatic cancer in clinical use. Thus, identifying risk groups, preclinical risk factors or surveillance strategies to facilitate early detection is a target for ongoing research. Hereditary genetic syndromes are a obvious, but small group at risk, and warrants close surveillance as suggested by society guidelines. Screening for pancreatic cancer in asymptomatic individuals is currently associated with the risk of false positive tests and, thus, risk of harms that outweigh benefits. The promise of cancer biomarkers and use of 'omics' technology (genomic, transcriptomics, metabolomics etc.) has yet to see a clinical breakthrough. Several proposed biomarker studies for early cancer detection lack external validation or, when externally validated, have shown considerably lower accuracy than in the original data. Biopsies or tissues are often taken at the time of diagnosis in research studies, hence invalidating the value of a time-dependent lag of the biomarker to detect a pre-clinical, asymptomatic yet operable cancer. New technologies will be essential for early diagnosis, with emerging data from image-based radiomics approaches, artificial intelligence and machine learning suggesting avenues for improved detection.

CONCLUSIONS

Early detection may come from analytics of various body fluids (eg 'liquid biopsies' from blood or urine). In this review we present some the technological platforms that are explored for their ability to detect pancreatic cancer, some of which may eventually change the prospects and outcomes of patients with pancreatic cancer.

Untargeted metabolomics characterization of the resectable pancreatic ductal adenocarcinoma

Background

Diagnosis of pancreatic ductal adenocarcinoma (PDAC) is difficult due to the lack of specific symptoms and screening methods. Only less than 10% of PDAC patients are candidates for surgery at the time of diagnosis. Thus, there is a great global unmet need for valuable biomarkers that could improve the opportunity to detect PDAC at the resectable stage. This study aimed to develop a potential biomarker model for the detection of resectable PDAC by tissue and serum metabolomics.

Methods

Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS) was performed for metabolome quantification in 98 serum samples (49 PDAC patients and 49 healthy controls (HCs)) and 20 pairs of matched pancreatic cancer tissues (PCTs) and adjacent noncancerous tissues (ANTs) from PDAC patients. Univariate and multivariate analyses were used to profile the differential metabolites between PDAC and HC.

Results

A total of 12 differential metabolites were present in both serum and tissue samples of PDAC. Among them, a total of eight differential metabolites showed the same expressional levels, including four upregulated and four downregulated metabolites. Finally, a panel of three metabolites including 16-hydroxypalmitic acid, phenylalanine, and norleucine was constructed by logistic regression analysis. Notably, the panel was capable of distinguishing resectable PDAC from HC with an AUC value of 0.942. Additionally, a multimarker model based on the 3-metabolites-based panel and CA19-9 showed a better performance than the metabolites panel or CA19-9 alone (AUC: 0.968 vs. 0.942, 0.850).

Conclusions

Taken together, the resectable early-stage PDAC has unique metabolic features in serum and tissue samples. The defined panel of three metabolites has the potential value for early screening of PDAC at the resectable stage.

Investigation of the Exometabolomic Profiles of Rat Islets of Langerhans Cultured in Microfluidic Biochip

Diabetes mellitus (DM) is a complex disease with high prevalence of comorbidity and mortality. DM is predicted to reach more than 700 million people by 2045. In recent years, several advanced in vitro models and analytical tools were developed to investigate the pancreatic tissue response to pathological situations and identify therapeutic solutions. Of all the in vitro promising models, cell culture in microfluidic biochip allows the reproduction of in-vivo-like micro-environments. Here, we cultured rat islets of Langerhans using dynamic cultures in microfluidic biochips. The dynamic cultures were compared to static islets cultures in Petri. The islets' exometabolomic signatures, with and without GLP1 and isradipine treatments, were characterized by GC-MS. Compared to Petri, biochip culture contributes to maintaining high secretions of insulin, C-peptide and glucagon. The exometabolomic profiling revealed 22 and 18 metabolites differentially expressed between Petri and biochip on Day 3 and 5. These metabolites illustrated the increase in lipid metabolism, the perturbation of the pentose phosphate pathway and the TCA cycle in biochip. After drug stimulations, the exometabolome of biochip culture appeared more perturbed than the Petri exometabolome. The GLP1 contributed to the increase in the levels of glycolysis, pentose phosphate and glutathione pathways intermediates, whereas isradipine led to reduced levels of lipids and carbohydrates.

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.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

The Role of Circulating Protein and Metabolite Biomarkers in the Development of Pancreatic Ductal Adenocarcinoma (PDAC): A Systematic Review and Meta-analysis

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and this is attributed to it being diagnosed at an advanced stage. Understanding the pathways involved in initial development may improve early detection strategies. This systematic review assessed the association between circulating protein and metabolite biomarkers and PDAC development.

METHODS

A literature search until August 2020 in MEDLINE, EMBASE, and Web of Science was performed. Studies were included if they assessed circulating blood, urine, or salivary biomarkers and their association with PDAC risk. Quality was assessed using the Newcastle-Ottawa scale for cohort studies. Random-effects meta-analyses were used to calculate pooled relative risk.

RESULTS

A total of 65 studies were included. Higher levels of glucose were found to be positively associated with risk of developing PDAC [n = 4 studies; pooled relative risk (RR): 1.61; 95% CI: 1.16-2.22]. Additionally, an inverse association was seen with pyridoxal 5'-phosphate (PLP) levels (n = 4 studies; RR: 0.62; 95% CI: 0.44-0.87). Meta-analyses showed no association between levels of C-peptide, members of the insulin growth factor signaling pathway, C-reactive protein, adiponectin, 25-hydroxyvitamin D, and folate/homocysteine and PDAC risk. Four individual studies also reported a suggestive positive association of branched-chain amino acids with PDAC risk, but due to differences in measures reported, a meta-analysis could not be performed.

CONCLUSIONS

Our pooled analysis demonstrates that higher serum glucose levels and lower levels of PLP are associated with risk of PDAC.

IMPACT

Glucose and PLP levels are associated with PDAC risk. More prospective studies are required to identify biomarkers for early detection.

Exploring the Clinical Utility of Pancreatic Cancer Circulating Tumor Cells

Pancreatic ductal adenocarcinoma (PDAC) is the most frequent pancreatic cancer type, characterized by a dismal prognosis due to late diagnosis, frequent metastases, and limited therapeutic response to standard chemotherapy. Circulating tumor cells (CTCs) are a rare subset of tumor cells found in the blood of cancer patients. CTCs has the potential utility for screening, early and definitive diagnosis, prognostic and predictive assessment, and offers the potential for personalized management. However, a gold-standard CTC detection and enrichment method remains elusive, hindering comprehensive comparisons between studies. In this review, we summarize data regarding the utility of CTCs at different stages of PDAC from early to metastatic disease and discuss the molecular profiling and culture of CTCs. The characterization of CTCs brings us closer to defining the specific CTC subpopulation responsible for metastasis with the potential to uncover new therapies and more effective management options for PDAC.

High-performance Collective Biomarker from Liquid Biopsy for Diagnosis of Pancreatic Cancer Based on Mass Spectrometry and Machine Learning

Background: Most pancreatic cancers are found at progressive stages when they cannot be surgically removed. Therefore, a highly accurate early detection method is urgently needed.

Methods: This study analyzed serum from Japanese patients who suffered from pancreatic ductal adenocarcinoma (PDAC) and aimed to establish a PDAC-diagnostic system with metabolites in serum. Two groups of metabolites, primary metabolites (PM) and phospholipids (PL), were analyzed using liquid chromatography/electrospray ionization mass spectrometry. A support vector machine was employed to establish a machine learning-based diagnostic algorithm.

Results: Integrating PM and PL databases improved cancer diagnostic accuracy and the area under the receiver operating characteristic curve. It was more effective than the algorithm based on either PM or PL database, or single metabolites as a biomarker. Subsequently, 36 statistically significant metabolites were fed into the algorithm as a collective biomarker, which improved results by accomplishing 97.4% and was further validated by additional serum. Interestingly, specific clusters of metabolites from patients with preoperative neoadjuvant chemotherapy (NAC) showed different patterns from those without NAC and were somewhat comparable to those of the control.

Conclusion: We propose an efficient screening system for PDAC with high accuracy by liquid biopsy and potential biomarkers useful for assessing NAC performance.

Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, characterized by rapid progression, metastasis, and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that uses machine learning (ML) and lipidomics to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an area under curve (AUC) of 0.9351 in the large external validation cohort and 85.00% accuracy with 0.9389 AUC in the prospective clinical cohort. Accordingly, single-cell sequencing, proteomics, and mass spectrometry imaging were applied and revealed notable alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach be used for early detection of PDAC.

Serum Metabolomic and Lipoprotein Profiling of Pancreatic Ductal Adenocarcinoma Patients of African Ancestry

Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with a characteristic dysregulated metabolism. Abnormal clinicopathological features linked to defective metabolic and inflammatory response pathways can induce PDAC development and progression. In this study, we investigated the metabolites and lipoproteins profiles of PDAC patients of African ancestry. Nuclear Magnetic Resonance (NMR) spectroscopy was conducted on serum obtained from consenting individuals (34 PDAC, 6 Chronic Pancreatitis, and 6 healthy participants). Seventy-five signals were quantified from each NMR spectrum. The Liposcale test was used for lipoprotein characterization. Spearman's correlation and Kapan Meier tests were conducted for correlation and survival analyses, respectively. In our patient cohort, the results demonstrated that levels of metabolites involved in the glycolytic pathway increased with the tumour stage. Raised ethanol and 3-hydroxybutyrate were independently correlated with a shorter patient survival time, irrespective of tumour stage. Furthermore, increased levels of bilirubin resulted in an abnormal lipoprotein profile in PDAC patients. Additionally, we observed that the levels of a panel of metabolites (such as glucose and lactate) and lipoproteins correlated with those of inflammatory markers. Taken together, the metabolic phenotype can help distinguish PDAC severity and be used to predict patient survival and inform treatment intervention.

Molecular advances in pancreatic cancer: A genomic, proteomic and metabolomic approach

Pancreatic ductal adenocarcinoma (PDAC) represents a challenging pathology with very poor outcomes and is increasing in incidence within the general population. The majority of patients are diagnosed incidentally with insidious symptoms and hence present late in the disease process. This significantly affects patient outcomes: the only cure is surgical resection but only up to 20% of patients present with resectable disease at the time of clinical presentation. The use of “omic” technology is expanding rapidly in the field of personalised medicine - using genomic, proteomic and metabolomic approaches allows researchers and clinicians to delve deep into the core molecular processes of this difficult disease. This review gives an overview of the current findings in PDAC using these “omic” approaches and summarises useful markers in aiding clinicians treating PDAC. Future strategies incorporating these findings and potential application of these methods are presented in this review article.

Integration of global metabolomics and lipidomics approaches reveals the molecular mechanisms and the potential biomarkers for postoperative recurrence in early-stage cholangiocarcinoma

Background

Cholangiocarcioma (CCA) treatment is challenging because most of the patients are diagnosed when the disease is advanced, and cancer recurrence is the main problem after treatment, leading to low survival rates. Therefore, our understanding of the mechanism underlying CCA recurrence is essential in order to prevent CCA recurrence and improve patient outcomes.

Methods

We performed ¹H-NMR and UPLC-MS-based metabolomics on the CCA serum. The differential metabolites were further analyzed using pathway analysis and potential biomarker identification.

Results

At an early stage, the metabolites involved in energy metabolisms, such as pyruvate metabolism, and the TCA cycle, are downregulated, while most lipids, including TGs, PCs, PEs, and PAs, are upregulated in recurrence patients. This metabolic feature has been described in cancer stem-like cell (CSC) metabolism. In addition, the CSC markers CD44v6 and CD44v8-10 are associated with CD36 (a protein involved in lipid uptake) as well as with recurrence-free survival. We also found that citrate, sarcosine, succinate, creatine, creatinine and pyruvate, and TGs have good predictive values for CCA recurrence.

Conclusion

Our study demonstrates the possible molecular mechanisms underlying CCA recurrence, and these may associate with the existence of CSCs. The metabolic change involved in the recurrence pathway might be used to determine biomarkers for predicting CCA recurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00266-5.

Sauchinone inhibits hypoxia-induced epithelial-mesenchymal transition in pancreatic ductal adenocarcinoma cells through the Wnt/β-catenin pathway

The hypoxic microenvironment is commonly found in various solid tumors including pancreatic ductal adenocarcinoma (PDAC). Saururus chinensis is a medicinal Chinese herb widely used because of documented anti-inflammatory and anti-angiogenic properties. Sauchinone is special active lignin extracted from S. chinensis and its biological functions have been extensively explored. Recent studies have found that sauchinone could affect tumor initiation, metastasis and progression of some cancers. However, the specific role of sauchinone in PDAC remains to be elucidated. The main aim of this study was to elucidate the involvement of sauchinone in the progression of PDAC under the hypoxic condition. The human PDAC cell lines PANC-1 and BxPC-3 were exposed to hypoxia and various concentrations of sauchinone. The CCK-8 assay was performed to detect cytotoxic effects of sauchinone on PDAC cells. The levels of vascular endothelial growth factor, hypoxia-inducible factor-1α, E-cadherin, N-cadherin, Wnt3a and β-catenin were examined by the western blot analysis. Wound healing and transwell assays were used to assess cell migration and invasion. The results showed that the migratory and invasive abilities of PDAC cells were enhanced after exposure to hypoxia and the expression of epithelial-mesenchymal transition markers was also significantly regulated by hypoxia. All these effects induced under the hypoxic condition were terminated by sauchinone treatment. In addition, sauchinone suppressed hypoxia-induced activation of the Wnt/β-catenin signaling pathway. Our study provided important insight into understanding the mechanisms of the anti-cancer effect of sauchinone. Taken together, we suggested that sauchinone may be considered a new therapeutic agent for PDAC treatment.

Metabolomic Laboratory-Developed Tests: Current Status and Perspectives

Laboratory-developed tests (LDTs) are a subset of in vitro diagnostic devices, which the US Food and Drug Administration defines as "tests that are manufactured by and used within a single laboratory". The review describes the emergence and history of LDTs. The current state and development prospects of LDTs based on metabolomics are analyzed. By comparing LDTs with the scientific metabolomics study of human bio samples, the characteristic features of metabolomic LDT are shown, revealing its essence, strengths, and limitations. The possibilities for further developments and scaling of metabolomic LDTs and their potential significance for healthcare are discussed. The legal aspects of LDT regulation in the United States, European Union, and Singapore, demonstrating different approaches to this issue, are also provided. Based on the data presented in the review, recommendations were made on the feasibility and ways of further introducing metabolomic LDTs into practice.

The evolving landscape of untargeted metabolomics

AIMS

Untargeted Metabolomics is a "hypothesis-generating discovery strategy" that compares groups of samples (e.g., cases vs controls); identifies the metabolome and establishes (early signs of) perturbations. Targeted Metabolomics helped gather key information in life sciences and disclosed novel strategies for the treatment of major clinical entities (e.g., malignancy, cardiovascular diabetes mellitus, drug toxicity). Because of its relevance in biomarker discovery, attention is now devoted to improving the translational potential of untargeted Metabolomics.

DATA SYNTHESIS

Expertise in laboratory medicine and in bioinformatics helps solve challenges/pitfalls that may bias metabolite profiling in untargeted Metabolomics. Clinical validation (availability/reliability of analytical instruments) and profitability (how many people will use the test) are mandatory steps for potential biomarkers. Biomarkers to predict individual patient response, patient populations that will best respond to specific strategies and/or approaches for an optimal response to treatment are now being developed. Additional help is expected from professional, and regulatory Agencies as to guidelines for study design and data acquisition and analysis, to be applied from the very beginning of a project. Evidence from food, plant, human, environmental, and animal research argues for the need of miniaturized approaches that employ low-cost, easy to use, mobile devices. ELISA kits with such characteristics that employ targeted metabolites are already available.

CONCLUSIONS

Improving knowledge of the mechanisms behind the disease status (pathophysiology) will help untargeted Metabolomics gather a direct positive impact on welfare and industrial advancements, and fade uncertainties perceived by regulators/payers and patients concerning variables related to miniaturised instruments and user-friendly software and databases.

Blood biomarkers for differential diagnosis and early detection of pancreatic cancer

Pancreatic cancer is currently the most lethal tumor entity and case numbers are rising. It will soon be the second most frequent cause of cancer-related death in the Western world. Mortality is close to incidence and patient survival after diagnosis stands at about five months. Blood-based diagnostics could be one crucial factor for improving this dismal situation and is at a stage that could make this possible. Here, we are reviewing the current state of affairs with its problems and promises, looking at various molecule types. Reported results are evaluated in the overall context. Also, we are proposing steps toward clinical utility that should advance the development toward clinical application by improving biomarker quality but also by defining distinct clinical objectives and the respective diagnostic accuracies required to achieve them. Many of the discussed points and conclusions are highly relevant to other solid tumors, too.

Mass spectrometry-based metabolomics diagnostics - myth or reality?

ABSTACTIntroduction: Metabolomics, one of the most high-promising technologies, is the most recently developed post-genomics discipline for developing new diagnostic tests for future implementation in medicine. More than 2,000 scientific papers, using mass spectrometry-based (MS-based) metabolomics analysis for human disease diagnostics, have been published during the past two decades, and almost every metabolomics study shows high diagnostic accuracy. However, despite the great results and promising perspectives, there are currently no diagnostic tests based on metabolomics that have been approved and introduced into clinics.Areas covered: In this report, the advantages and challenges of MS-based metabolomics are discussed with a focus on its developing role in diagnostics, and the current trends in implementing metabolomics diagnostics in the clinic.Expert opinion: In the development of new clinical diagnostics tests, MS-based metabolomics has potential as both a preliminary discovery base for routine testing and a multi-test prototype, which is hoped to be introduced into clinical practice in the near future. A laboratory-developed test (LDT) is one possible way that multi-testing could be developed.

Early Detection of Pancreatic Cancer in Type 2 Diabetes Mellitus Patients Based on 1H NMR Metabolomics

The association of pancreatic cancer with type 2 diabetes mellitus was investigated by ¹H NMR metabolomic analysis of blood plasma. Concentration data of 58 metabolites enabled discrimination of pancreatic cancer (PC) patients from healthy controls (HC) and long-term type 2 diabetes mellitus (T2DM) patients. A panel of eight metabolites was proposed and successfully tested for group discrimination. Furthermore, a prediction model for the identification of at-risk individuals for future development of pancreatic cancer was built and tested on recent-onset diabetes mellitus (RODM) patients. Six of 59 RODM samples were assessed as PC with an accuracy of more than 80%. The health condition of these individuals was re-examined, and in four cases, a correlation to the prediction was found. The current health condition can be retrospectively attributed to misdiagnosed pancreatogenic diabetes or to early-stage pancreatic cancer.

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.

The application of metabolomics in ovarian cancer management: a systematic review

Metabolomics, the global analysis of metabolites in a biological specimen, could potentially provide a fast method of biomarker identification for ovarian cancer. This systematic review aims to examine findings from studies that apply metabolomics to the diagnosis, prognosis, treatment, and recurrence of ovarian cancer. A systematic search of English language publications was conducted on PubMed, Science Direct, and SciFinder. It was augmented by a snowball strategy, whereby further relevant studies are identified from reference lists of included studies. Studies in humans with ovarian cancer which focus on metabolomics of biofluids and tumor tissue were included. No restriction was placed on the time of publication. A separate review of targeted metabolomic studies was conducted for completion. Qualitative data were summarized in a comprehensive table. The studies were assessed for quality and risk of bias using the ROBINS-I tool. 32 global studies were included in the main systematic review. Most studies applied metabolomics to diagnosing ovarian cancer, within which the most frequently reported metabolite changes were a down-regulation of phospholipids and amino acids: histidine, citrulline, alanine, and methionine. Dysregulated phospholipid metabolism was also reported in the separately reviewed 18 targeted studies. Generally, combinations of more than one significant metabolite as a panel, in different studies, achieved a higher sensitivity and specificity for diagnosis than a single metabolite; for example, combinations of different phospholipids. Widespread metabolite differences were observed in studies examining prognosis, treatment, and recurrence, and limited conclusions could be drawn. Cellular processes of proliferation and invasion may be reflected in metabolic changes present in poor prognosis and recurrence. For example, lower levels of lysine, with increased cell invasion as an underlying mechanism, or glutamine dependency of rapidly proliferating cancer cells. In conclusion, this review highlights potential metabolites and biochemical pathways which may aid the clinical care of ovarian cancer if further validated.

[Ten years of the Russian metabolomics: history of development and achievements]

Metabolomics is one of the omics sciences, the technologies of which are widely used today in many life sciences. Its application influenced the discovery of new biomarkers of diseases, the description of biochemical processes occurring in many organisms, laid the basis for a new generation of clinical laboratory diagnostics. The purpose of this review is to show how metabolomics is represented in the studies of Russian scientists, to demonstrate the main directions and achievements of the Russian science in this field. The review also highlights the history of metabolomics, existing problems and the place of Russian metabolomics in their solution.

[Blood metabolome analysis for creating a digital image of a healthy person]

In the frame of the work, data on the implementation of metabolomics tests in medicine have been systematized. Based on the obtained data, a set of protocols was proposed, the sequential realization of which makes it possible to conduct a blood metabolome analysis for medical purposes. Using this analysis and the number of blood samples from healthy volunteers, a prototype of a healthy person's metabolomic image has been developed; it allows visually and digitally to assess the compliance of the human blood metabolome with the norm. At the same time, 99% of the metabolic processes reflected in the blood plasma are estimated. If abnormalities are detected, the metabolomic image allows to get the value of these deviations of metabolic processes in digital terms.

Association of Arterial Hypertension with Hepatobiliary Pathology: The Occurrence of Comorbidity and Features of Metabolic Processes

Comorbidity of hypertension and hepatobiliary pathology has negative medical and social consequences, including an increase in the indicators of hospital admissions, disability and mortality.

OBJECTIVE

The aim was to study the occurrence of hypertension combined with hepatobiliary diseases depending on social status, gender and age in 2003-2017 and their influence on indicators of metabolic processes in patients with a therapeutic profile.

METHODS

A cross-sectional study using the inpatients' medical record database of the clinic of Federal Research Centre for Basic and Translational Medicine (Novosibirsk, Russia), which collects demographics, diagnoses (using ICD-10 codes), procedures and examinations of all inpatients from 2003-2017 was conducted. The incidence of comorbidity of hypertension and hepatobiliary pathology depending on age, gender and social status, based on the analysis of 13496 medical records was examined. A comparative analysis of biochemical parameters characterizing the main types of metabolism (lipid, protein, carbohydrate and purine) was carried out in 3 groups of patients: with hypertension; with hepatobiliary pathology, and with a combined pathology.

RESULTS

During the years 2003-2005, there was the greatest frequency of this comorbidity in workers, in women, in the age group 60 years and older. In 2009-2017, the highest incidence was observed in the male administrative staff. In patients with this comorbidity, more pronounced changes in carbohydrate, protein, lipid and purine metabolism were found in comparison with groups of patients with isolated diseases.

CONCLUSION

The results highlight the need to improve the system of prevention and treatment of comorbidity taking into account sex, age, occupation and features of metabolism.

Developing a preoperative serum metabolome-based recurrence-predicting nomogram for patients with resected pancreatic ductal adenocarcinoma

We investigated the potential application of preoperative serum metabolomes in predicting recurrence in patients with resected pancreatic cancer. From November 2012 to June 2014, patients who underwent potentially curative pancreatectomy for pancreatic ductal adenocarcinoma were examined. Among 57 patients, 32 were men; 42 had pancreatic head cancers. The 57 patients could be clearly categorized into two main clusters using 178 preoperative serum metabolomes. Patients within cluster 2 showed earlier tumor recurrence, compared with those within cluster 1 (p = 0.034). A nomogram was developed for predicting the probability of early disease-free survival in patients with resected pancreatic cancer. Preoperative cancer antigen (CA) 19–9 levels and serum metabolomes PC.aa.C38_4, PC.ae.C42_5, and PC.ae.C38_6 were the most powerful preoperative clinical variables with which to predict 6-month and 1-year cancer recurrence-free survival after radical pancreatectomy, with a Harrell’s concordance index of 0.823 (95% CI: 0.750–0.891) and integrated area under the curve of 0.816 (95% CI: 0.736–0.893). Patients with resected pancreatic cancer could be categorized according to their different metabolomes to predict early cancer recurrence. Preoperative detectable parameters, serum CA 19–9, PC.aa.C38_4, PC.ae.C42_5, and PC.ae.C38_6 were the most powerful predictors of early recurrence of pancreatic cancer.

The metabolic time line of pancreatic cancer: Opportunities to improve early detection of adenocarcinoma

BACKGROUND

A reliable biomarker to detect pancreatic ductal adenocarcinoma (PDAC) continues to be elusive. With employing metabolomics we hypothesize that a broader analysis of systemic blood can differentiate different stages of PDAC.

METHODS

Patients undergoing pancreatic resection had plasma samples grouped by diagnosis and assayed with mass spectrometry. 10 per group [neuroendocrine (PNET), intraductal papillary mucinous neoplasm (IPMN), localized PDAC, locally advanced PDAC, and metastatic] were analyzed to assess if metabolites could delineation different stages of adenocarcinoma.

RESULTS

Of the 215 metabolites measured, four had a stronger correlation to disease burden than CA19-9. However, none of these metabolites differentiated stepwise progression in malignancy. Principal component analysis identified five metabolic components. Each cancer cohort was characterized by a unique combination of components, two components were predictors of PDCA stages.

CONCLUSIONS

Enhanced metabolomic analysis identified metabolic pathways that may assist in differentiating PDCA stages that do not occur in a linear stepwise progression.

Postoperative serum metabolites of patients on a low carbohydrate ketogenic diet after pancreatectomy for pancreatobiliary cancer: a nontargeted metabolomics pilot study

A ketogenic diet is a potential adjuvant cancer therapy that limits glucose availability to tumours while fuelling normal tissues with ketone bodies. We examined the effect of a low carbohydrate ketogenic diet (LCKD) (80% kcal from fat, ketogenic ratio 1.75:1, w/w) compared to a general hospital diet (GD) on serum metabolic profiles in patients (n = 18, ≥ 19 years old) who underwent pancreatectomy for pancreatobiliary cancer. Serum samples collected preoperatively (week 0) and after the dietary intervention (week 2) were analysed with a nontargeted metabolomics approach using liquid chromatography-tandem mass spectrometry. Serum β-hydroxybutyrate and total ketone levels significantly increased after 2 weeks of LCKD compared to GD (p < 0.05). Principal component analysis score plots and orthogonal partial least squares discriminant analysis also showed significant differences between groups at week 2, with strong validation. In all, 240 metabolites differed between LCKD and GD. Pathways including glycerophospholipid and sphingolipid metabolisms were significantly enriched in the LCKD samples. LCKD decreased C22:1-ceramide levels, which are reported to be high in pancreatic cancer, while increasing lysophosphatidylcholine (18:2), uric acid, citrulline, and inosine levels, which are generally low in pancreatic cancer. Postoperative LCKD might beneficially modulate pancreatic cancer-related metabolites in patients with pancreatobiliary cancer.

Elevating pancreatic cystic lesion stratification: Current and future pancreatic cancer biomarker(s)

Pancreatic ductal adenocarcinoma (PDAC) is an incredibly deadly disease with a 5-year survival rate of 9%. The presence of pancreatic cystic lesions (PCLs) confers an increased likelihood of future pancreatic cancer in patients placing them in a high-risk category. Discerning concurrent malignancy and risk of future PCL progression to cancer must be carefully and accurately determined to improve survival outcomes and avoid unnecessary morbidity of pancreatic resection. Unfortunately, current image-based guidelines are inadequate to distinguish benign from malignant lesions. There continues to be a need for accurate molecular and imaging biomarker(s) capable of identifying malignant PCLs and predicting the malignant potential of PCLs to enable risk stratification and effective intervention management. This review provides an update on the current status of biomarkers from pancreatic cystic fluid, pancreatic juice, and seromic molecular analyses and discusses the potential of radiomics for differentiating PCLs harboring cancer from those that do not.

Glutamine Metabolism Drives Growth in Advanced Hormone Receptor Positive Breast Cancer

Dependence on the glutamine pathway is increased in advanced breast cancer cell models and tumors regardless of hormone receptor status or function. While 70% of breast cancers are estrogen receptor positive (ER+) and depend on estrogen signaling for growth, advanced ER+ breast cancers grow independent of estrogen. Cellular changes in amino acids such as glutamine are sensed by the mammalian target of rapamycin (mTOR) complex, mTORC1, which is often deregulated in ER+ advanced breast cancer. Inhibitor of mTOR, such as everolimus, has shown modest clinical activity in ER+ breast cancers when given with an antiestrogen. Here we show that breast cancer cell models that are estrogen independent and antiestrogen resistant are more dependent on glutamine for growth compared with their sensitive parental cell lines. Co-treatment of CB-839, an inhibitor of GLS, an enzyme that converts glutamine to glutamate, and everolimus interrupts the growth of these endocrine resistant xenografts. Using human tumor microarrays, we show that GLS is significantly higher in human breast cancer tumors with increased tumor grade, stage, ER-negative and progesterone receptor (PR) negative status. Moreover, GLS levels were significantly higher in breast tumors from African-American women compared with Caucasian women regardless of ER or PR status. Among patients treated with endocrine therapy, high GLS expression was associated with decreased disease free survival (DFS) from a multivariable model with GLS expression treated as dichotomous. Collectively, these findings suggest a complex biology for glutamine metabolism in driving breast cancer growth. Moreover, targeting GLS and mTOR in advanced breast cancer may be a novel therapeutic approach in advanced ER+ breast cancer.

Urinary Metabolites Diagnostic and Prognostic of Intrahepatic Cholangiocarcinoma

BACKGROUND

Liver cancer is the second leading cause of cancer-related deaths worldwide. With a predicted 2.4-fold rise in liver cancer incidence by 2020, there is an urgent need for early, inexpensive diagnostic biomarkers to deploy in the clinic.

METHODS

We employed ultraperformance liquid chromatography tandem mass-spectrometry (UPLC/MS-MS) for the quantitation of four metabolites, creatine riboside (CR), N-acetylneuraminic acid (NANA), cortisol sulfate, and a lipid molecule designated as 561+, in urine samples from the NCI-MD cohort comprising 98 hepatocellular carcinoma (HCC) cases, 101 high-risk subjects, and 95 controls. Validation was carried out in the TIGER-LC cohort [n = 370 HCC and intrahepatic cholangiocarcinoma (ICC) cases, 471 high-risk subjects, 251 controls], where ICC, the second most common primary hepatic malignancy, is highly prevalent. Metabolite quantitation was also conducted in TIGER-LC tissue samples (n = 48 ICC; n = 51 HCC).

RESULTS

All profiled metabolites were significantly increased in liver cancer when compared with high-risk subjects and controls in the NCI-MD study. In the TIGER-LC cohort, the four-metabolite profile was superior at classifying ICC than a clinically utilized marker, CA19-9, and their combination led to a significantly improved model (AUC = 0.88, P = 4E-8). Metabolites CR and NANA were significantly elevated in ICC when compared with HCC cases in both urine and tissue samples. High levels of CR were associated with poorer prognosis in ICC.

CONCLUSIONS

Four metabolites are significantly increased in HCC and ICC and are robust at classifying ICC in combination with the clinically utilized marker CA19-9.

IMPACT

Noninvasive urinary metabolite biomarkers hold promise for diagnostic and prognostic evaluation of ICC.

Integrated targeted metabolomic and lipidomic analysis: A novel approach to classifying early cystic precursors to invasive pancreatic cancer

Pancreatic cystic neoplasms (PCNs) are a highly prevalent disease of the pancreas. Among PCNs, Intraductal Papillary Mucinous Neoplasms (IPMNs) are common lesions that may progress from low-grade dysplasia (LGD) through high-grade dysplasia (HGD) to invasive cancer. Accurate discrimination of IPMN-associated neoplastic grade is an unmet clinical need. Targeted (semi)quantitative analysis of 100 metabolites and >1000 lipid species were performed on peri-operative pancreatic cyst fluid and pre-operative plasma from IPMN and serous cystic neoplasm (SCN) patients in a pancreas resection cohort (n = 35). Profiles were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Integrated data modeling was used for group classification and selection of the best explanatory molecules. Over 1000 different compounds were identified in plasma and cyst fluid. IPMN profiles showed significant lipid pathway alterations compared to SCN. Integrated data modeling discriminated between IPMN and SCN with 100% accuracy and distinguished IPMN LGD or IPMN HGD and invasive cancer with up to 90.06% accuracy. Free fatty acids, ceramides, and triacylglycerol classes in plasma correlated with circulating levels of CA19-9, albumin and bilirubin. Integrated metabolomic and lipidomic analysis of plasma or cyst fluid can improve discrimination of IPMN from SCN and within PMNs predict the grade of dysplasia.

Search for Early Pancreatic Cancer Blood Biomarkers in Five European Prospective Population Biobanks Using Metabolomics

Most patients with pancreatic cancer present with advanced disease and die within the first year after diagnosis. Predictive biomarkers that signal the presence of pancreatic cancer in an early stage are desperately needed. We aimed to identify new and validate previously found plasma metabolomic biomarkers associated with early stages of pancreatic cancer. Prediagnostic blood samples from individuals who were to receive a diagnosis of pancreatic cancer between 1 month and 17 years after sampling (N = 356) and age- and sex-matched controls (N = 887) were collected from five large population cohorts (HUNT2, HUNT3, FINRISK, Estonian Biobank, Rotterdam Study). We applied proton nuclear magnetic resonance-based metabolomics on the Nightingale platform. Logistic regression identified two interesting hits: glutamine (P = 0.011) and histidine (P = 0.012), with Westfall-Young family-wise error rate adjusted P values of 0.43 for both. Stratification in quintiles showed a 1.5-fold elevated risk for the lowest 20% of glutamine and a 2.2-fold increased risk for the lowest 20% of histidine. Stratification by time to diagnosis suggested glutamine to be involved in an earlier process (2 to 5 years before diagnosis), and histidine in a process closer to the actual onset (<2 years). Our data did not support the branched-chain amino acids identified earlier in several US cohorts as potential biomarkers for pancreatic cancer. Thus, although we identified glutamine and histidine as potential biomarkers of biological interest, our results imply that a study at this scale does not yield metabolomic biomarkers with sufficient predictive value to be clinically useful per se as prognostic biomarkers.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Unraveling altered RNA metabolism in pancreatic cancer cells by liquid-chromatography coupling to ion mobility mass spectrometry

Ion mobility coupling to mass spectrometry facilitates enhanced identification certitude. Further coupling to liquid chromatography results in multi-dimensional analytical methods, especially suitable for complex matrices with structurally similar compounds. Modified nucleosides represent a large group of very similar members linked to aberrant proliferation. Besides basal production under physiological conditions, they are increasingly excreted by transformed cells and subsequently discussed as putative biomarkers for various cancer types. Here, we report a method for modified nucleosides covering 37 species. We determined collisional cross-sections with high reproducibility from pure analytical standards. For sample purification, we applied an optimized phenylboronic acid solid-phase extraction on media obtained from four different pancreatic cancer cell lines. Our analysis could discriminate different subtypes of pancreatic cancer cell lines. Importantly, they could clearly be separated from a pancreatic control cell line as well as blank medium. m1A, m27G, and Asm were the most important features discriminating cancer cell lines derived from well-differentiated and poorly differentiated cancers. Eventually, we suggest the analytical method reported here for future tumor-marker identification studies. Graphical abstract.

A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer

BACKGROUND

We applied a training and testing approach to develop and validate a plasma metabolite panel for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC) alone and in combination with a previously validated protein panel for early-stage PDAC.

METHODS

A comprehensive metabolomics platform was initially applied to plasmas collected from 20 PDAC cases and 80 controls. Candidate markers were filtered based on a second independent cohort that included nine invasive intraductal papillary mucinous neoplasm cases and 51 benign pancreatic cysts. Blinded validation of the resulting metabolite panel was performed in an independent test cohort consisting of 39 resectable PDAC cases and 82 matched healthy controls. The additive value of combining the metabolite panel with a previously validated protein panel was evaluated.

RESULTS

Five metabolites (acetylspermidine, diacetylspermine, an indole-derivative, and two lysophosphatidylcholines) were selected as a panel based on filtering criteria. A combination rule was developed for distinguishing between PDAC and healthy controls using the Training Set. In the blinded validation study with early-stage PDAC samples and controls, the five metabolites yielded areas under the curve (AUCs) ranging from 0.726 to 0.842, and the combined metabolite model yielded an AUC of 0.892 (95% confidence interval [CI] = 0.828 to 0.956). Performance was further statistically significantly improved by combining the metabolite panel with a previously validated protein marker panel consisting of CA 19-9, LRG1, and TIMP1 (AUC = 0.924, 95% CI = 0.864 to 0.983, comparison DeLong test one-sided P= .02).

CONCLUSIONS

A metabolite panel in combination with CA19-9, TIMP1, and LRG1 exhibited substantially improved performance in the detection of early-stage PDAC compared with a protein panel alone.

A Prospective Targeted Serum Metabolomics Study of Pancreatic Cancer in Postmenopausal Women

To examine the association between metabolic deregulation and pancreatic cancer, we conducted a two-stage case-control targeted metabolomics study using prediagnostic sera collected one year before diagnosis in the Women's Health Initiative study. We used the LC/MS to quantitate 470 metabolites in 30 matched case/control pairs. From 180 detectable metabolites, we selected 14 metabolites to be validated in additional 18 matched case/control pairs. We used the paired t test to compare the concentrations of each metabolite between cases and controls and used the log fold change (FC) to indicate the magnitude of difference. FDR adjusted q-value < 0.25 was indicated statistically significant. Logistic regression model and ROC curve analysis were used to evaluate the clinical utility of the metabolites. Among 30 case/control pairs, 1-methyl-l-tryptophan (L-1MT) was significantly lower in the cases than in the controls (log₂ FC = -0.35; q-value = 0.03). The area under the ROC curve was 0.83 in the discrimination analysis based on the levels of L-1MT, acadesine, and aspartic acid. None of the metabolites was validated in additional independent 18 case/control pairs. No significant association was found between the examined metabolites and undiagnosed pancreatic cancer.

Trait and state biomarkers for psychiatric disorders: Importance of infrastructure to bridge the gap between basic and clinical research and industry

To further improve clinical activities in psychiatry by early diagnosis and early intervention with novel mechanism-guided treatments, there is a great need for biomarkers that reflect 'trait' and 'state' in major mental disorders. Stable trait biomarkers would allow early diagnosis, prognosis, and hopefully early intervention in these disorders, while dynamic state markers that reflect symptomatic changes would help to develop treatments that target these molecular mechanisms. However, in the search for such biomarkers, and eventually translating them to the clinic and industry, challenges currently exist at multiple levels, from basic scientific understanding, patient sample collection, and sample and data management, to bridging the gap between basic and clinical research and industry. To address these challenges, we propose an infrastructure that emphasizes: (i) a research and educational framework to facilitate translation between basic neuroscience, clinical research, and industry; (ii) patient recruitment and collection of disease-relevant samples to study trait and state biomarkers; and (iii) a comprehensive database to integrate patient and sample information with biological and clinical data. We believe that such an approach would bolster: research into the biological mechanisms of psychiatric disorders; and collaboration among the laboratory, clinic, and industry to translate these findings into successful treatments.

NMR-based metabolomics of biofluids in cancer

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

Elevated Polyamines in Saliva of Pancreatic Cancer

Detection of pancreatic cancer (PC) at a resectable stage is still difficult because of the lack of accurate detection tests. The development of accurate biomarkers in low or non-invasive biofluids is essential to enable frequent tests, which would help increase the opportunity of PC detection in early stages. Polyamines have been reported as possible biomarkers in urine and saliva samples in various cancers. Here, we analyzed salivary metabolites, including polyamines, using capillary electrophoresis-mass spectrometry. Salivary samples were collected from patients with PC (n = 39), those with chronic pancreatitis (CP, n = 14), and controls (C, n = 26). Polyamines, such as spermine, N₁-acetylspermidine, and N₁-acetylspermine, showed a significant difference between patients with PC and those with C, and the combination of four metabolites including N₁-acetylspermidine showed high accuracy in discriminating PC from the other two groups. These data show the potential of saliva as a source for tests screening for PC.