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

How Early Can Pancreatic Tumors Be Detected Using NMR-Based Urine Metabolic Profiling? Identification of Early-Stage Biomarkers of Tumor Initiation and Progression in an Orthotopic Xenograft Mouse Model of Pancreatic Cancer

Background: Pancreatic cancer is the most lethal of all human cancers. The disease has no obvious symptoms in its early stages and in the majority of cases, the cancer goes undetected until it has advanced to the point that surgery is no longer a viable option or until it has metastasized to other organs. The absence of reliable and sensitive biomarkers for the early detection of pancreatic cancer contributes to the poor ability to detect the disease before it progresses to an untreatable stage. Objectives: Here, an orthotopic xenograft mouse model of pancreatic cancer was investigated to determine if urinary metabolic biomarkers could be identified and used to detect the early formation of pancreatic tumors. Methods: The orthotopic xenograft mouse model of pancreatic cancer was established by injecting human MiaPaCa-2 cells, derived from a male patient aged 65 years with pancreatic adenocarcinoma, into the pancreata of severe combined immunodeficient mice. Orthotopic pancreatic tumors, allowed to grow for eight weeks, were successfully established in the pancreata in 15 out of 20 mice. At the time of sacrifice, tumors were excised and histologically analyzed and the masses and volumes recorded. Urine samples were collected prior to injection, at one-week post injection, and every two weeks afterwards for eight weeks. Results: NMR-based metabolic profiling of the urine samples indicated that 31 metabolites changed significantly over the course of tumor initiation and growth. Longitudinal metabolic profiling analysis indicated an initial increase in activity of the metabolic pathways involved in energy production and/or cell synthesis by cancer cells as required to support tumor growth that was followed by a diminished difference between control and orthotopic mice associated with tumor senescence as the tumors reached 7-8 weeks post injection. Conclusions: The results indicate that NMR-based urinary metabolic profiling may be able to detect the earliest stages of pancreatic tumor initiation and growth, highlighting the potential for translation to human clinical studies.

Development of a metabolite calculator for diagnosis of pancreatic cancer

BACKGROUND

Carbohydrate antigen (CA) 19-9 is a known pancreatic cancer (PC) biomarker, but is not commonly used for general screening due to its low sensitivity and specificity. This study aimed to develop a serum metabolites-based diagnostic calculator for detecting PC with high accuracy.

METHODS

A targeted quantitative approach of direct flow injection-tandem mass spectrometry combined with liquid chromatography-tandem mass spectrometry was employed for metabolomic analysis of serum samples using an Absolute IDQ™ p180 kit. Integrated metabolomic analysis was performed on 241 pooled or individual serum samples collected from healthy donors and patients from nine disease groups, including chronic pancreatitis, PC, other cancers, and benign diseases. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on characteristics of 116 serum metabolites distinguished patients with PC from those with other diseases. Sparse partial least squares discriminant analysis (SPLS-DA) was also performed, incorporating simultaneous dimension reduction and variable selection. Predictive performance between discrimination models was compared using a 2-by-2 contingency table of predicted probabilities obtained from the models and actual diagnoses.

RESULTS

Predictive values obtained through OPLS-DA for accuracy, sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were 0.9825, 0.9916, 0.9870, 0.9866, and 0.9870, respectively. The number of metabolite candidates was narrowed to 76 for SPLS-DA. The SPLS-DA-obtained predictive values for accuracy, sensitivity, specificity, balanced accuracy, and AUC were 0.9773, 0.9649, 0.9832, 0.9741, and 0.9741, respectively.

CONCLUSIONS

We successfully developed a 76 metabolome-based diagnostic panel for detecting PC that demonstrated high diagnostic performance in differentiating PC from other diseases.

Transcriptomic and Metabolomic Changes Reveal the Immunomodulatory Function of Casein Phosphopeptide-Selenium Chelate in Beagle Dogs

Casein phosphopeptide-selenium chelate (CPP-Se) is an organic compound produced by the chelation of casein phosphopeptide with selenium. This compound showed the ability to modulate canine immune response in our previous study; but its effect on the peripheral blood transcriptome and serum metabolome was unknown. This study aims to reveal the potential mechanism behind the immunomodulatory function of CPP-Se. We have identified 341 differentially expressed genes (DEGs) in CPP-Se groups as compared to the control group which comprised 110 up-regulated and 231 down-regulated genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis found that DEGs were mainly involved in immune-related signaling pathways. Moreover, the immune-related DEGs and hub genes were identified. Similarly, metabolomics identified 53 differentially expressed metabolites (DEMs) in the CPP-Se group, of which 17 were up-regulated and 36 were down-regulated. The pathways mainly enriched by DEMs were primary bile acid biosynthesis, tryptophan metabolism, and other amino acids metabolic pathways. Combined analysis of transcriptomic and metabolomic data showed that the DEGs and DEMs were commonly enriched in fatty acid biosynthesis, pyrimidine metabolism, glutathione metabolism, and glycerolipid metabolic pathways. Taken together, our findings provided a theoretical basis for further understanding of the immunomodulatory function of CPP-Se as well as a scientific reference for the future use of CPP-Se in pet foods as a dietary supplement to modulate the immunity.

Prediction of Pathologic Change Development in the Pancreas Associated with Diabetes Mellitus Assessed by NMR Metabolomics

Nuclear magnetic resonance (NMR) metabolomics was used for identification of metabolic changes in pancreatic cancer (PC) blood plasma samples when compared to healthy controls or diabetes mellitus patients. An increased number of PC samples enabled a subdivision of the group according to individual PC stages and the construction of predictive models for finer classification of at-risk individuals recruited from patients with recently diagnosed diabetes mellitus. High-performance values of orthogonal partial least squares (OPLS) discriminant analysis were found for discrimination between individual PC stages and both control groups. The discrimination between early and metastatic stages was achieved with only 71.5% accuracy. A predictive model based on discriminant analyses between individual PC stages and the diabetes mellitus group identified 12 individuals out of 59 as at-risk of development of pathological changes in the pancreas, and four of them were classified as at moderate risk.

4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone provokes progression from chronic pancreatitis to pancreatic intraepithelial neoplasia

The risk of pancreatic cancer is higher among people who are cigarette smokers than among non-smokers; however, the action mechanisms of cigarette metabolites are not yet fully understood. In this study, we investigated the effect of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) in cigarette smoking on chronic pancreatitis and pancreatic cancer as well as the biological mechanism of NNK causing malignant transformation. We show that smoking may promote Kras mutation and P16 promoter methylation from clinical samples and NNK markedly facilitates the growth and migration of pancreatic cancer cells via the activation of Sonic Hedgehog signaling. We demonstrate that NNK promotes acinar-to-ductal metastasis and pancreatic intraepithelial neoplasia in rats with chronic pancreatitis, accompanied by desmoplastic reaction and Gli1 overexpression. Together, we here present evidence that NNK provokes the progression of chronic pancreatitis toward pancreatic cancer and highlight potential strategies and targets for early prevention of pancreatic cancer and its therapeutics.

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Smoking is positively correlated with Kras mutation and P16 hypermethylation

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NNK promotes acinar-to-ductal metastasis and preneoplasia lesions in rats

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NNK promotes desmoplastic reaction and Gli1 expression in chronic pancreatitis

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NNK facilitates the growth and migration of cancer via Sonic Hedgehog signaling

Current State of "Omics" Biomarkers in Pancreatic Cancer

Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different "omics" levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.

Irradiation of the kidneys causes pathologic remodeling in the nontargeted heart: A role for the immune system

Cardiac disease is a frequent and significant adverse event associated with radiotherapy for cancer. Identifying the underlying mechanism responsible for radiation injury to the heart will allow interventions to be developed. In the present study, we tested if local kidney irradiation results in remodeling of the shielded, nontargeted heart. One kidney, two kidneys, or the total body of male WAG and Dahl SS rats were irradiated with 10 Gy of X-rays. Local kidney irradiation resulted in systemic hypertension, increased BUN, infiltration of T lymphocytes, natural killer cells, and macrophages into the renal cortex and medulla, and renal fibrosis. Local irradiation of kidneys in WAG rats resulted in remodeling in the nontargeted heart after 120 days, manifested by perivascular fibrosis and increased interventricular septal thickness, but was not seen in Dahl SS rats due to a high baseline level of fibrosis in the sham-irradiated animals. Genetic depletion of T cells mitigated the nephropathy after local kidney irradiation, indicating a role for the immune system in mediating this outcome. Local kidney irradiation resulted in a cascade of pro-inflammatory cytokines and low-molecular weight metabolites into the circulation associated with transmission of signals resulting in pathologic remodeling in the nontargeted heart. A new model is proposed whereby radiation-induced cardiac remodeling in susceptible animals is indirect, with lower hemi body organs such as the kidney exporting factors into the circulation that cause remodeling outside of the irradiated field in the shielded, nontargeted heart. This nontargeted effect appears to be mediated, in part, by the immune system.

Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets

Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.

RAS mutations in human cancers: Roles in precision medicine

Ras proteins play a crucial role as a central component of the cellular networks controlling a variety of signaling pathways that regulate growth, proliferation, survival, differentiation, adhesion, cytoskeletal rearrangements and motility of a cell. Almost, 4 decades passed since Ras research was started and ras genes were originally discovered as retroviral oncogenes. Later on, mutations of the human RAS genes were linked to tumorigenesis. Genetic analyses found that RAS is one of the most deregulated oncogenes in human cancers. In this review, we summarize the pioneering works which allowed the discovery of RAS oncogenes, the finding of frequent mutations of RAS in various human cancers, the role of these mutations in tumorigenesis and mutation-activated signaling networks. We further describe the importance of RAS mutations in personalized or precision medicine particularly in molecular targeted therapy, as well as their use as diagnostic and prognostic markers as therapeutic determinants in human cancers.

Pharmacokinetic and Metabolomic Studies with BIO 300, a Nanosuspension of Genistein, in a Nonhuman Primate Model

Genistein is a naturally occurring phytoestrogen isoflavone and is the active drug ingredient in BIO 300, a radiation countermeasure under advanced development for acute radiation syndrome (H-ARS) and for the delayed effects of acute radiation exposure (DEARE). Here we have assessed the pharmacokinetics (PK) and safety of BIO 300 in the nonhuman primate (NHP). In addition, we analyzed serum samples from animals receiving a single dose of BIO 300 for global metabolomic changes using ultra-performance liquid chromatography (UPLC) quadrupole time-of-flight mass spectrometry (QTOF-MS). We present a comparison of how either intramuscularly (im) or orally (po) administered BIO 300 changed the metabolomic profile. We observed transient alterations in phenylalanine, tyrosine, glycerophosphocholine, and glycerophosphoserine which reverted back to near-normal levels 7 days after drug administration. We found a significant overlap in the metabolite profile changes induced by each route of administration; with the po route showing fewer metabolic alterations. Taken together, our results suggest that the administration of BIO 300 results in metabolic shifts that could provide an overall advantage to combat radiation injury. This initial assessment also highlights the utility of metabolomics and lipidomics to determine the underlying physiological mechanisms involved in the radioprotective efficacy of BIO 300.

The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

OBJECTIVE

 The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology.

METHODS

 In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome.

RESULTS

 Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player.

CONCLUSIONS

 The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.

Metabolome analysis for pancreatic cancer risk in nested case-control study: Japan Public Health Center-based prospective Study

Discovery of a high-risk group for pancreatic cancer is important for prevention of pancreatic cancer. The present study was conducted as a nested case-control study including 170 pancreatic cancer cases and 340 matched controls of our population-based cohort study involving 30 239 subjects who answered a baseline questionnaire and supplied blood samples. Twelve targeted metabolites were quantitatively analyzed by gas chromatography/tandem mass spectrometry. Odds ratios (OR) and their corresponding 95% confidence intervals (CI) were calculated using conditional logistic regression models. Statistically significant P-value was defined as P < .05. Increasing 1,5-anhydro-d-glucitol (1,5-AG) levels were associated with a decreasing trend in pancreatic cancer risk (OR of quartile 4 [Q4], 0.50; 95% CI, 0.27-0.93; P = .02). Increasing methionine levels were also associated with an increasing trend of pancreatic cancer risk (OR of Q4, 1.79; 95% CI, 0.94-3.40: P = .03). Additional adjustment for potential confounders attenuated the observed associations of 1,5-AG and methionine (P for trend = .06 and .07, respectively). Comparing subjects diagnosed in the first 0-6 years, higher levels of 1,5-AG, asparagine, tyrosine and uric acid showed a decreasing trend for pancreatic cancer risk (P for trend = .04, .04, .04 and .02, respectively), even after adjustment for potential confounders. We found that the 12 target metabolites were not associated with pancreatic cancer risk. However, metabolic changes in the subjects diagnosed in the first 0-6 years showed a similar tendency to our previous reports. These results might suggest that these metabolites are useful for early detection but not for prediction of pancreatic cancer.

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

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

A Metabolomic and Lipidomic Serum Signature from Nonhuman Primates Administered with a Promising Radiation Countermeasure, Gamma-Tocotrienol

The development of radiation countermeasures for acute radiation syndrome (ARS) has been underway for the past six decades, leading to the identification of multiple classes of radiation countermeasures. However, to date, only two growth factors (Neupogen and Neulasta) have been approved by the United States Food and Drug Administration (US FDA) for the mitigation of hematopoietic acute radiation syndrome (H-ARS). No radioprotector for ARS has been approved by the FDA yet. Gamma-tocotrienol (GT3) has been demonstrated to have radioprotective efficacy in murine as well as nonhuman primate (NHP) models. Currently, GT3 is under advanced development as a radioprotector that can be administered prior to radiation exposure. We are studying this agent for its safety profile and efficacy using the NHP model. In this study, we analyzed global metabolomic and lipidomic changes using ultra-performance liquid chromatography (UPLC) quadrupole time-of-flight mass spectrometry (QTOF-MS) in serum samples of NHPs administered GT3. Our study, using 12 NHPs, demonstrates that alterations in metabolites manifest only 24 h after GT3 administration. Furthermore, metabolic changes are associated with transient increase in the bioavailability of antioxidants, including lactic acid and cholic acid and anti-inflammatory metabolites 3 deoxyvitamin D3, and docosahexaenoic acid. Taken together, our results show that the administration of GT3 to NHPs causes metabolic shifts that would provide an overall advantage to combat radiation injury. This initial assessment also highlights the utility of metabolomics and lipidomics to determine the underlying physiological mechanisms involved in the radioprotective efficacy of GT3.

Dissection of the Mouse Pancreas for Histological Analysis and Metabolic Profiling

We have been investigating the pancreas specific transcription factor, 1a cre-recombinase; lox-stop-lox- Kristen rat sarcoma, glycine to aspartic acid at the 12 codon (Ptf1a^cre/+;LSL-Kras^G12D/+) mouse strain as a model of human pancreatic cancer. The goal of our current studies is to identify novel metabolic biomarkers of pancreatic cancer progression. We have performed metabolic profiling of urine, feces, blood, and pancreas tissue extracts, as well as histological analyses of the pancreas to stage the cancer progression. The mouse pancreas is not a well-defined solid organ like in humans, but rather is a diffusely distributed soft tissue that is not easily identified by individuals unfamiliar with mouse internal anatomy or by individuals that have little or no experience performing mouse organ dissections. The purpose of this article is to provide a detailed step-wise visual demonstration to guide novices in the removal of the mouse pancreas by dissection. This article should be especially valuable to students and investigators new to research that requires harvesting of the mouse pancreas by dissection for metabolic profiling or histological analyses.

Untargeted LC-HRMS-Based Metabolomics for Searching New Biomarkers of Pancreatic Ductal Adenocarcinoma: A Pilot Study

Pancreatic ductal adenocarcinoma is one of the most lethal tumors since it is usually detected at an advanced stage in which surgery and/or current chemotherapy have limited efficacy. The lack of sensitive and specific markers for diagnosis leads to a dismal prognosis. The purpose of this study is to identify metabolites in serum of pancreatic ductal adenocarcinoma patients that could be used as diagnostic biomarkers of this pathology. We used liquid chromatography-high-resolution mass spectrometry for a nontargeted metabolomics approach with serum samples from 28 individuals, including 16 patients with pancreatic ductal adenocarcinoma and 12 healthy controls. Multivariate statistical analysis, which included principal component analysis and partial least squares, revealed clear separation between the patient and control groups analyzed by liquid chromatography-high-resolution mass spectrometry using a nontargeted metabolomics approach. The metabolic analysis showed significantly lower levels of phospholipids in the serum from patients with pancreatic ductal adenocarcinoma compared with serum from controls. Our results suggest that the liquid chromatography-high-resolution mass spectrometry-based metabolomics approach provides a potent and promising tool for the diagnosis of pancreatic ductal adenocarcinoma patients using the specific metabolites identified as novel biomarkers that could be used for an earlier detection and treatment of these patients.

Early detection of pancreatic cancer: Where are we now and where are we going?

Pancreatic cancer (PC) is one of the most lethal malignancies. Recent studies indicate that patients with incidentally diagnosed PC have better prognosis than those with symptoms and that there is a sufficient window for early detection. However, effective early diagnosis remains difficult and depends mainly on imaging modalities and the development of screening methodologies with highly sensitive and specific biomarkers. This review summarizes recent advances in effective screening for early diagnosis of PC using imaging modalities and novel molecular biomarkers discovered from various "omics" studies including genomics, epigenomics, non-coding RNA, metabonomics, liquid biopsy (CTC, ctDNA and exosomes) and microbiomes, and their use in body fluids (feces, urine and saliva). Although many biomarkers for early detection of PC have been discovered through various methods, larger scale and rigorous validation is required before their application in the clinic. In addition, more effective and specific biomarkers of PC are urgently needed.

Metabolomics approaches in pancreatic adenocarcinoma: tumor metabolism profiling predicts clinical outcome of patients

BACKGROUND

Pancreatic adenocarcinomas (PAs) have very poor prognoses even when surgery is possible. Currently, there are no tissular biomarkers to predict long-term survival in patients with PA. The aims of this study were to (1) describe the metabolome of pancreatic parenchyma (PP) and PA, (2) determine the impact of neoadjuvant chemotherapy on PP and PA, and (3) find tissue metabolic biomarkers associated with long-term survivors, using metabolomics analysis.

METHODS

1H high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy using intact tissues was applied to analyze metabolites in PP tissue samples (n = 17) and intact tumor samples (n = 106), obtained from 106 patients undergoing surgical resection for PA.

RESULTS

An orthogonal partial least square-discriminant analysis (OPLS-DA) showed a clear distinction between PP and PA. Higher concentrations of myo-inositol and glycerol were shown in PP, whereas higher levels of glucose, ascorbate, ethanolamine, lactate, and taurine were revealed in PA. Among those metabolites, one of them was particularly obvious in the distinction between long-term and short-term survivors. A high ethanolamine level was associated with worse survival. The impact of neoadjuvant chemotherapy was higher on PA than on PP.

CONCLUSIONS

This study shows that HRMAS NMR spectroscopy using intact tissue provides important and solid information in the characterization of PA. Metabolomics profiling can also predict long-term survival: the assessment of ethanolamine concentration can be clinically relevant as a single metabolic biomarker. This information can be obtained in 20 min, during surgery, to distinguish long-term from short-term survival.

Proteomic strategies in the search for novel pancreatic cancer biomarkers and drug targets: recent advances and clinical impact

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers; despite a low incidence rate it is the fourth leading cause of cancer-related death in the world. Improvement of the diagnosis, prognosis and treatment remains the main focus of pancreatic cancer research. Rapid developments in proteomic technologies has improved our understanding of the pancreatic cancer proteome. Here, the authors summarise the recent proteomic strategies undertaken in the search for: novel biomarkers for early diagnosis, pancreatic cancer-specific proteins which may be used for novel targeted therapies and proteins which may be useful for monitoring disease progression post-therapy. Recent advances and findings discussed here provide great promise of having a significant clinical impact and improving the outcome of patients with this malignancy.

Metabolomics in pancreatic cancer biomarkers research

Pancreatic cancer is one of the worst prognoses of all malignancies. More than 40,000 deaths a year from this disease are observed in European Union alone. The only possibly curative treatment of pancreatic cancer is surgery, yet only 15-20% of patients have operable disease and even patients, which go through surgery and adjuvant chemotherapy, survival is less than 30%. The sensitive and specific biomarkers which could be used for the advance of early diagnostics are needed and constantly researched. Metabolomics is a technology which analyzes the concentrations of low-molecular-weight metabolites (the metabolome) has lately effectively developed due to the improvements in analytical technology. Metabolome analysis can be a one of the useful approaches for the biomarker discovery and disease diagnosis. Here we discuss recent discoveries in the field of pancreatic cancer metabolomics as well as the most promising biomarkers for diagnostics, prognosis and prediction.

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.

Molecular detection of pancreatic neoplasia: Current status and future promise

Pancreatic cancer is usually diagnosed at an advanced stage and curative resection is feasible in only a small minority of patients at the time of diagnosis. Diagnosis at an early stage is unequivocally associated with better long-term survival. Several candidate molecular markers for early detection are currently under investigation in different phases of discovery and validation. Recent advances in the technology for whole genome, methylome, ribonucleome, and proteome interrogation has enabled rapid advancements in the field of biomarker discovery. In this review we discuss the current status of molecular markers for detection of pancreatic cancer in blood, pancreatic cyst fluid, pancreatic juice and stool and briefly highlight some promising preliminary results of new approaches that have the potential of advancing this field in the near future.

Serum metabolomics in animal models and human disease

PURPOSE OF REVIEW

The aim of this study is to highlight some recent uses of serum metabolomics in human and animal studies. The main themes are the importance of understanding the underlying variation in human metabolism and the use of serum metabolomics in disease profiling.

RECENT FINDINGS

Several studies have attempted to use serum metabolomics to develop noninvasive biomarkers of disease and/or track the consequences of nutritional and genetic interventions. Many advances have been made with common changes being identified in ageing, the menopause and cancer but several problems of interpretation have emerged from these studies. These include the small sample sizes in most human studies and the differences between human and rodent metabolomes. However, a metabolic screen of over 1000 'healthy' humans (the Humsermet project) has highlighted many variables that may be used to refine the interpretation and design of previous and future human studies alike, in addition to data mining.

SUMMARY

Some common serum metabolome alterations have been identified but many inconsistencies remain. The construction of a human serum metabolome database should be informative in the design of future human and animal model studies.