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

Global lipid remodelling by hypoxia aggravates migratory potential in pancreatic cancer while maintaining plasma membrane homeostasis

Hypoxia plays an important role in pancreatic cancer progression. It drives various metabolic reprogramming in cells including that of lipids, which in turn, can modify the structure and function of cell membranes. Homeostatic adaptation of membranes is well-recognized, but how and if it is regulated in hypoxic pancreatic cancer and its relation to aggressive phenotype and metastasis remains elusive. Here we show hypoxia-induced extensive global lipid remodelling spanning changes in lipid classes, unsaturation levels, glyceryl backbone and acyl chain lengths. No major modulation of plasma membrane biophysical properties revealed a decoupling of lipidome modulation from membrane properties under hypoxia. This was supported by observing minor changes in the lipidome of plasma membranes under hypoxia. Further, hypoxia increased migration and invasion underpinned by reduced actin volume, cell cortical stiffness and facile tether dynamics. In conclusion, we demonstrate buffering of the lipidome alterations leading to a homeostatic membrane response. These findings will help to understand the hypoxic regulation of pancreatic membrane homeostasis and identify tangible theranostic avenues.

Earlier Diagnosis of Pancreatic Cancer: Is It Possible?

Pancreatic ductal adenocarcinoma has a very high mortality rate which has been only minimally improved in the last 30 years. This high mortality is closely related to late diagnosis, which is usually made when the tumor is large and has extensively infiltrated neighboring tissues or distant metastases are already present. This is a paradoxical situation for a tumor that requires nearly 15 years to develop since the first founding mutation. Response to chemotherapy under such late circumstances is poor, resistance is frequent, and prolongation of survival is almost negligible. Early surgery has been, and still is, the only approach with a slightly better outcome. Unfortunately, the relapse percentage after surgery is still very high. In fact, early surgery clearly requires early diagnosis. Despite all the advances in diagnostic methods, the available tools for improving these results are scarce. Serum tumor markers permit a late diagnosis, but their contribution to an improved therapeutic result is very limited. On the other hand, effective screening methods for high-risk populations have not been fully developed as yet. This paper discusses the difficulties of early diagnosis, evaluates whether the available diagnostic tools are adequate, and proposes some simple and not-so-simple measures to improve it.

Marine Plasmalogens: A Gift from the Sea with Benefits for Age-Associated Diseases

Aging increases oxidative and inflammatory stress caused by a reduction in metabolism and clearance, thus leading to the development of age-associated diseases. The quality of our daily diet and exercise is important for the prevention of these diseases. Marine resources contain various valuable nutrients, and unique glycerophospholipid plasmalogens are found abundantly in some marine invertebrates, including ascidians. One of the major classes, the ethanolamine class (PlsEtn), exists in a high ratio to phospholipids in the brain and blood, while decreased levels have been reported in patients with age-associated diseases, including Alzheimer's disease. Animal studies have shown that the administration of marine PlsEtn prepared from marine invertebrates improved PlsEtn levels in the body and alleviated inflammation. Animal and human studies have reported that marine PlsEtn ameliorates cognitive impairment. In this review, we highlight the biological significance, relationships with age-associated diseases, food functions, and healthcare materials of plasmalogens based on recent knowledge and discuss the contribution of marine plasmalogens to health maintenance in aging.

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.

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.

RNA-sequencing for transcriptional profiling of whole blood in early stage and metastatic pancreatic cancer patients

We investigated the transcriptional profile of whole blood in early and metastatic stages of pancreatic cancer (PaC) patients to identify potential diagnostic factors for early diagnosis. Blood samples from 18 participants (6 healthy individuals, 6 patients in early stage (I/II) PaC, and 6 patients in metastatic PaC) were analyzed by RNA-sequencing. The expression levels of identified genes were subsequently compared with their expression in pancreatic tumor tissues based on TCGA data reported in UALCAN and GEPIA2 databases. Overall, 331 and 724 genes were identified as differentially expressed genes in early and metastatic stages, respectively. Of these, 146 genes were shared by early and metastatic stages. Upregulation of PTCD3 and UBA52 genes and downregulation of A2M and ARID1B genes in PaC patients were observed from early stage to metastasis. TCGA database showed increasing trend in expression levels of these genes from stage I to IV in pancreatic tumor tissue. Finally, we found that low expression of PTCD3, A2M, and ARID1B genes and high expression of UBA52 gene were positively correlated with PaC patients survival. We identified a four-gene set (PTCD3, UBA52, A2M, and ARID1B) expressed in peripheral blood of early stage and metastatic PaC patients that may be useful for PaC early diagnosis.

Tumor metabolic alterations after neoadjuvant chemoradiotherapy predict postoperative recurrence in patients with pancreatic cancer

OBJECTIVE

We investigated the metabolic changes in pancreatic ductal adenocarcinoma to identify the mechanisms of treatment response of neoadjuvant chemoradiation therapy.

METHODS

Frozen tumor and non-neoplastic pancreas tissues were prospectively obtained from 88 patients with pancreatic ductal adenocarcinoma who underwent curative-intent surgery. Sixty-two patients received neoadjuvant chemoradiation therapy and 26 patients did not receive neoadjuvant therapy (control group). Comprehensive analysis of metabolites in tumor and non-neoplastic pancreatic tissue was performed by capillary electrophoresis-mass spectrometry.

RESULTS

Capillary electrophoresis-mass spectrometry detected 90 metabolites for analysis among more than 500 ionic metabolites quantified. There were significant differences in 27 tumor metabolites between the neoadjuvant chemoradiation therapy and control groups. There were significant differences in eight metabolites [1-MethylnNicotinamide, Carnitine, Glucose, Glutathione (red), N-acetylglucosamine 6-phosphate, N-acetylglucosamine 1-phosphate, UMP, Phosphocholine] between good responder and poor responder for neoadjuvant chemoradiation therapy. Among these metabolites, phosphocholine, Carnitine and Glutathione were associated with recurrence-free survival only in the neoadjuvant chemoradiation therapy group. Microarray confirmed marked gene suppression of choline transporters [CTL1-4 (SLC44A1-44A4)] in pancreatic ductal adenocarcinoma tissue of neoadjuvant chemoradiation therapy group.

CONCLUSION

The present study identifies several important metabolic consequences and potential neoadjuvant chemoradiation therapy targets in pancreatic ductal adenocarcinoma. Choline metabolism is one of the key pathways involved in recurrence of the patients with pancreatic ductal adenocarcinoma who received neoadjuvant chemoradiation therapy.

Lipidomic profiling of exosomes from colorectal cancer cells and patients reveals potential biomarkers

Strong evidence suggests that differences in the molecular composition of lipids in exosomes depend on the cell type and has an influence on cancer initiation and progression. Here, we analyzed by liquid chromatography–mass spectrometry (LC‐MS) the lipidomic signature of exosomes derived from the human cell lines normal colon mucosa (NCM460D), and colorectal cancer (CRC) nonmetastatic (HCT116) and metastatic (SW620), and exosomes isolated from the plasma of nonmetastatic and metastatic CRC patients and healthy donors. Analysis of this exhaustive lipid study highlighted changes in some molecular species that were found in the cell lines and confirmed in the patients. For example, exosomes from primary cancer patients and nonmetastatic cells compared with healthy donors and control cells displayed a common marked increase in phosphatidylcholine (PC) 34 : 1, phosphatidylethanolamine (PE) 36 : 2, sphingomyelin (SM) d18 : 1/16 : 0, hexosylceramide (HexCer) d18 : 1/24 : 0 and HexCer d18 : 1/24 : 1. Interestingly, these same lipids species were decreased in the metastatic cell line and patients. Further, levels of PE 34 : 2, PE 36 : 2, and phosphorylated PE p16 : 0/20 : 4 were also significantly decreased in metastatic conditions when compared to the nonmetastatic counterparts. The only molecule species found markedly increased in metastatic conditions (in both patients and cells) when compared to controls was ceramide (Cer) d18 : 1/24 : 1. These decreases in lipid species in the extracellular vesicles might reflect function‐associated changes in the metastatic cell membrane. Although these potential biomarkers need to be validated in a larger cohort, they provide new insight toward the use of clusters of lipid biomarkers rather than a single molecule for the diagnosis of different stages of CRC.

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.

Comprehensive Metabolomics and Lipidomics Profiling of Prostate Cancer Tissue Reveals Metabolic Dysregulations Associated with Disease Development

Prostate cancer (PCa) is a global health problem that affects millions of men every year. In the past decade, metabolomics and related subareas, such as lipidomics, have demonstrated an enormous potential to identify novel mechanisms underlying PCa development and progression, providing a good basis for the development of new and more effective therapies and diagnostics. In this study, a multiplatform metabolomics and lipidomics approach, combining untargeted mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based techniques, was applied to PCa tissues to investigate dysregulations associated with PCa development, in a cohort of 40 patients submitted to radical prostatectomy for PCa. Results revealed significant alterations in the levels of 26 metabolites and 21 phospholipid species in PCa tissue compared with adjacent nonmalignant tissue, suggesting dysregulation in 13 metabolic pathways associated with PCa development. The most affected metabolic pathways were amino acid metabolism, nicotinate and nicotinamide metabolism, purine metabolism, and glycerophospholipid metabolism. A clear interconnection between metabolites and phospholipid species participating in these pathways was observed through correlation analysis. Overall, these dysregulations may reflect the reprogramming of metabolic responses to produce high levels of cellular building blocks required for rapid PCa cell proliferation.

Changes in Plasma Phospholipid Metabolism Are Associated with Clinical Manifestations of Systemic Sclerosis

Systemic sclerosis (SSc) is an autoimmune disease with fibrosis of the skin and/or internal organs, causing a decrease in quality of life and survival. There is no causative therapy, and the pathophysiology of the SSc remains unclear. Studies showed that lipid metabolism was relevant for autoimmune diseases, but little is known about the role of lipids in SSc. In the present study, we sought to explore the phospholipid profile of SSc by using the lipidomics approach. We also aimed to analyze lipidomics results for different clinical manifestations of SSc. Experiments were performed using high-performance liquid chromatography coupled to mass spectrometry for the lipidomic profiling of plasma samples from patients with SSc. Our study showed, for the first time, significant changes in the level of phospholipids such as plasmalogens and sphingomyelins from the plasma of SSc patients as compared to controls. Phosphatidylcholine plasmalogens species and sphingomyelins were significantly increased in SSc patients as compared to controls. Our results also demonstrated a significant association of changes in the metabolism of phospholipids (phosphatidylcholine and phosphatidylethanolamine plasmalogens species and sphingomyelins) with different clinical manifestations of SSc. Further lipidomic studies might lead to the detection of lipids as new biomarkers or therapeutic targets of SSc.

Plasmalogen Deficiency and Overactive Fatty Acid Elongation Biomarkers in Serum of Breast Cancer Patients Pre- and Post-Surgery-New Insights on Diagnosis, Risk Assessment, and Disease Mechanisms

Simple Summary

Breast cancer (BC) is the most commonly diagnosed cancer in women. Mammography and ultrasonography are commonly used for BC screening; however, they are associated with problems such as inconvenience, radiation exposure, and dependence on the skill level of operators. To overcome this problem, we performed a comprehensive lipid metabolomic analysis of serum using high-resolution accurate mass spectrometry from two case-control studies that included non-BC, BC subjects pre-surgery and BC subjects one-month post-surgery to determine if the metabolic signatures of over-active fatty acid elongation and other lipid changes could be detected in BC vs. non-BC subjects. The ratios of the linoleic acid to the oleic acid which were evaluated in multiple lipid pools were lower in pre-surgery BC subjects, however, these ratios increased at post-surgery and were no longer different from non-BC subjects. On the other hand, the ethanolamine plasmalogen levels were lower in pre-surgery BC subjects and were not recovered by surgical removal. These do not appear to be caused by BC tumor activity and may be pre-existent and a possible risk factor for BC. In this study, we have identified several lipid metabolic systems that detect both BC risk and BC activity.

Abstract

The polyunsaturated fatty acid (PUFA) elongase, ELOVL5, is upregulated in breast cancer (BC) vs. adjacent normal tissue. We performed a comprehensive lipid metabolomic analysis of serum using high-resolution accurate mass spectrometry from two case-control studies that included non-BC, BC subjects pre-surgery, and BC subjects one-month post-surgery to determine if the metabolic signatures of over-active fatty acid elongation and other lipid changes could be detected in BC vs. non-BC subjects: study 1 (n = 48: non-BC, n = 69: pre-surgery BC); study 2 (blinded validation: n = 121: non-BC, n = 62: pre-surgery BC, n = 31: one month post-surgery). The ratio of the ELOVL5 precursor, linoleic acid (18:2) to a non-ELOVL5 precursor, oleic acid (18:1) was evaluated in multiple lipid pools (phosphatidylethanolamine (PtdEtn), phosphatidylcholine (PtdCho), lyso-PtdCho, and free fatty acids). This ratio was lower in pre-surgery BC subjects in all pools in both studies (p < 0.001). At one-month post-surgery, the 18:2/18:1 ratios increased vs. pre-surgery and were no longer different from non-BC subjects (p > 0.05 expect for lyso-PtdCho). In contrast to the elongation biomarkers, docosahexaenoic acid (22:6n-3) containing ethanolamine plasmalogen (EtnPls) species were observed to be further decreased in BC subjects one-month post-surgery vs. pre-surgery levels (p < 0.001). These results are consistent with the hypothesis that ELOVL5 is upregulated in BC tissue, which would result in the selective depletion of 18:2 vs. 18:1 containing lipid species. Surgical removal of the tumor removes the overactive ELOVL5 effect on serum lipids. In contrast, the low EtnPls levels do not appear to be caused by BC tumor activity and may be pre-existent and a possible risk factor for BC. These results indicate that it may be possible to screen for both breast cancer risk and breast cancer activity using a simple blood test.

Dietary intake of fatty acids and risk of pancreatic cancer: Golestan cohort study

Background

As pancreatic cancer (PC) is a malignancy with poor prognosis, finding strategies for its prevention became a notable priority. Among all the factors influencing the risk of PC, dietary items especially fats are considered as the most modifiable risk factors.This study is designed to assess the associations of dietary intake of fatty acids with the risk of PC incidence.

Methods

A total of 50,045 adults between 40 and 75 years old participated in this cohort study in 2004–2008 and were followed up to the present. Intakes of fatty acids was evaluated by validated food-frequency questionnaire (FFQ). Cox proportional hazards regression model was used to estimate hazard ratio (HR) with 95 % confidence interval of differing levels of dietary intakes of fatty acids for incidence of PC.

Results

At the end of follow-up period, 76 cases of PC were identified and 46,904 participants without history of cancer, acute kidney disorders, fibrosis and cirrhosis were included in the study. Dietary total saturated fatty acids (SFAS) was associated with PC risk (HR = 1.05 (1.01–1.09), P_trend=0.01), whereas dietary total monounsaturated fatty acids (MUFAS) was inversely associated with the risk of PC (HR = 0.92 (0.86–0.99), P_trend=0.04). Dietary total polyunsaturated fatty acids (PUFAS) did show a protective but not significant association with the risk of PC (HR = 0.91(0.84-1.00), P_trend=0.05).

Conclusions

The amount of total fat intake is not a risk factor for PC in our study and focusing on the intake of specific fatty acids becomes more striking. Unsaturated fatty acids including PUFAS and especially MUFAS are considered as protective dietary factors in PC prevention. In contrast, total SFAS is positively associated with the increased risk of PC. However, very long chain and odd-chain saturated fatty acids intake may be protective against PC.

Circulating amino acids and amino acid-related metabolites and risk of breast cancer among predominantly premenopausal women

Known modifiable risk factors account for a small fraction of premenopausal breast cancers. We investigated associations between pre-diagnostic circulating amino acid and amino acid-related metabolites (N = 207) and risk of breast cancer among predominantly premenopausal women of the Nurses' Health Study II using conditional logistic regression (1057 cases, 1057 controls) and multivariable analyses evaluating all metabolites jointly. Eleven metabolites were associated with breast cancer risk (q-value < 0.2). Seven metabolites remained associated after adjustment for established risk factors (p-value < 0.05) and were selected by at least one multivariable modeling approach: higher levels of 2-aminohippuric acid, kynurenic acid, piperine (all three with q-value < 0.2), DMGV and phenylacetylglutamine were associated with lower breast cancer risk (e.g., piperine: ORadjusted (95%CI) = 0.84 (0.77-0.92)) while higher levels of creatine and C40:7 phosphatidylethanolamine (PE) plasmalogen were associated with increased breast cancer risk (e.g., C40:7 PE plasmalogen: ORadjusted (95%CI) = 1.11 (1.01-1.22)). Five amino acids and amino acid-related metabolites (2-aminohippuric acid, DMGV, kynurenic acid, phenylacetylglutamine, and piperine) were inversely associated, while one amino acid and a phospholipid (creatine and C40:7 PE plasmalogen) were positively associated with breast cancer risk among predominately premenopausal women, independent of established breast cancer risk factors.

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.

Metabolomics Identifies Biomarker Signatures to Differentiate Pancreatic Cancer from Type 2 Diabetes Mellitus in Early Diagnosis

METHODS

Plasma metabolic profiles in 26 PC patients, 27 DM patients, and 23 healthy volunteers were examined using an ultraperformance liquid chromatography coupled with tandem mass spectrometry platform. Differential metabolite ions were then identified using the principal component analysis (PCA) model and the orthogonal partial least-squares discrimination analysis (OPLS-DA) model. The diagnosis performance of metabolite biomarkers was validated by logistic regression models.

RESULTS

We established a PCA model (R2X = 23.5%, Q2 = 8.21%) and an OPLS-DA model (R2X = 70.0%, R2Y = 84.9%, Q2 = 69.7%). LysoPC (16 : 0), catelaidic acid, cerebronic acid, nonadecanetriol, and asparaginyl-histidine were found to identify PC, with a sensitivity of 89% and a specificity of 91%. Besides, lysoPC (16 : 0), lysoPC (16 : 1), lysoPC (22 : 6), and lysoPC (20 : 3) were found to differentiate PC from DM, with higher accuracy (68% versus 55%) and higher AUC values (72% versus 63%) than those of CA19-9. The diagnostic performance of metabolite biomarkers was finally validated by logistic regression models.

CONCLUSION

We succeeded in screening differential metabolite ions among PC and DM patients and healthy individuals, thus providing a preliminary basis for screening the biomarkers for the early diagnosis of PC.

Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study

AIM

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at a late, incurable stage. We sought to determine whether individuals at high risk of developing PDAC could be identified early using routinely collected data.

METHODS

Electronic health record (EHR) databases from two independent hospitals in Boston, Massachusetts, providing inpatient, outpatient, and emergency care, from 1979 through 2017, were used with case-control matching. PDAC cases were selected using International Classification of Diseases 9/10 codes and validated with tumour registries. A data-driven feature selection approach was used to develop neural networks and L2-regularised logistic regression (LR) models on training data (594 cases, 100,787 controls) and compared with a published model based on hand-selected diagnoses ('baseline'). Model performance was validated on an external database (408 cases, 160,185 controls). Three prediction lead times (180, 270 and 365 days) were considered.

RESULTS

The LR model had the best performance, with an area under the curve (AUC) of 0.71 (confidence interval [CI]: 0.67-0.76) for the training set, and AUC 0.68 (CI: 0.65-0.71) for the validation set, 365 days before diagnosis. Data-driven feature selection improved results over 'baseline' (AUC = 0.55; CI: 0.52-0.58). The LR model flags 2692 (CI 2592-2791) of 156,485 as high risk, 365 days in advance, identifying 25 (CI: 16-36) cancer patients. Risk stratification showed that the high-risk group presented a cancer rate 3 to 5 times the prevalence in our data set.

CONCLUSION

A simple EHR model, based on diagnoses, can identify high-risk individuals for PDAC up to one year in advance. This inexpensive, systematic approach may serve as the first sieve for selection of individuals for PDAC screening programs.

Phospholipase A2 superfamily in cancer

Phospholipase A2 enzymes (PLA₂s) comprise a superfamily that is generally divided into six subfamilies known as cytosolic PLA₂s (cPLA₂s), calcium-independent PLA₂s (iPLA₂s), secreted PLA₂s (sPLA₂s), lysosomal PLA₂s, platelet-activating factor (PAF) acetylhydrolases, and adipose specific PLA₂s. Each subfamily consists of several isozymes that possess PLA₂ activity. The first three PLA₂ subfamilies play important roles in inflammation-related diseases and cancer. In this review, the roles of well-studied enzymes sPLA₂-IIA, cPLA₂α and iPLA₂β in carcinogenesis and cancer development were discussed. sPLA₂-IIA seems to play conflicting roles and can act as a tumor suppressor or a tumor promoter according to the cancer type, but cPLA₂α and iPLA₂β play protumorigenic role in most cancers. The mechanisms of PLA₂-mediated signal transduction and crosstalk between cancer cells and endothelial cells in the tumor microenvironment are described. Moreover, the mechanisms by which PLA₂s mediate lipid reprogramming and glycerophospholipid remodeling in cancer cells are illustrated. PLA₂s as the upstream regulators of the arachidonic acid cascade are generally high expressed and activated in various cancers. Therefore, they can be considered as potential pharmacological targets and biomarkers in cancer. The detailed information summarized in this review may aid in understanding the roles of PLA₂s in cancer, and provide new clues for the development of novel agents and strategies for tumor prevention and treatment.

Roles of endogenous ether lipids and associated PUFAs in the regulation of ion channels and their relevance for disease

Ether lipids (ELs) are lipids characterized by the presence of either an ether linkage (alkyl lipids) or a vinyl ether linkage [i.e., plasmalogens (Pls)] at the sn1 position of the glycerol backbone, and they are enriched in PUFAs at the sn2 position. In this review, we highlight that ELs have various biological functions, act as a reservoir for second messengers (such as PUFAs) and have roles in many diseases. Some of the biological effects of ELs may be associated with their ability to regulate ion channels that control excitation-contraction/secretion/mobility coupling and therefore cell physiology. These channels are embedded in lipid membranes, and lipids can regulate their activities directly or indirectly as second messengers or by incorporating into membranes. Interestingly, ELs and EL-derived PUFAs have been reported to play a key role in several pathologies, including neurological disorders, cardiovascular diseases, and cancers. Investigations leading to a better understanding of their mechanisms of action in pathologies have opened a new field in cancer research. In summary, newly identified lipid regulators of ion channels, such as ELs and PUFAs, may represent valuable targets to improve disease diagnosis and advance the development of new therapeutic strategies for managing a range of diseases and conditions.

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

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

Discovery of Pancreatic Adenocarcinoma Biomarkers by Untargeted Metabolomics

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal cancers, with a 5-year survival rate of less than 5%. In fact, complete surgical resection remains the only curative treatment. However, fewer than 20% of patients are candidates for surgery at the time of presentation. Hence, there is a critical need to identify diagnostic biomarkers with potential clinical utility in this pathology. In this context, metabolomics could be a powerful tool to search for new robust biomarkers. Comparative metabolomic profiling was performed in serum samples from 59 unresectable PDAC patients and 60 healthy controls. Samples were analyzed by using an untargeted metabolomics workflow based on liquid chromatography, coupled to high-resolution mass spectrometry in positive and negative electrospray ionization modes. Univariate and multivariate analysis allowed the identification of potential candidates that were significantly altered in PDAC patients. A panel of nine candidates yielded excellent diagnostic capacities. Pathway analysis revealed four altered pathways in our patients. This study shows the potential of liquid chromatography coupled to high-resolution mass spectrometry as a diagnostic tool for PDAC. Furthermore, it identified novel robust biomarkers with excellent diagnostic capacities.

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.

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.

Characterization of the urinary metabolic profile of cholangiocarcinoma in a United Kingdom population

Background: Outside South-East Asia, most cases of cholangiocarcinoma (CCA) have an obscure etiology. There is often diagnostic uncertainty. Metabolomics using ultraperformance liquid chromatography mass spectrometry (UPLC-MS) offers the portent to distinguish disease-specific metabolic signatures. We aimed to define such a urinary metabolic signature in a patient cohort with sporadic CCA and investigate whether there were characteristic differences from those in patients with hepatocellular carcinoma (HCC), metastatic secondary liver cancer, pancreatic cancer and ovarian cancer (OCA). Methods: Spot urine specimens were obtained from 211 subjects in seven participating centers across the UK. Samples were collected from healthy controls and from patients with benign hepatic disease (gallstone, biliary strictures, sphincter of Oddi dysfunction and viral hepatitis) and patients with malignant conditions (HCC, pancreatic cancer, OCA and metastatic cancer in the liver). The spectral metabolite profiles were generated using a UPLC-MS detector and data were analyzed using multivariate and univariate statistical analyses. Results: The greatest class differences were seen between the metabolic profiles of disease-free controls compared to individuals with CCA with altered acylcarnitine, bile acid and purine levels. Individuals with benign strictures showed comparable urine profiles to patients with malignant bile duct lesions. The metabolic signatures of patients with bile duct tumors were distinguishable from patients with hepatocellular and ovarian tumors, but no difference was observed between CCA cases and patients with pancreatic cancer or hepatic secondary metastases. Conclusion: CCA causes subtle but detectable changes in the urine metabolic profiles. The findings point toward potential applications of metabonomics in early tumor detection. However, it is key to utilize both global and targeted metabonomics in a larger cohort for in-depth characterization of the urine metabolome in hepato-pancreato-biliary disease.

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.

Potential Nutritional and Metabolomic Advantages of High Fat Oral Supplementation in Pancreatectomized Pancreaticobiliary Cancer Patients

We examined the effect of high fat oral nutritional supplement (HFS) on the nutritional status, oral intake, and serum metabolites of postoperative pancreaticobiliary cancer patients. Pancreaticobiliary cancer patients were voluntarily recruited. The HFS group received postoperative oral high fat supplementation (80% of total calories from fat; n = 12) until discharge; the control group (non-HFS; n = 9) received none. Dietary intake, anthropometry, blood chemistry, nutritional risk index (NRI), and serum metabolites analyzed by liquid chromatography tandem mass spectrometry were evaluated. Overall, cumulative caloric supply via parental and oral/enteral routes were not different between groups. However, oral fat intake, caloric intake, and NRI scores of the HFS group were higher than those of the non-HFS group with increased oral meal consumption. Oral caloric, fat, and meal intakes correlated with NRI scores. Metabolomics analysis identified 195 serum metabolites pre-discharge. Oral fat intake was correlated with 42 metabolites relevant to the glycerophospholipid pathway. Oral high fat-specific upregulation of sphingomyelin (d18:1/24:1), a previously reported pancreatic cancer-downregulated metabolite, and lysophosphatidylcholine (16:0) were associated with NRI scores. Provision of HFS in postoperative pancreatic cancer patients may facilitate the recovery of postoperative health status by increasing oral meal intake, improving nutritional status, and modulating serum metabolites

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.

High-Throughput Metabolomics Based on Direct Mass Spectrometry Analysis in Biomedical Research

Metabolomics based on direct mass spectrometry analysis shows a great potential in biomedical research because of its high-throughput screening capability and wide metabolome coverage. This chapter contains detailed protocols to perform comprehensive metabolomic fingerprinting of multiple biological samples (serum, plasma, urine, brain, liver, spleen, thymus) by using complementary analytical platforms. The most important issues to be considered are discussed, including sample treatment, metabolomic analysis, raw data preprocessing, and data analysis.

Endogenous Anti-Inflammatory Very-Long-Chain Dicarboxylic Acids: Potential Chemopreventive Lipids

In a paradigm shift, cancer research efforts are being dedicated to the discovery of chemopreventive agents. The goal of this approach is to delay or prevent the progression of augmented cell division to established cancer. Research has focused on dietary supplements, drugs, and endogenous lipids that possess anti-inflammatory properties. We undertook a lipidomics analysis of potential endogenous anti-inflammatory/anti-proliferative lipids in human plasma. We performed high-resolution mass spectrometric lipidomics analyses of plasma samples from controls and patients with colorectal, kidney, pancreatic, glioblastoma, and breast cancers. We present evidence that endogenous very-long-chain dicarboxylic acids (VLCDCA) are anti-inflammatory lipids that possess chemopreventative properties. In a family of VLCDCAs, we characterized VLCDCA 28:4, which is decreased in the plasma of patients with colorectal, kidney, and pancreatic cancers. The structure of this biomarker was validated by derivatization strategies, synthesis of the analytical standard, and tandem mass spectrometry. Our data suggest that VLCDCA 28:4 may be a useful blood biomarker for a number of cancers and that resupplying this lipid, via a prodrug for example, may offer a new anti-inflammatory therapeutic strategy for delaying or preventing the progression of cancer and other inflammatory diseases.

Network-based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

Pancreatic cancer is regarded as the most fatal and aggressive malignancy cancer due to its low 5-year survival rate and poor prognosis. The approaches of early diagnosis and treatment are limited, which makes it urgent to identify the complex mechanism of pancreatic oncogenesis. In this study, we used RNA-seq to investigate the transcriptomic (mRNA and miRNA) profiles of pancreatic cancer in paired tumor and normal pancreatic samples from ten patients. More than 1000 differentially expressed genes were identified, nearly half of which were also found to be differentially expressed in the majority of examined patients. Functional enrichment analysis revealed that these genes were significantly enriched in multicellular organismal and metabolic process, secretion, mineral transport, and intercellular communication. In addition, only 24 differentially expressed miRNAs were found, all of which have been reported to be associated with pancreatic cancer. Furthermore, an integrated miRNA-mRNA interaction network was generated using multiple resources. Based on the calculation of disease correlation scores developed here, several genes present in the largest connected subnetwork, such as albumin, ATPase H⁺ /K⁺ exchanging alpha polypeptide and carcinoembryonic antigen-related cell adhesion molecule 1, were considered as novel genes that play important roles in the development of pancreatic cancer. Overall, our data provide new insights into further understanding of key molecular mechanisms underlying pancreatic tumorigenesis.

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

INTRODUCTION

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

OBJECTIVES

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

METHODS

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

Peroxisomes and cancer: The role of a metabolic specialist in a disease of aberrant metabolism

Cancer is irrevocably linked to aberrant metabolic processes. While once considered a vestigial organelle, we now know that peroxisomes play a central role in the metabolism of reactive oxygen species, bile acids, ether phospholipids (e.g. plasmalogens), very-long chain, and branched-chain fatty acids. Immune system evasion is a hallmark of cancer, and peroxisomes have an emerging role in the regulation of cellular immune responses. Investigations of individual peroxisome proteins and metabolites support their pro-tumorigenic functions. However, a significant knowledge gap remains regarding how individual functions of proteins and metabolites of the peroxisome orchestrate its potential role as a pro-tumorigenic organelle. This review highlights new advances in our understanding of biogenesis, enzymatic functions, and autophagic degradation of peroxisomes (pexophagy), and provides evidence linking these activities to tumorigenesis. Finally, we propose avenues that may be exploited to target peroxisome-related processes as a mode of combatting cancer.

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

Plasmalogen lipids: functional mechanism and their involvement in gastrointestinal cancer

The plasmalogens are a class of glycerophospholipids which contain a vinyl-ether and an ester bond at the sn-1 and sn-2 positions, respectively, in the glycerol backbone. They constitute 10 mol% of the total mass of phospholipids in humans, mainly as membrane structure components. Plasmalogens are important for the organization and stability of lipid raft microdomains and cholesterol-rich membrane regions involved in cellular signaling. In addition to their structural roles, a subset of ether lipids are thought to function as endogenous antioxidants and emerging studies suggest that they are involved in cell differentiation and signaling pathways. Although the clinical significance of plasmalogens is linked to peroxisomal disorders, the pathophysiological roles and their possible metabolic pathways are not fully understood since they present unique structural attributes for the different tissue types. Studies suggest that changes in plasmalogen metabolism may contribute to the development of various types of cancer. Here, we review the molecular characteristics of plasmalogens in order to significantly increase our understanding of the plasmalogen molecule and its involvement in gastrointestinal cancers as well as other types of cancers.

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.

Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis

OBJECTIVE

Current non-invasive diagnostic tests can distinguish between pancreatic cancer (pancreatic ductal adenocarcinoma (PDAC)) and chronic pancreatitis (CP) in only about two thirds of patients. We have searched for blood-derived metabolite biomarkers for this diagnostic purpose.

DESIGN

For a case-control study in three tertiary referral centres, 914 subjects were prospectively recruited with PDAC (n=271), CP (n=282), liver cirrhosis (n=100) or healthy as well as non-pancreatic disease controls (n=261) in three consecutive studies. Metabolomic profiles of plasma and serum samples were generated from 477 metabolites identified by gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry.

RESULTS

A biomarker signature (nine metabolites and additionally CA19-9) was identified for the differential diagnosis between PDAC and CP. The biomarker signature distinguished PDAC from CP in the training set with an area under the curve (AUC) of 0.96 (95% CI 0.93-0.98). The biomarker signature cut-off of 0.384 at 85% fixed specificity showed a sensitivity of 94.9% (95% CI 87.0%-97.0%). In the test set, an AUC of 0.94 (95% CI 0.91-0.97) and, using the same cut-off, a sensitivity of 89.9% (95% CI 81.0%-95.5%) and a specificity of 91.3% (95% CI 82.8%-96.4%) were achieved, successfully validating the biomarker signature.

CONCLUSIONS

In patients with CP with an increased risk for pancreatic cancer (cumulative incidence 1.95%), the performance of this biomarker signature results in a negative predictive value of 99.9% (95% CI 99.7%-99.9%) (training set) and 99.8% (95% CI 99.6%-99.9%) (test set). In one third of our patients, the clinical use of this biomarker signature would have improved diagnosis and treatment stratification in comparison to CA19-9.

Metabolomic biomarkers of pancreatic cancer: a meta-analysis study

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.

Serum Metabolomic Profiles for Human Pancreatic Cancer Discrimination

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

Sequential Validation of Blood-Based Protein Biomarker Candidates for Early-Stage Pancreatic Cancer

Background

CA19-9, which is currently in clinical use as a pancreatic ductal adenocarcinoma (PDAC) biomarker, has limited performance in detecting early-stage disease. We and others have identified protein biomarker candidates that have the potential to complement CA19-9. We have carried out sequential validations starting with 17 protein biomarker candidates to determine which markers and marker combination would improve detection of early-stage disease compared with CA19-9 alone.

Methods

Candidate biomarkers were subjected to enzyme-linked immunosorbent assay based sequential validation using independent multiple sample cohorts consisting of PDAC cases (n = 187), benign pancreatic disease (n = 93), and healthy controls (n = 169). A biomarker panel for early-stage PDAC was developed based on a logistic regression model. All statistical tests for the results presented below were one-sided.

Results

Six out of the 17 biomarker candidates and CA19-9 were validated in a sample set consisting of 75 PDAC patients, 27 healthy subjects, and 19 chronic pancreatitis patients. A second independent set of 73 early-stage PDAC patients, 60 healthy subjects, and 74 benign pancreatic disease patients (combined validation set) yielded a model that consisted of TIMP1, LRG1, and CA19-9. Additional blinded testing of the model was done using an independent set of plasma samples from 39 resectable PDAC patients and 82 matched healthy subjects (test set). The model yielded areas under the curve (AUCs) of 0.949 (95% confidence interval [CI] = 0.917 to 0.981) and 0.887 (95% CI = 0.817 to 0.957) with sensitivities of 0.849 and 0.667 at 95% specificity in discriminating early-stage PDAC vs healthy subjects in the combined validation and test sets, respectively. The performance of the biomarker panel was statistically significantly improved compared with CA19-9 alone (P < .001, combined validation set; P = .008, test set).

Conclusion

The addition of TIMP1 and LRG1 immunoassays to CA19-9 statistically significantly improves the detection of early-stage PDAC.

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.

Racial disparity in metabolic regulation of cancer

Genetic mutations and metabolic reprogramming are two key hallmarks of cancer, required for proliferation, invasion, and metastasis of the disease. While genetic mutations, whether inherited or acquired, are critical for the initiation of tumor development, metabolic reprogramming is an effector mechanism imperative for adaptational transition during the progression of cancer. Recent findings in the literature emphasize the significance of molecular cross-talk between these two cellular processes in regulating signaling and differentiation of cancer cells. Genome-wide sequencing analyses of cancer genomes have highlighted the association of various genic mutations in predicting cancer risk and survival. Oncogenic mutational frequency is heterogeneously distributed among various cancer types in different populations, resulting in varying susceptibility to cancer risk. In this review, we explore and discuss the role of genetic mutations in metabolic enzymes and metabolic oncoregulators to stratify cancer risk in persons of different racial backgrounds.

Direct infusion mass spectrometry for metabolomic phenotyping of diseases

Metabolomics based on direct mass spectrometry (MS) analysis, either by direct infusion or flow injection of crude sample extracts, shows a great potential for metabolic fingerprinting because of its high-throughput screening capability, wide metabolite coverage and reduced time of analysis. Considering that numerous metabolic pathways are significantly perturbed during the initiation and progression of diseases, these metabolomic tools can be used to get a deeper understanding about disease pathogenesis and discover potential biomarkers for early diagnosis. In this work, we describe the most common metabolomic platforms used in biomedical research, with special focus on strategies based on direct MS analysis. Then, a comprehensive review on the application of direct MS fingerprinting in clinical issues is provided.

Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available.

OBJECTIVES

The aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS).

METHODS

A discovery cohort comprising PDAC (n = 44) and CP (n = 23) samples was analyzed by LC-MS followed by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n = 20) and CP (n = 31) samples.

RESULTS

Glycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.

CONCLUSION

Using LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.

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

Purpose

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

Experimental Design

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

Materials and Methods

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

Conclusion

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

UPLC-MS for metabolomics: a giant step forward in support of pharmaceutical research

Metabolomics is a relatively new and rapidly growing area of post-genomic biological research. As use of metabolomics technology grows throughout the spectrum of drug discovery and development, and its applications broaden, its impact is expanding dramatically. This review seeks to provide the reader with a brief history of the development of metabolomics, its significance and strategies for conducting metabolomics studies. The most widely used analytical tools for metabolomics: NMR, LC-MS and GC-MS, are discussed along with considerations for their use. Herein, we will show how metabolomics can assist in pharmaceutical research studies, such as pharmacology and toxicology, and discuss some examples of the importance of metabolomics analysis in research and development.

Serum Metabolite Profiling for the Detection of Pancreatic Cancer: Results of a Large Independent Validation Study

OBJECTIVES

To improve the detection of pancreatic cancer (PC), a robust diagnostic biomarker is essential. We have previously discovered 4 serum metabolites (PC-594, lysophosphatidylcholine, phosphatidylcholine, and sphingomyelin) in distinguishing patients with PC from healthy controls. Here, we report the results of our validation phase by using larger numbers of independent and blinded samples.

METHODS

We collected 3 mL of serum from 116 patients with PC and 138 healthy controls. Samples were blinded and expression of the 4 candidate metabolites in each sample was determined by triple quadrupole tandem mass spectrometry. We then used cutoffs established in the discovery phase to predict the disease state of each of the validation samples.

RESULTS

All 4 metabolites showed significantly lower expression in patients with PC compared with healthy controls. PC-594 showed 73.3% sensitivity and 92.0% specificity, whereas the other 3 metabolites showed 58.6% and 92.0%, 76.7% and 69.6%, and 58.6% and 81.9% sensitivity and specificity, respectively. Area under the receiver operating characteristic curve for PC-594 alone was 0.92, whereas a combination method using all 4 metabolites showed 86.2% sensitivity and 84.8% specificity.

CONCLUSIONS

Our validation results confirmed that a reduction in PC-594, along with 3 other serum-based choline metabolites, is highly associated with PC.

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.

Evaluating Ultra-long-Chain Fatty Acids as Biomarkers of Colorectal Cancer Risk

BACKGROUND

Cross-sectional studies reported a novel set of hydroxylated ultra-long-chain fatty acids (ULCFA) that were present at significantly lower levels in colorectal cancer cases than controls. Follow-up studies suggested that these molecules were potential biomarkers of protective exposure for colorectal cancer. To test the hypothesis that ULCFAs reflect causal pathways, we measured their levels in prediagnostic serum from incident colorectal cancer cases and controls.

METHODS

Serum from 95 colorectal cancer patients and 95 matched controls was obtained from the Italian arm of the European Prospective Investigation into Cancer and Nutrition cohort and analyzed by liquid chromatography-high-resolution mass spectrometry. Levels of 8 ULCFAs were compared between cases and controls with paired t tests and a linear model that used time to diagnosis (TTD) to determine whether case-control differences were influenced by disease progression.

RESULTS

Although paired t tests detected significantly lower levels of four ULCFAs in colorectal cancer cases, confirming earlier reports, the case-control differences diminished significantly with increasing TTD (7 days-14 years).

CONCLUSION

Levels of several ULCFAs were lower in incident colorectal cancer cases than controls. However, because case-control differences decreased with increasing TTD, we conclude that these molecules were likely consumed by processes related to cancer progression rather than causal pathways.

IMPACT

ULCFA levels are unlikely to represent exposures that protect individuals from colorectal cancer. Future research should focus on the diagnostic potential and origins of these molecules. Our use of TTD as a covariate in a linear model provides an efficient method for distinguishing causal and reactive biomarkers in biospecimens from prospective cohorts. Cancer Epidemiol Biomarkers Prev; 25(8); 1216-23. ©2016 AACR.