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

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.

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.

High-resolution magic angle spinning (1)H nuclear magnetic resonance spectroscopy metabolomics of hyperfunctioning parathyroid glands

BACKGROUND

Primary hyperparathyroidism (PHPT) may be related to a single gland disease or multiglandular disease, which requires specific treatments. At present, an operation is the only curative treatment for PHPT. Currently, there are no biomarkers available to identify these 2 entities (single vs. multiple gland disease). The aims of the present study were to compare (1) the tissue metabolomics profiles between PHPT and renal hyperparathyroidism (secondary and tertiary) and (2) single gland disease with multiglandular disease in PHPT using metabolomics analysis.

METHODS

The method used was (1)H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy. Forty-three samples from 32 patients suffering from hyperparathyroidism were included in this study.

RESULTS

Significant differences in the metabolomics profile were assessed according to PHPT and renal hyperparathyroidism. A bicomponent orthogonal partial least square-discriminant analysis showed a clear distinction between PHPT and renal hyperparathyroidism (R(2)Y = 0.85, Q(2) = 0.63). Interestingly, the model also distinguished single gland disease from multiglandular disease (R(2)Y = 0.96, Q(2) = 0.55). A network analysis was also performed using the Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information (ADEMA). Single gland disease was accurately predicted by ADEMA and was associated with higher levels of phosphorylcholine, choline, glycerophosphocholine, fumarate, succinate, lactate, glucose, glutamine, and ascorbate compared with multiglandular disease.

CONCLUSION

This study shows for the first time that (1)H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy is a reliable and fast technique to distinguish single gland disease from multiglandular disease in patients with PHPT. The potential use of this method as an intraoperative tool requires specific further studies.

Metabolic analysis and mechanism of lipids, amino acids and carbohydrates in gastrointestinal cancer

Metabolomics has recently been applied in a variety of biomedical research fields. However, there have been no articles on the application of metabonomics in gastrointestinal cancer and the relevant detailed mechanisms. In this article, the application of metabolomics in early diagnosis of gastrointestinal cancer is reviewed, and the metabolic role and metabolic mechanism of lipids, amino acids and carbohydrates, as well as the future challenge of metabolomics in the clinical application are summarized.

Pancreatic cancer serum biomarker PC-594: Diagnostic performance and comparison to CA19-9

AIM: To investigate serum PC-594 fatty acid levels as a potential biomarker in North American pancreatic cancer (PaC) patients, and to compare its performance to CA19-9.

METHODS: Using tandem mass spectrometry, we evaluated serum PC-594 levels from 84 North American patients with confirmed PaC and 99 cancer-free control subjects. We determined CA19-9 levels by ELISA. Significance between PaC patients and controls, and association with clinical variables was determined by analysis of variance and t-tests. Diagnostic performance was evaluated by receiver-operator characteristic (ROC) curve analysis, and PC-594 correlation with age and CA19-9 determined by regression analysis.

RESULTS: Mean PC-594 levels were 3.7 times lower in PaC patients compared to controls (P < 0.0001). The mean level in PaC patient serum was 0.76 ± 0.07 μmol/L, and the mean level in control subjects was 2.79 ± 0.15 μmol/L. There was no correlation between PC-594 and age, disease stage or gender (P > 0.05). Using 1.25 μmol/L as a PC-594 threshold produced a relative risk (RR) of 9.4 (P < 0.0001, 95%CI: 5.0-17.7). The area under the receiver-operator characteristic curve (ROC-AUC) was 0.93 (95%CI: 0.91-0.95) for PC-594 and 0.85 (95%CI: 0.82-0.88) for CA19-9. Sensitivity at 90% specificity was 87% for PC-594 and 71% for CA19-9. Six PaC patients with CA19-9 above 35 U/mL showed normal PC-594 levels, while 24 PaC patients with normal CA19-9 showed low PC-594 levels. Eighty-five of the 99 control subjects (86%) showed normal levels of both markers.

CONCLUSION: PC-594 biomarker levels are significantly reduced in North American PaC patients, and showed superior diagnostic performance compared to CA19-9.

Metabolic rewiring of pancreatic ductal adenocarcinoma: New routes to follow within the maze

Pancreatic ductal adenocarcinoma (PDAC) is a debilitating and almost universally fatal malignancy. Despite advances in understanding of the oncogenetics of the disease, very few clinical benefits have been shown. One of the main characteristics of PDAC is the tumor architecture where tumor cells are surrounded by a firm desmoplasia. By reducing vascularization, thus both oxygen and nutrients delivery to the tumor, this stroma causes the appearance of hypoxic zones driving metabolic adaptation in surviving tumor cells in order to cope with challenging conditions. This metabolic reprogramming promoted by environmental constraints enhances PDAC aggressiveness. In this review, we provide a brief overview of previous works regarding the importance of glucose and glutamine addiction of PDAC cells. In particular we aim to highlight the need for exploring the impact of metabolites other than glucose and glutamine, such as non-essential amino acids and oncometabolites, to find new treatments. We also discuss the need for progress in methodology for metabolites detection. The overall purpose of our review is to emphasize the need to look beyond what is currently known, with a focus on amino acid availability, in order to improve our understanding of PDAC biology.

Pancreatic cancer early detection: Expanding higher-risk group with clinical and metabolomics parameters

Pancreatic ductal adenocarcinoma (PDAC) is the fourth and fifth leading cause of cancer death for each gender in developed countries. With lack of effective treatment and screening scheme available for the general population, the mortality rate is expected to increase over the next several decades in contrast to the other major malignancies such as lung, breast, prostate and colorectal cancers. Endoscopic ultrasound, with its highest level of detection capacity of smaller pancreatic lesions, is the commonly employed and preferred clinical imaging-based PDAC detection method. Various molecular biomarkers have been investigated for characterization of the disease, but none are shown to be useful or validated for clinical utilization for early detection. As seen from studies of a small subset of familial or genetically high-risk PDAC groups, the higher yield and utility of imaging-based screening methods are demonstrated for these groups. Multiple recent studies on the unique cancer metabolism including PDAC, demonstrate the potential for utility of the metabolites as the discriminant markers for this disease. In order to generate an early PDAC detection screening strategy available for a wider population, we propose to expand the population of higher risk PDAC group with combination clinical and metabolomics parameters.

Metabonomics and drug development

Metabolites as an end product of metabolism possess a wealth of information about altered metabolic control and homeostasis that is dependent on numerous variables including age, sex, and environment. Studying significant changes in the metabolite patterns has been recognized as a tool to understand crucial aspects in drug development like drug efficacy and toxicity. The inclusion of metabonomics into the OMICS study platform brings us closer to define the phenotype and allows us to look at alternatives to improve the diagnosis of diseases. Advancements in the analytical strategies and statistical tools used to study metabonomics allow us to prevent drug failures at early stages of drug development and reduce financial losses during expensive phase II and III clinical trials. This chapter introduces metabonomics along with the instruments used in the study; in addition relevant examples of the usage of metabonomics in the drug development process are discussed along with an emphasis on future directions and the challenges it faces.

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

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

The role of quantitative mass spectrometry in the discovery of pancreatic cancer biomarkers for translational science

In the post-genomic era, it has become evident that genetic changes alone are not sufficient to understand most disease processes including pancreatic cancer. Genome sequencing has revealed a complex set of genetic alterations in pancreatic cancer such as point mutations, chromosomal losses, gene amplifications and telomere shortening that drive cancerous growth through specific signaling pathways. Proteome-based approaches are important complements to genomic data and provide crucial information of the target driver molecules and their post-translational modifications. By applying quantitative mass spectrometry, this is an alternative way to identify biomarkers for early diagnosis and personalized medicine. We review the current quantitative mass spectrometric technologies and analyses that have been developed and applied in the last decade in the context of pancreatic cancer. Examples of candidate biomarkers that have been identified from these pancreas studies include among others, asporin, CD9, CXC chemokine ligand 7, fibronectin 1, galectin-1, gelsolin, intercellular adhesion molecule 1, insulin-like growth factor binding protein 2, metalloproteinase inhibitor 1, stromal cell derived factor 4, and transforming growth factor beta-induced protein. Many of these proteins are involved in various steps in pancreatic tumor progression including cell proliferation, adhesion, migration, invasion, metastasis, immune response and angiogenesis. These new protein candidates may provide essential information for the development of protein diagnostics and targeted therapies. We further argue that new strategies must be advanced and established for the integration of proteomic, transcriptomic and genomic data, in order to enhance biomarker translation. Large scale studies with meta data processing will pave the way for novel and unexpected correlations within pancreatic cancer, that will benefit the patient, with targeted treatment.