Feasibility of Identifying Pancreatic Cancer Based on Serum Metabolomics

Bathe, Oliver F.; Shaykhutdinov, Rustem; Kopciuk, Karen; Weljie, Aalim M.; McKay, Andrew; Sutherland, Francis; Dixon, Elijah; Dunse, Nicole; Sotiropoulos, Dina; Vogel, Hans J.

doi:10.1158/1055-9965.epi-10-0712

Cited by 148 publications

(134 citation statements)

References 32 publications

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“…Regarding the pancreas, we previously found that human serum metabolomics using gas chromatography/mass spectrometry (GC/MS) is able to discriminate patients with pancreatic cancer from healthy volunteers (20). Similar results were obtained from the nuclear magnetic resonance (NMR)-based metabolic profiling of rat tissues (27) and NMR-based serum metabolomics in humans (28). In this study, we constructed a diagnostic model for pancreatic cancer using GC/MSbased human serum metabolomics and then confirmed its diagnostic performance via validation analysis and comparisons with conventional tumor markers.…”

Section: Introductionsupporting

confidence: 60%

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A Novel Serum Metabolomics-Based Diagnostic Approach to Pancreatic Cancer

Kobayashi

Nishiumi

Ikeda

et al. 2013

Cancer Epidemiology, Biomarkers &Amp; Prevention

155

144

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Background: To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer.Methods: Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study.Results: In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers.Conclusions: Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis.Impact: It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis. Cancer Epidemiol Biomarkers Prev; 22(4); 571-9. Ó2013 AACR.

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Section: Introductionsupporting

confidence: 60%

“…Recently, the possibility of using metabolomics as a diagnostic tool for pancreatic cancer has been investigated in a number of studies (20,27,28,(31)(32)(33)(34). However, in these reports, principle component analysis (PCA) was used to test the use of metabolomics.…”

Section: Discussionmentioning

confidence: 99%

A Novel Serum Metabolomics-Based Diagnostic Approach to Pancreatic Cancer

Kobayashi

Nishiumi

Ikeda

et al. 2013

Cancer Epidemiology, Biomarkers &Amp; Prevention

155

144

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“…Examples of metabolites that are highly affected by preanalytics and previously reported as biomarkers in various biomed-ical indications (11,(33)(34)(35)(36)(37)(38)(39) are serotonin, taurine, sphingosine-1-phosphate, glutamine, and glutamate (Fig. 2C, Fig.…”

Section: Discussionmentioning

confidence: 90%

Quality Markers Addressing Preanalytical Variations of Blood and Plasma Processing Identified by Broad and Targeted Metabolite Profiling

Kamlage¹,

Maldonado²,

Bethan³

et al. 2014

Clinical Chemistry

160

174

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BACKGROUND:Metabolomics is a valuable tool with applications in almost all life science areas. There is an increasing awareness of the essential need for high-quality biospecimens in studies applying omics technologies and biomarker research. Tools to detect effects of both blood and plasma processing are a key for assuring reproducible and credible results. We report on the response of the human plasma metabolome to common preanalytical variations in a comprehensive metabolomics analysis to reveal such high-quality markers.

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“…A breakthrough technology for probing lectin-glycan interactions was the glycan microarray (13)(14)(15)(16)(17)(18)(19). Relative to previous technologies, glycan arrays significantly reduced the time and reagent costs of experiments and increased the number of different lectin-glycan interactions that could be practically probed.…”

mentioning

confidence: 99%

Global Comparisons of Lectin–Glycan Interactions Using a Database of Analyzed Glycan Array Data

Kletter

Singh

Bern

et al. 2013

Molecular & Cellular Proteomics

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Lectin-glycan interactions have critical functions in multiple normal and pathological processes, but the binding partners and functions for many glycans and lectins are not known. An important step in better understanding glycan-lectin biology is enabling systematic quantification and analysis of the interactions. Glycan arrays can provide the experimental information for such analyses, and the thousands of glycan array datasets available through the Consortium for Functional Glycomics provide the opportunity to extend the analyses to a broad scale. We developed software, based on our previously described Motif Segregation algorithm, for the automated analysis of glycan array data, and we analyzed the entire storehouse of 2883 datasets from the Consortium for Functional Glycomics. We mined the resulting database to make comparisons of specificities across multiple lectins and comparisons between glycans in their lectin receptors. Of the lectins in the database, viral lectins were the most different from other organism types, with specificities nearly always restricted to sialic acids, and mammalian lectins had the most diverse range of specificities. Certain mammalian lectins were unique in their specificities for sulfated glycans. Simple modifications to a lactosamine core structure radically altered the types of lectins that were highly specific for the glycan. Unmodified lactosamine was specifically recognized by plant, fungal, viral, and mammalian lectins; sialylation shifted the binding mainly to viral lectins; and sulfation resulted in mainly mammalian lectins with the highest specificities. We anticipate that this analysis program and database will be valuable in fundamental glycobiology studies, detailed analyses of lectin specificities, and practical applications in translational research. Molecular & Cellular Proteomics 12:

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Feasibility of Identifying Pancreatic Cancer Based on Serum Metabolomics

Cited by 148 publications

References 32 publications

A Novel Serum Metabolomics-Based Diagnostic Approach to Pancreatic Cancer

A Novel Serum Metabolomics-Based Diagnostic Approach to Pancreatic Cancer

Quality Markers Addressing Preanalytical Variations of Blood and Plasma Processing Identified by Broad and Targeted Metabolite Profiling

Global Comparisons of Lectin–Glycan Interactions Using a Database of Analyzed Glycan Array Data

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