Correction: Integration of metabolites from meta-analysis with transcriptome reveals enhanced SPHK1 in PDAC with a background of pancreatitis (original) (raw)
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International journal of cancer, 2018
Using a metabolomics approach, we systematically searched for circulating metabolite biomarkers for pancreatic cancer risk in a case-control study nested within two prospective Shanghai cohorts. Included in this study were 226 incident pancreatic cancer cases and their individually-matched controls. Untargeted mass spectrometry platforms were used to measure metabolites in blood samples collected prior to cancer diagnosis. Conditional logistic regression was performed to assess the associations of metabolites with pancreatic cancer risk. We identified 10 metabolites associated with pancreatic cancer, after accounting for multiple comparisons (the Benjamini-Hochberg false discovery rate < 0.05). The majority of the identified metabolites were glycerophospholipids (ORs per SD increase: 0.44 to 2.32; p values: 7.2 × 10 to 1.0 × 10 ), six of which were associated with decreased risk and one with increased risk. Additionally, levels of coumarin (OR = 1.96, p = 3.7 × 10 ) and picolinic...
Meta-Analysis Reveals Both the Promises and the Challenges of Clinical Metabolomics
Cancers
Clinical metabolomics is a rapidly expanding field focused on identifying molecular biomarkers to aid in the efficient diagnosis and treatment of human diseases. Variations in study design, metabolomics methodologies, and investigator protocols raise serious concerns about the accuracy and reproducibility of these potential biomarkers. The explosive growth of the field has led to the recent availability of numerous replicate clinical studies, which permits an evaluation of the consistency of biomarkers identified across multiple metabolomics projects. Pancreatic ductal adenocarcinoma (PDAC) is the third-leading cause of cancer-related death and has the lowest five-year survival rate primarily due to the lack of an early diagnosis and the limited treatment options. Accordingly, PDAC has been a popular target of clinical metabolomics studies. We compiled 24 PDAC metabolomics studies from the scientific literature for a detailed meta-analysis. A consistent identification across these m...
Plasma Metabolome Profiling Identifies Metabolic Subtypes of Pancreatic Ductal Adenocarcinoma
Cells, 2021
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Developing biomarkers for early detection and chemotherapeutic response prediction is crucial to improve the dismal prognosis of PDAC patients. However, molecular cancer signatures based on transcriptome analysis do not reflect intratumoral heterogeneity. To explore a more accurate stratification of PDAC phenotypes in an easily accessible matrix, plasma metabolome analysis using MxP® Global Profiling and MxP® Lipidomics was performed in 361 PDAC patients. We identified three metabolic PDAC subtypes associated with distinct complex lipid patterns. Subtype 1 was associated with reduced ceramide levels and a strong enrichment of triacylglycerols. Subtype 2 demonstrated increased abundance of ceramides, sphingomyelin and other complex sphingolipids, whereas subtype 3 showed decreased levels of sphingolipid metabolites in plasma. Pathway enrichment analysis revealed that sphingolipid-related pathways differ most amo...
Oncotarget, 2015
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. 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. 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, oleanoli...
Circulating Metabolites and Survival Among Patients With Pancreatic Cancer
Journal of the National Cancer Institute, 2016
Pancreatic tumors cause changes in whole-body metabolism, but whether prediagnostic circulating metabolites predict survival is unknown. We measured 82 metabolites by liquid chromatography-mass spectrometry in prediagnostic plasma from 484 pancreatic cancer case patients enrolled in four prospective cohort studies. Association of metabolites with survival was evaluated using Cox proportional hazards models adjusted for age, cohort, race/ethnicity, cancer stage, fasting time, and diagnosis year. After multiple-hypothesis testing correction, a P value of .0006 or less (.05/82) was considered statistically significant. Based on the results, we evaluated 33 tagging single-nucleotide polymorphisms (SNPs) in the ACO1 gene, requiring a P value of less than .002 (.05/33) for statistical significance. All statistical tests were two-sided. Two metabolites in the tricarboxylic acid (TCA) cycle-isocitrate and aconitate-were statistically significantly associated with survival. Participants in t...
Metabolomic biomarkers of pancreatic cancer - a meta-analysis study
Oncotarget
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.
EBioMedicine, 2021
Background: Although significant advances have been made recently to characterize the biology of pancreatic ductal adenocarcinoma (PDAC), more efforts are needed to improve our understanding and to face challenges related to the aggressiveness, high mortality rate and chemoresistance of this disease. Methods: In this study, we perform the metabolomics profiling of 77 PDAC patient-derived tumor xenografts (PDTX) to investigate the relationship of metabolic profiles with overall survival (OS) in PDAC patients, tumor phenotypes and resistance to five anticancer drugs (gemcitabine, oxaliplatin, docetaxel, SN-38 and 5-Fluorouracil). Findings: We identified a metabolic signature that was able to predict the clinical outcome of PDAC patients (p < 0.001, HR=2.68 [95% CI: 1.5À4.9]). The correlation analysis showed that this metabolomic signature was significantly correlated with the PDAC molecular gradient (PAMG) (R = 0.44 and p < 0.001) indicating significant association to the transcriptomic phenotypes of tumors. Resistance score established, based on growth rate inhibition metrics using 35 PDTX-derived primary cells, allowed to identify several metabolites related to drug resistance which was globally accompanied by accumulation of several diacy-phospholipids and decrease in lysophospholipids. Interestingly, targeting glycerophospholipid synthesis improved sensitivity to the three tested cytotoxic drugs indicating that interfering with metabolism could be a promising therapeutic strategy to overcome the challenging resistance of PDAC. Interpretation: In conclusion, this study shows that the metabolomic profile of pancreatic PDTX models is strongly associated to clinical outcome, transcriptomic phenotypes and drug resistance. We also showed that targeting the lipidomic profile could be used in combinatory therapies against chemoresistance in PDAC.
Altered Sphingolipid Metabolism in Patients with Metastatic Pancreatic Cancer
Biomolecules, 2013
Although numerous genetic mutations and amplifications have been identified in pancreatic cancer, much of the molecular pathogenesis of the disease remains undefined. While proteomic and transcriptomic analyses have been utilized to probe and characterize pancreatic tumors, lipidomic analyses have not been applied to identify perturbations in pancreatic cancer patient samples. Thus, we utilized a mass spectrometry-based lipidomic approach, focused towards the sphingolipid class of lipids, to quantify changes in human pancreatic cancer tumor and plasma specimens. Subgroup analysis revealed that patients with positive lymph node metastasis have a markedly higher level of ceramide species (C16:0 and C24:1) in their tumor specimens compared to pancreatic cancer patients without nodal disease or to patients with pancreatitis. Also of interest, ceramide metabolites, including phosphorylated (sphingosine-and sphinganine-1-phosphate) and glycosylated (cerebroside) species were elevated in the plasma, but not the pancreas, of pancreatic cancer patients with nodal disease. Analysis of plasma level of cytokine and growth factors revealed that IL-6, IL-8, CCL11 (eotaxin), EGF and IP10 (interferon inducible protein 10, CXCL10) were elevated in patients with positive lymph nodes metastasis, but that only IP10 and EGF directly correlated with several sphingolipid
Pancreatic Ductal Adenocarcinoma is Associated with a Distinct Urinary Metabolomic Signature
Annals of Surgical Oncology, 2012
Background. Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with poor prognosis in part due to the lack of early detection and screening methods. Metabolomics provides a means for noninvasive screening of tumor-associated perturbations in cellular metabolism. Methods. Urine samples of PDAC patients (n = 32), healthy age and gender-matched controls (n = 32), and patients with benign pancreatic conditions (n = 25) were examined using 1 H-NMR spectroscopy. Targeted profiling of spectra permitted quantification of 66 metabolites. Unsupervised (principal component analysis, PCA) and supervised (orthogonal partial-least squares discriminant analysis, OPLS-DA) multivariate pattern recognition techniques were applied to discriminate between sample spectra using SIMCA-P ? (version 12, Umetrics, Sweden). Results. Clear distinction between PDAC and controls was noted when using OPLS-DA. Significant differences in metabolite concentrations between cancers and controls (p \ 0.001) were noted. Model parameters for both goodness of fit, and predictive capability were high (R 2 = 0.85; Q 2 = 0.59, respectively). Internal validation methods were used to confirm model validity. Sensitivity and specificity of the multivariate OPLS-DA model were summarized using a receiver operating characteristics (ROC) curve, with an area under the curve (AUROC) = 0.988, indicating strong predictive power. Preliminary analysis revealed an AUROC = 0.958 for the model of benign pancreatic disease compared with PDAC, and suggest that the cancer-associated metabolomic signature dissipates following RO resection. Conclusions. Urinary metabolomics detected distinct differences in the metabolic profiles of pancreatic cancer compared with healthy controls and benign pancreatic disease. These preliminary results suggest that metabolomic approaches may facilitate discovery of novel pancreatic cancer biomarkers.
Kanchan 5 Metabolomics 2013, 9, 515–528
Proton NMR based metabolic profile of serum associated with different gallbladder pathologies is presented. Quantitative and qualitative variations in the metabolic profile of serum in control samples and three different pathologies of gallbladder, chronic cholecystitis, xanthogranulomatous cholecystitis and carcinoma of gallbladder has been evaluated by use of 1 H NMR based metabonomics and multivariate chemometric methods. Multivariate partial least square discriminant analysis of 1 H NMR spectra showed a clear discrimination between control and diseased groups on the basis of quantitative and qualitative metabolic variations. Increased levels of lactate and pyruvate whereas decreased levels of glucose, some amino acids and low density lipoprotein/very low density lipoprotein (LDL/VLDL) were observed. These metabolites, responsible for class discrimination, from different metabolic pathways could be considered as the signatures of the carcinoma of gallbladder.