Document Details

Document Type : Article In Journal 
Document Title :
Distinct serum metabolomics profiles associated with malignant progression in the KrasG12D mouse model of pancreatic ductal adenocarcinoma.
Distinct serum metabolomics profiles associated with malignant progression in the KrasG12D mouse model of pancreatic ductal adenocarcinoma.
 
Document Language : English 
Abstract : BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer deaths worldwide with less than a 6% 5-year survival rate. PDAC is associated with poor prognosis based on the late stage diagnosis of the disease. Current diagnostic tests lack the sensitivity and specificity to identify markers of early staging. Metabolomics has provided biomarkers for various diseases, stressors, and environmental exposures. In this study we utilized the p48-Cre/LSL-KrasG12D mouse model with age-matched wild type mice. This model shows malignant progression to PDAC analogous to the human disease stages via early and late pancreatic intra-epithelial neoplasia (PanIN) lesions. RESULTS: Serum was collected from mice with early PanIN lesions (at 3-5 months) and with late PanIN or invasive PDAC lesions (13-16 months), as determined by histopathology. Metabolomics analysis of the serum samples was conducted through UPLC-TOFMS (Ultra Performance Liquid Chromatography coupled to Time-of-flight Mass Spectrometry). Multivariate data analysis revealed distinct metabolic patterns in serum samples collected during malignant progression towards invasive PDAC. Animals with early or late stage lesions were distinguished from their respective controls with 82.1% and 81.5% accuracy, respectively. This also held up for randomly selected subgroups in the late stage lesion group that showed less variability between animals. One of the metabolites, citrate, was validated through tandem mass spectrometry and showed increased levels in serum with disease progression. Furthermore, serum metabolite signatures from animals with early stage lesions identified controls and animals with late stage lesions with 81.5% accuracy (p<0.01) and vice-versa with 73.2% accuracy (p<0.01). CONCLUSIONS: We conclude that metabolomics analysis of serum samples can identify the presence of early and late stage pancreatic cancer. 
ISSN : 1471-2164 
Journal Name : BMC Genomics 
Volume : 16 
Issue Number : 1 
Publishing Year : 1436 AH
2015 AD 
Article Type : Article 
Added Date : Sunday, April 24, 2016 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Joseph J LaContiLaConti, Joseph JInvestigator  
Evagelia C LaiakisLaiakis, Evagelia CResearcher  
Anne Deslattes MaysMays, Anne DeslattesResearcher  
Ivana PeranPeran, Ivana Researcher  
Sung Eun KimKim, Sung EunResearcher  
Jerry W ShayShay, Jerry WResearcher  
Anna T RiegelRiegel, Anna TResearcher  
Albert J Fornace JrFornace Jr, Albert JResearcher  
Anton WellsteinWellstein, Anton Researcher  

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