Abstract 1768: Comprehensive mass spectrometry based metabolomic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer

Abstract

Abstract Background: Poor outcome of pancreatic cancer (PDAC) necessitates development of early diagnostic method to reduce mortality. In current study, metabolic profiling of plasma samples from selected cancer patients and noncancerous controls was performed to seek novel metabolic biomarkers of PDAC. Comprehensive mass spectrometry based analytical platform were applied for detection of compounds in the plasma from early stage PDAC patients. Methods: Plasma available from the patients involved in the IRB-approved UC Davis Pancreas Registry was utilized. Five histologically proven cases of early stage pancreatic cancer without clinical evidence of distant metastasis [AJCC TNM stages: IB (2) and IIB (3)] and 5 noncancerous cases [3 chronic pancreatitis, 2 normal-based on clinical and follow-up evidences] were chosen from the Registry. Combination mass spectrometry (MS) methods, GC-TOF-, HILIC-LC-ESI-, and RP-LC-ESI-MS were applied for the plasma analysis. Data values obtained were evaluated for principle component analysis (PCA). Further feature selection and ANOVA were conducted after the PCA. Statistical significance was defined as p-value less than 0.05. Feature components with statistically significant classifiers of the PDAC were analyzed further with LTQ-Orbitrap MS for accurate mass measurement. Mass spectra of potential biomarkers thus obtained were spectrally corrected and unique elemental formula was searched against CAS database using the strategy of Explore Substances-Chemical Structure for known compounds. Mass Frontier 5.0 was used for MS/MS fragmentation modeling analysis; with parent compounds that had the best match of MS/MS fragmentation pattern were considered as the molecular structure of the potential biomarkers. Results: The candidate biomarkers included amino acids (N-methylalanine, lysine, glutamine, phenylalanine), fatty acids (arachidonic acid), lipids (lysoPC (18:2), PC (34:2), PE (26:0)), and bile acids (tauroursodeoxycholic and taurocholic acids, deoxycholylglycine, cholylglycine). Conclusion: We have demonstrated that combination of the comprehensive mass spectrometry based metabolic profiling techniques, data mining, and data analysis successfully find potent classifiers for pancreatic cancer in plasma. These metabolites upon validation, selectivity study, and broader cohort study could become clinical biomarkers for early diagnosis of pancreatic cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1768.