Metabolomics based predictive classifier for early detection of pancreatic ductal adenocarcinoma

// Keith Unger 1, * , Khyati Y. Mehta 2, * , Prabhjit Kaur 2 , Yiwen Wang 3 , Smrithi S. Menon 2 , Shreyans K. Jain 2 , Rose A. Moonjelly 2 , Shubhankar Suman 4 , Kamal Datta 4 , Rajbir Singh 2 , Paul Fogel 5 and Amrita K. Cheema 2, 4 1 MedStar Georgetown University Hospital, Washington, DC, United States of America 2 Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, United States of America 3 Department of Biostatistics and Biomathematics, Georgetown University Medical Center, Washington, DC, United States of America 4 Departments of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC, United States of America 5 Unite MeDIAN, UMR CNRS 6237 MEDYC, Universite de Reims, Reims, France * These authors contributed equally to this work Correspondence to: Amrita K. Cheema, email: akc27@georgetown.edu Keywords: PDAC; tissue metabolomics; predictive biomarkers Received: January 19, 2018      Accepted: April 06, 2018      Published: May 01, 2018 ABSTRACT The availability of robust classification algorithms for the identification of high risk individuals with resectable disease is critical to improving early detection strategies and ultimately increasing survival rates in PC. We leveraged high quality biospecimens with extensive clinical annotations from patients that received treatment at the Medstar-Georgetown University hospital. We used a high resolution mass spectrometry based global tissue profiling approach in conjunction with multivariate analysis for developing a classification algorithm that would predict early stage PC with high accuracy. The candidate biomarkers were annotated using tandem mass spectrometry. We delineated a six metabolite panel that could discriminate early stage PDAC from benign pancreatic disease with >95% accuracy of classification (Specificity = 0.85, Sensitivity = 0.9). Subsequently, we used multiple reaction monitoring mass spectrometry for evaluation of this panel in plasma samples obtained from the same patients. The pattern of expression of these metabolites in plasma was found to be discordant as compared to that in tissue. Taken together, our results show the value of using a metabolomics approach for developing highly predictive panels for classification of early stage PDAC. Future investigations will likely lead to the development of validated biomarker panels with potential for clinical translation in conjunction with CA-19-9 and/or other biomarkers.