SERUM METABOLITES AS DIAGNOSTIC BIOMARKERS FOR CHOLANGIOCARCINOMA, HEPATOCELLULAR CARCINOMA AND PRIMARY SCLEROSING CHOLANGITIS J.M. BANALES1,2,3, M. IÑARRAIRAEGUI1,4, A. ARBELAIZ2, P. MILKIEWICZ5, J. MUNTANE1,6, A. LA CASTA2, L. MUÑOZ-BELLVIS7, L.M. GONZALEZ7, E. ARRETXE8, C. ALONSO8, I. MARTÍNEZ-ARRANZ8, I. MARTÍNEZ-ARRANZ8, A. LAPITZ2, A. SANTOS-LASO2, M.A. AVILA1,9, M.L. MARTINEZ-CHANTAR1,10, L. BUJANDA1,2, J.J.G. MARIN1,11, B. SANGRO1,4, R.I.R. MACIAS1,11 1 National Institute for the Study of Liver and Gastrointestinal Diseases (CIBERehd, Carlos III Health Institute), Madrid, Spain. 2 Department of Liver and Gastrointestinal Diseases, Biodonostia Research Institute, Donostia University Hospital, University of the Basque Country (UPV/EHU), San Sebastian, Spain. 3 IKERBASQUE, Basque Foundation for Science, Bilbao, Spain. 4 Liver Unit, Clínica Universidad de Navarra- IDISNA, Pamplona, Spain. 5 Liver and Internal Medicine Unit, Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Warsaw, Poland. 6 Department of General Surgery “Virgen del Rocío” University Hospital/IBiS/CSIC/University of Seville, Seville, Spain. 7 Service of General and Gastrointestinal Surgery, University Hospital of Salamanca, Biomedical Research Institute of Salamanca (IBSAL), CIBERONC, Salamanca, Spain. 8 OWL Metabolomics, Bizkaia Technology Park, Derio Spain. 9 Program of Hepatology, Center for Applied Medical Research (CIMA), University of Navarra-IDISNA, Pamplona. 10 CIC bioGUNE, Bizkaia Technology Park, Derio, Spain. 11 Experimental Hepatology and Drug Targeting (HEVEFARM), University of Salamanca, Biomedical Research Institute of Salamanca (IBSAL), Salamanca, Spain. 1 INTRODUCTION 4 RESULTS Figure 4. Metabolic profiling workflow. (A) Metabolite extraction was Early and differential diagnosis of intrahepatic accomplished by cholangiocarcinoma (iCCA) and hepatocellular fractionating samples into carcinoma (HCC) by non-invasive methods pools of species with similar physicochemical Poster presented at: presented Poster represents a current clinical challenge (1,2). The properties using analysis of low-molecular weight metabolites by appropriate combinations of organic solvents. (B) new high-throughput techniques is a novel Three separate UHPLC- strategy for identifying biomarkers. MS-based platforms were B used. (C) Data pre- processing generated a list of chromatographic peak areas for the metabolites detected in each sample injection. An approximated linear detection range was defined for each identified metabolite.. (D) Once normalized, multivariate 2 and univariate data AIM C analyses were performed. (E) Selection of the best To investigate whether serum metabolome can candidate biomarkers. DOI: 10.3252/pso.eu.ILC2019.2019 provide useful biomarkers in the diagnosis of iCCA and HCC and could discriminate iCCA from HCC. Since primary sclerosing cholangitis (PSC) is a risk 5 CONCLUSIONS factor for CCA, serum metabolomic profiles of PSC and CCA have also been compared. Specific changes in serum concentrations of certain D metabolites are useful to differentiate iCCA from HCC or PSC, and could help in the early diagnosis of these diseases. 3 METHOD • Serum samples from iCCA, HCC, PSC, and Figure 2. PCA plots of human serum samples. (A) Colors represent the origin of the samples (1: Pamplona, 2: Salamanca, healthy individuals (n=20/group in the 3: San Sebastian, 4: Seville, 5: Warsaw), (B) gender, (C) age range, (D) presence or absence of cirrhosis, (E) iCCA stage E “discovery cohort” and n=14-15/group in the group, (F) HCC stage group, (G) group of sample and (H) discovery or validation cohort. Each dot represents one sample. “validation cohort”) and from non-alcoholic fatty 6 REFERENCES liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) patients (n=20/group) iCCA vs HCC iCCA vs PSC 1- Macias et al. Biochim Biophys Acta, 1864:1468-1477. were used. A B 2- Banales et al. Nat Rev Gastroenterol Hepatol 2016; 13: 261-80. 3- Barr et al. J Proteome Res 2012; 11: 2521-32. • Inclusion criteria for patients with iCCA and HCC included the following: 1) diagnosis Banales et al. Hepatology (in press) doi: 10.1002/hep.30319. confirmed by histopathology; 2) availability of Figure 1. Comparative serum metabolomic profiles in the discovery cohort. serum samples obtained just after diagnosis Volcano plot [-log10(p-value) and log2(fold-change)] of the serum metabolites and before patients had received any type of of iCCA patients compared to Controls (A), of HCC patients compared to treatment; and 3) availability of complete Controls (B), of iCCA patients compared to HCC patients (C), of PSC patients compared to Controls (D) and of iCCA patients compared to PSC. 7 CONTACT demographic and clinical information. AA, amino acids; AC, acylcarnitines; AUC, area under the receiver operating characteristic curve; BA, bile acids; Cer, ceramides; ChoE, cholesteryl esters; • Lipids and aminoacids in methanol and CMH, monohexosylceramides; DG, diglycerides; FC, fold-change; FSB, free General hepatology Rocio Ir Macias sphingoid bases; LPC, lysophosphatidylcholines; LPE, Rocio I.R. Macias chloroform/methanol serum extracts were lysophosphatidylethanolamines; LPI, lysophosphatidylinositols; MUFA, Department of Physiology and Pharmacology. Campus Miguel analysed using ultra-high performance liquid monounsaturated fatty acids; NAE, N-acyl ethanolamines; oxFA, oxidized de Unamuno E.D. Lab. B-17 chromatography (UHPLC)-Time of Flight-MS fatty acids; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PI, phosphatidylinositols; PUFA, polyunsaturated fatty acids; SEN, sensitivity; Figure 3. Diagnostic prediction capacity of the selected metabolites in iCCA vs HCC, and iCCA vs PSC in discovery and validation cohorts. (A) 37007-Salamanca, Spain based platforms (3). SFA, saturated fatty acids; SM, sphingomyelins; SPE, specificity; ST, steroids; Combination of aspartic acid, glycine, SM(42:3) and SM(43:2) in serum iCCA vs HCC. (B) Combination of histidine and PC(34:3) in serum Telephone: 34-666 596 621 E-mail: [email protected] TG, triglycerides. iCCA vs PSC; AUC, area under the receiver operating characteristic curve; SEN, sensitivity; SPE, specificity. SAT-425 ILC2019.