Abdessamad El Kaoutari1, Nicolas A Fraunhoffer2, Owen Hoare2, Carlos Teyssedou2, Philippe Soubeyran2, Odile Gayet2, Julie Roques2, Gwen Lomberk3, Raul Urrutia3, Nelson Dusetti4, Juan Iovanna5. 1. Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Parc Scientifique et Technologique de Luminy, Marseille, France; COMPO unit, Inria Sophia Antipolis and CRCM, INSERM U1068, CNRS UMR7258, Aix-Marseille Université UM105, Marseille, France. 2. Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Parc Scientifique et Technologique de Luminy, Marseille, France. 3. Genomics and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI, USA; Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI Center, Medical College of Wisconsin, Milwaukee, WI, USA. 4. Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Parc Scientifique et Technologique de Luminy, Marseille, France. Electronic address: nelson.dusetti@inserm.fr. 5. Centre de Recherche en Cancérologie de Marseille (CRCM), INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Parc Scientifique et Technologique de Luminy, Marseille, France. Electronic address: juan.iovanna@inserm.fr.
Abstract
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: 1.5-4.9]). 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.
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 PDACpatient-derived tumor xenografts (PDTX) to investigate the relationship of metabolic profiles with overall survival (OS) in PDACpatients, 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 PDACpatients (p < 0.001, HR=2.68 [95% CI: 1.5-4.9]). 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.