Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance

Autores
Kaoutari, Abdessamad El; Fraunhoffer Navarro, Nicolas Alejandro; Hoare, Owen; Teyssedou, Carlos; Soubeyran, Philippe; Gayet, Odile; Roques, Julie; Lomberk, Gwen; Urrutia, Raul; Dusetti, Nelson; Iovanna, Juan Lucio
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Kaoutari, Abdessamad El. Inserm; Francia
Fil: Fraunhoffer Navarro, Nicolas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; Argentina
Fil: Hoare, Owen. Inserm; Francia
Fil: Teyssedou, Carlos. Inserm; Francia
Fil: Soubeyran, Philippe. Inserm; Francia
Fil: Gayet, Odile. Inserm; Francia
Fil: Roques, Julie. Inserm; Francia
Fil: Lomberk, Gwen. Medical College Of Wisconsin; Estados Unidos
Fil: Urrutia, Raul. Medical College Of Wisconsin; Estados Unidos
Fil: Dusetti, Nelson. Inserm; Francia
Fil: Iovanna, Juan Lucio. Inserm; Francia
Materia
CHEMOSENSITIVITY
FSG67
METABOLIC SIGNATURE
METABOLOMICS
PANCREATIC CANCER
PRECISION MEDICINE
TUMOR HETEROGENEITY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/179475
Acceder
id CONICETDig_eb59236f1a24d7401d3895c77a309328
oai_identifier_str oai:ri.conicet.gov.ar:11336/179475
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistanceKaoutari, Abdessamad ElFraunhoffer Navarro, Nicolas AlejandroHoare, OwenTeyssedou, CarlosSoubeyran, PhilippeGayet, OdileRoques, JulieLomberk, GwenUrrutia, RaulDusetti, NelsonIovanna, Juan LucioCHEMOSENSITIVITYFSG67METABOLIC SIGNATUREMETABOLOMICSPANCREATIC CANCERPRECISION MEDICINETUMOR HETEROGENEITYhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Background: 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.Fil: Kaoutari, Abdessamad El. Inserm; FranciaFil: Fraunhoffer Navarro, Nicolas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; ArgentinaFil: Hoare, Owen. Inserm; FranciaFil: Teyssedou, Carlos. Inserm; FranciaFil: Soubeyran, Philippe. Inserm; FranciaFil: Gayet, Odile. Inserm; FranciaFil: Roques, Julie. Inserm; FranciaFil: Lomberk, Gwen. Medical College Of Wisconsin; Estados UnidosFil: Urrutia, Raul. Medical College Of Wisconsin; Estados UnidosFil: Dusetti, Nelson. Inserm; FranciaFil: Iovanna, Juan Lucio. Inserm; FranciaElsevier2021-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/179475Kaoutari, Abdessamad El; Fraunhoffer Navarro, Nicolas Alejandro; Hoare, Owen; Teyssedou, Carlos; Soubeyran, Philippe; et al.; Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance; Elsevier; EBioMedicine; 66; 4-2021; 1-132352-3964CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ebiom.2021.103332info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2352396421001250?via%3Dihubinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:52:12Zoai:ri.conicet.gov.ar:11336/179475instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:52:13.025CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance
title Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance
spellingShingle Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance
Kaoutari, Abdessamad El
CHEMOSENSITIVITY
FSG67
METABOLIC SIGNATURE
METABOLOMICS
PANCREATIC CANCER
PRECISION MEDICINE
TUMOR HETEROGENEITY
title_short Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance
title_full Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance
title_fullStr Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance
title_full_unstemmed Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance
title_sort Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance
dc.creator.none.fl_str_mv Kaoutari, Abdessamad El
Fraunhoffer Navarro, Nicolas Alejandro
Hoare, Owen
Teyssedou, Carlos
Soubeyran, Philippe
Gayet, Odile
Roques, Julie
Lomberk, Gwen
Urrutia, Raul
Dusetti, Nelson
Iovanna, Juan Lucio
author Kaoutari, Abdessamad El
author_facet Kaoutari, Abdessamad El
Fraunhoffer Navarro, Nicolas Alejandro
Hoare, Owen
Teyssedou, Carlos
Soubeyran, Philippe
Gayet, Odile
Roques, Julie
Lomberk, Gwen
Urrutia, Raul
Dusetti, Nelson
Iovanna, Juan Lucio
author_role author
author2 Fraunhoffer Navarro, Nicolas Alejandro
Hoare, Owen
Teyssedou, Carlos
Soubeyran, Philippe
Gayet, Odile
Roques, Julie
Lomberk, Gwen
Urrutia, Raul
Dusetti, Nelson
Iovanna, Juan Lucio
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv CHEMOSENSITIVITY
FSG67
METABOLIC SIGNATURE
METABOLOMICS
PANCREATIC CANCER
PRECISION MEDICINE
TUMOR HETEROGENEITY
topic CHEMOSENSITIVITY
FSG67
METABOLIC SIGNATURE
METABOLOMICS
PANCREATIC CANCER
PRECISION MEDICINE
TUMOR HETEROGENEITY
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv 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.
Fil: Kaoutari, Abdessamad El. Inserm; Francia
Fil: Fraunhoffer Navarro, Nicolas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Centro de Estudios Farmacológicos y Botánicos. Universidad de Buenos Aires. Facultad de Medicina. Centro de Estudios Farmacológicos y Botánicos; Argentina
Fil: Hoare, Owen. Inserm; Francia
Fil: Teyssedou, Carlos. Inserm; Francia
Fil: Soubeyran, Philippe. Inserm; Francia
Fil: Gayet, Odile. Inserm; Francia
Fil: Roques, Julie. Inserm; Francia
Fil: Lomberk, Gwen. Medical College Of Wisconsin; Estados Unidos
Fil: Urrutia, Raul. Medical College Of Wisconsin; Estados Unidos
Fil: Dusetti, Nelson. Inserm; Francia
Fil: Iovanna, Juan Lucio. Inserm; Francia
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/179475
Kaoutari, Abdessamad El; Fraunhoffer Navarro, Nicolas Alejandro; Hoare, Owen; Teyssedou, Carlos; Soubeyran, Philippe; et al.; Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance; Elsevier; EBioMedicine; 66; 4-2021; 1-13
2352-3964
CONICET Digital
CONICET
url http://hdl.handle.net/11336/179475
identifier_str_mv Kaoutari, Abdessamad El; Fraunhoffer Navarro, Nicolas Alejandro; Hoare, Owen; Teyssedou, Carlos; Soubeyran, Philippe; et al.; Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance; Elsevier; EBioMedicine; 66; 4-2021; 1-13
2352-3964
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ebiom.2021.103332
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S2352396421001250?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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