Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction

Autores
Fraunhoffer Navarro, Nicolas Alejandro; Abuelafia, Analía Meilerman; Bigonnet, Martin; Gayet, Odile; Roques, Julie; Nicolle, Remy; Lomberk, Gwen; Urrutia, Raul; Dusetti, Nelson; Iovanna, Juan Lucio
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Pancreatic ductal adenocarcinoma (PDAC), has recently been found to be a heterogeneous disease, although the extension of its diversity remains to be fully understood. Here, we harmonize transcriptomic profiles derived from both PDAC epithelial and microenvironment cells to develop a Master Regulators (MR)-Gradient model that allows important inferences on transcriptional networks, epigenomic states, and metabolomics pathways that underlies this disease heterogeneity. This gradient model was generated by applying a blind source separation based on independent components analysis and robust principal component analyses (RPCA), following regulatory network inference. The result of these analyses reveals that PDAC prognosis strongly associates with the tumor epithelial cell phenotype and the immunological component. These studies were complemented by integration of methylome and metabolome datasets generated from patient-derived xenograft (PDX), together experimental measurements of metabolites, immunofluorescence microscopy, and western blot. At the metabolic level, PDAC favorable phenotype showed a positive correlation with enzymes implicated in complex lipid biosynthesis. In contrast, the unfavorable phenotype displayed an augmented OXPHOS independent metabolism centered on the Warburg effect and glutaminolysis. Epigenetically, we find that a global hypermethylation profile associates with the worst prognosis. Lastly, we report that, two antagonistic histone code writers, SUV39H1/SUV39H2 (H3K9Me3) and KAT2B (H3K9Ac) were identified key deregulated pathways in PDAC. Our analysis suggests that the PDAC phenotype, as it relates to prognosis, is determined by a complex interaction of transcriptomic, epigenomic, and metabolic features. Furthermore, we demonstrated that PDAC prognosis could be modulated through epigenetics.
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. Centre de Recherche en Cancérologie de Marseille; Francia. Universidad de Buenos Aires. Facultad de Medicina; Argentina
Fil: Abuelafia, Analía Meilerman. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Bigonnet, Martin. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Gayet, Odile. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Roques, Julie. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Nicolle, Remy. French League Against Cancer; Francia
Fil: Lomberk, Gwen. Medical College Of Wisconsin; Estados Unidos
Fil: Urrutia, Raul. Medical College Of Wisconsin; Estados Unidos
Fil: Dusetti, Nelson. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Iovanna, Juan Lucio. Centre de Recherche en Cancérologie de Marseille; Francia
Materia
PDAC
MULTIOMICS
GRADIENTS
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:11336/215398
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic predictionFraunhoffer Navarro, Nicolas AlejandroAbuelafia, Analía MeilermanBigonnet, MartinGayet, OdileRoques, JulieNicolle, RemyLomberk, GwenUrrutia, RaulDusetti, NelsonIovanna, Juan LucioPDACMULTIOMICSGRADIENTShttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Pancreatic ductal adenocarcinoma (PDAC), has recently been found to be a heterogeneous disease, although the extension of its diversity remains to be fully understood. Here, we harmonize transcriptomic profiles derived from both PDAC epithelial and microenvironment cells to develop a Master Regulators (MR)-Gradient model that allows important inferences on transcriptional networks, epigenomic states, and metabolomics pathways that underlies this disease heterogeneity. This gradient model was generated by applying a blind source separation based on independent components analysis and robust principal component analyses (RPCA), following regulatory network inference. The result of these analyses reveals that PDAC prognosis strongly associates with the tumor epithelial cell phenotype and the immunological component. These studies were complemented by integration of methylome and metabolome datasets generated from patient-derived xenograft (PDX), together experimental measurements of metabolites, immunofluorescence microscopy, and western blot. At the metabolic level, PDAC favorable phenotype showed a positive correlation with enzymes implicated in complex lipid biosynthesis. In contrast, the unfavorable phenotype displayed an augmented OXPHOS independent metabolism centered on the Warburg effect and glutaminolysis. Epigenetically, we find that a global hypermethylation profile associates with the worst prognosis. Lastly, we report that, two antagonistic histone code writers, SUV39H1/SUV39H2 (H3K9Me3) and KAT2B (H3K9Ac) were identified key deregulated pathways in PDAC. Our analysis suggests that the PDAC phenotype, as it relates to prognosis, is determined by a complex interaction of transcriptomic, epigenomic, and metabolic features. Furthermore, we demonstrated that PDAC prognosis could be modulated through epigenetics.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. Centre de Recherche en Cancérologie de Marseille; Francia. Universidad de Buenos Aires. Facultad de Medicina; ArgentinaFil: Abuelafia, Analía Meilerman. Centre de Recherche en Cancérologie de Marseille; FranciaFil: Bigonnet, Martin. Centre de Recherche en Cancérologie de Marseille; FranciaFil: Gayet, Odile. Centre de Recherche en Cancérologie de Marseille; FranciaFil: Roques, Julie. Centre de Recherche en Cancérologie de Marseille; FranciaFil: Nicolle, Remy. French League Against Cancer; FranciaFil: Lomberk, Gwen. Medical College Of Wisconsin; Estados UnidosFil: Urrutia, Raul. Medical College Of Wisconsin; Estados UnidosFil: Dusetti, Nelson. Centre de Recherche en Cancérologie de Marseille; FranciaFil: Iovanna, Juan Lucio. Centre de Recherche en Cancérologie de Marseille; FranciaNature Publishing Group2022-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/215398Fraunhoffer Navarro, Nicolas Alejandro; Abuelafia, Analía Meilerman; Bigonnet, Martin; Gayet, Odile; Roques, Julie; et al.; Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction; Nature Publishing Group; npj Precision Oncology; 6; 1; 12-2022; 1-162397-768XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/s41698-022-00299-zinfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41698-022-00299-zinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:19:15Zoai:ri.conicet.gov.ar:11336/215398instacron: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:34982025-10-22 11:19:16.127CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction
title Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction
spellingShingle Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction
Fraunhoffer Navarro, Nicolas Alejandro
PDAC
MULTIOMICS
GRADIENTS
title_short Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction
title_full Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction
title_fullStr Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction
title_full_unstemmed Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction
title_sort Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction
dc.creator.none.fl_str_mv Fraunhoffer Navarro, Nicolas Alejandro
Abuelafia, Analía Meilerman
Bigonnet, Martin
Gayet, Odile
Roques, Julie
Nicolle, Remy
Lomberk, Gwen
Urrutia, Raul
Dusetti, Nelson
Iovanna, Juan Lucio
author Fraunhoffer Navarro, Nicolas Alejandro
author_facet Fraunhoffer Navarro, Nicolas Alejandro
Abuelafia, Analía Meilerman
Bigonnet, Martin
Gayet, Odile
Roques, Julie
Nicolle, Remy
Lomberk, Gwen
Urrutia, Raul
Dusetti, Nelson
Iovanna, Juan Lucio
author_role author
author2 Abuelafia, Analía Meilerman
Bigonnet, Martin
Gayet, Odile
Roques, Julie
Nicolle, Remy
Lomberk, Gwen
Urrutia, Raul
Dusetti, Nelson
Iovanna, Juan Lucio
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv PDAC
MULTIOMICS
GRADIENTS
topic PDAC
MULTIOMICS
GRADIENTS
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Pancreatic ductal adenocarcinoma (PDAC), has recently been found to be a heterogeneous disease, although the extension of its diversity remains to be fully understood. Here, we harmonize transcriptomic profiles derived from both PDAC epithelial and microenvironment cells to develop a Master Regulators (MR)-Gradient model that allows important inferences on transcriptional networks, epigenomic states, and metabolomics pathways that underlies this disease heterogeneity. This gradient model was generated by applying a blind source separation based on independent components analysis and robust principal component analyses (RPCA), following regulatory network inference. The result of these analyses reveals that PDAC prognosis strongly associates with the tumor epithelial cell phenotype and the immunological component. These studies were complemented by integration of methylome and metabolome datasets generated from patient-derived xenograft (PDX), together experimental measurements of metabolites, immunofluorescence microscopy, and western blot. At the metabolic level, PDAC favorable phenotype showed a positive correlation with enzymes implicated in complex lipid biosynthesis. In contrast, the unfavorable phenotype displayed an augmented OXPHOS independent metabolism centered on the Warburg effect and glutaminolysis. Epigenetically, we find that a global hypermethylation profile associates with the worst prognosis. Lastly, we report that, two antagonistic histone code writers, SUV39H1/SUV39H2 (H3K9Me3) and KAT2B (H3K9Ac) were identified key deregulated pathways in PDAC. Our analysis suggests that the PDAC phenotype, as it relates to prognosis, is determined by a complex interaction of transcriptomic, epigenomic, and metabolic features. Furthermore, we demonstrated that PDAC prognosis could be modulated through epigenetics.
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. Centre de Recherche en Cancérologie de Marseille; Francia. Universidad de Buenos Aires. Facultad de Medicina; Argentina
Fil: Abuelafia, Analía Meilerman. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Bigonnet, Martin. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Gayet, Odile. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Roques, Julie. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Nicolle, Remy. French League Against Cancer; Francia
Fil: Lomberk, Gwen. Medical College Of Wisconsin; Estados Unidos
Fil: Urrutia, Raul. Medical College Of Wisconsin; Estados Unidos
Fil: Dusetti, Nelson. Centre de Recherche en Cancérologie de Marseille; Francia
Fil: Iovanna, Juan Lucio. Centre de Recherche en Cancérologie de Marseille; Francia
description Pancreatic ductal adenocarcinoma (PDAC), has recently been found to be a heterogeneous disease, although the extension of its diversity remains to be fully understood. Here, we harmonize transcriptomic profiles derived from both PDAC epithelial and microenvironment cells to develop a Master Regulators (MR)-Gradient model that allows important inferences on transcriptional networks, epigenomic states, and metabolomics pathways that underlies this disease heterogeneity. This gradient model was generated by applying a blind source separation based on independent components analysis and robust principal component analyses (RPCA), following regulatory network inference. The result of these analyses reveals that PDAC prognosis strongly associates with the tumor epithelial cell phenotype and the immunological component. These studies were complemented by integration of methylome and metabolome datasets generated from patient-derived xenograft (PDX), together experimental measurements of metabolites, immunofluorescence microscopy, and western blot. At the metabolic level, PDAC favorable phenotype showed a positive correlation with enzymes implicated in complex lipid biosynthesis. In contrast, the unfavorable phenotype displayed an augmented OXPHOS independent metabolism centered on the Warburg effect and glutaminolysis. Epigenetically, we find that a global hypermethylation profile associates with the worst prognosis. Lastly, we report that, two antagonistic histone code writers, SUV39H1/SUV39H2 (H3K9Me3) and KAT2B (H3K9Ac) were identified key deregulated pathways in PDAC. Our analysis suggests that the PDAC phenotype, as it relates to prognosis, is determined by a complex interaction of transcriptomic, epigenomic, and metabolic features. Furthermore, we demonstrated that PDAC prognosis could be modulated through epigenetics.
publishDate 2022
dc.date.none.fl_str_mv 2022-12
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/11336/215398
Fraunhoffer Navarro, Nicolas Alejandro; Abuelafia, Analía Meilerman; Bigonnet, Martin; Gayet, Odile; Roques, Julie; et al.; Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction; Nature Publishing Group; npj Precision Oncology; 6; 1; 12-2022; 1-16
2397-768X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/215398
identifier_str_mv Fraunhoffer Navarro, Nicolas Alejandro; Abuelafia, Analía Meilerman; Bigonnet, Martin; Gayet, Odile; Roques, Julie; et al.; Multi-omics data integration and modeling unravels new mechanisms for pancreatic cancer and improves prognostic prediction; Nature Publishing Group; npj Precision Oncology; 6; 1; 12-2022; 1-16
2397-768X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1038/s41698-022-00299-z
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41698-022-00299-z
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 Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
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 12.982451

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