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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/215398
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:49:55Zoai: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:34982025-09-29 09:49:55.704CONICET 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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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1844613542016188416 |
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13.070432 |