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A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is characterized by an important heterogeneity, reflected by different clinical outcomes and chemoresistance. During carcinogenesis, tumor cells display aberrant glycosylated structures, synthetized by deregulated glycosyltransferases, supporting t...

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Autores principales: Mohamed Abd-El-Halim, Yousra, El Kaoutari, Abdessamad, Silvy, Françoise, Rubis, Marion, Bigonnet, Martin, Roques, Julie, Cros, Jérôme, Nicolle, Rémy, Iovanna, Juan, Dusetti, Nelson, Mas, Eric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379629/
https://www.ncbi.nlm.nih.gov/pubmed/34425307
http://dx.doi.org/10.1016/j.ebiom.2021.103541
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author Mohamed Abd-El-Halim, Yousra
El Kaoutari, Abdessamad
Silvy, Françoise
Rubis, Marion
Bigonnet, Martin
Roques, Julie
Cros, Jérôme
Nicolle, Rémy
Iovanna, Juan
Dusetti, Nelson
Mas, Eric
author_facet Mohamed Abd-El-Halim, Yousra
El Kaoutari, Abdessamad
Silvy, Françoise
Rubis, Marion
Bigonnet, Martin
Roques, Julie
Cros, Jérôme
Nicolle, Rémy
Iovanna, Juan
Dusetti, Nelson
Mas, Eric
author_sort Mohamed Abd-El-Halim, Yousra
collection PubMed
description BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is characterized by an important heterogeneity, reflected by different clinical outcomes and chemoresistance. During carcinogenesis, tumor cells display aberrant glycosylated structures, synthetized by deregulated glycosyltransferases, supporting the tumor progression. In this study, we aimed to determine whether PDAC could be stratified through their glycosyltransferase expression profiles better than the current binary classification (basal-like and classical) in order to improve detection of patients with poor prognosis. METHODS: Bioinformatic analysis of 169 glycosyltransferase RNA sequencing data were performed for 74 patient-derived xenografts (PDX) of resected and unresectable tumors. The Australian cohort of International Cancer Genome Consortium and the microarray dataset from Puleo patient's cohort were used as independent validation datasets. FINDINGS: New PDAC stratification based on glycosyltransferase expression profile allowed to distinguish different groups of patients with distinct clinical outcome (p-value = 0.007). A combination of 19 glycosyltransferases differentially expressed in PDX defined a glyco-signature, whose prognostic value was validated on datasets including resected whole tumor tissues. The glyco-signature was able to discriminate three clusters of PDAC patients on the validation cohorts, two clusters displaying a short overall survival compared to one cluster having a better prognosis. Both poor prognostic clusters having different glyco-profiles in Puleo patient's cohort were correlated with stroma activated or desmoplastic subtypes corresponding to distinct microenvironment features (p-value < 0.0001). Besides, differential expression and enrichment analyses revealed deregulated functional pathways specific to different clusters. INTERPRETATION: This study identifies a glyco-signature relevant for a prognostic use, potentially applicable to resected and unresectable PDAC. Furthermore, it provides new potential therapeutic targets. FUNDING: This work was supported by INCa (Grants number 2018-078 and 2018-079), Fondation ARC (Grant number ARCPJA32020070002326), Cancéropôle PACA, DGOS (labelization SIRIC, Grant number 6038), Amidex Foundation and Ligue Nationale Contre le Cancer and by institutional fundings from INSERM and the Aix-Marseille Université.
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spelling pubmed-83796292021-08-27 A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis Mohamed Abd-El-Halim, Yousra El Kaoutari, Abdessamad Silvy, Françoise Rubis, Marion Bigonnet, Martin Roques, Julie Cros, Jérôme Nicolle, Rémy Iovanna, Juan Dusetti, Nelson Mas, Eric EBioMedicine Research Paper BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is characterized by an important heterogeneity, reflected by different clinical outcomes and chemoresistance. During carcinogenesis, tumor cells display aberrant glycosylated structures, synthetized by deregulated glycosyltransferases, supporting the tumor progression. In this study, we aimed to determine whether PDAC could be stratified through their glycosyltransferase expression profiles better than the current binary classification (basal-like and classical) in order to improve detection of patients with poor prognosis. METHODS: Bioinformatic analysis of 169 glycosyltransferase RNA sequencing data were performed for 74 patient-derived xenografts (PDX) of resected and unresectable tumors. The Australian cohort of International Cancer Genome Consortium and the microarray dataset from Puleo patient's cohort were used as independent validation datasets. FINDINGS: New PDAC stratification based on glycosyltransferase expression profile allowed to distinguish different groups of patients with distinct clinical outcome (p-value = 0.007). A combination of 19 glycosyltransferases differentially expressed in PDX defined a glyco-signature, whose prognostic value was validated on datasets including resected whole tumor tissues. The glyco-signature was able to discriminate three clusters of PDAC patients on the validation cohorts, two clusters displaying a short overall survival compared to one cluster having a better prognosis. Both poor prognostic clusters having different glyco-profiles in Puleo patient's cohort were correlated with stroma activated or desmoplastic subtypes corresponding to distinct microenvironment features (p-value < 0.0001). Besides, differential expression and enrichment analyses revealed deregulated functional pathways specific to different clusters. INTERPRETATION: This study identifies a glyco-signature relevant for a prognostic use, potentially applicable to resected and unresectable PDAC. Furthermore, it provides new potential therapeutic targets. FUNDING: This work was supported by INCa (Grants number 2018-078 and 2018-079), Fondation ARC (Grant number ARCPJA32020070002326), Cancéropôle PACA, DGOS (labelization SIRIC, Grant number 6038), Amidex Foundation and Ligue Nationale Contre le Cancer and by institutional fundings from INSERM and the Aix-Marseille Université. Elsevier 2021-08-20 /pmc/articles/PMC8379629/ /pubmed/34425307 http://dx.doi.org/10.1016/j.ebiom.2021.103541 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Paper
Mohamed Abd-El-Halim, Yousra
El Kaoutari, Abdessamad
Silvy, Françoise
Rubis, Marion
Bigonnet, Martin
Roques, Julie
Cros, Jérôme
Nicolle, Rémy
Iovanna, Juan
Dusetti, Nelson
Mas, Eric
A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis
title A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis
title_full A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis
title_fullStr A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis
title_full_unstemmed A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis
title_short A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis
title_sort glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379629/
https://www.ncbi.nlm.nih.gov/pubmed/34425307
http://dx.doi.org/10.1016/j.ebiom.2021.103541
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