Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC)
Background: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease characterized by a diverse tumor microenvironment. The heterogeneous cellular composition of PDAC makes it challenging to study molecular features of tumor cells using extracts from bulk tumor. The metabolic features in tumor ce...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1282824/full |
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| author | Jannat Pervin Mohammad Asad Shaolong Cao Gun Ho Jang Nikta Feizi Benjamin Haibe-Kains Joanna M. Karasinska Grainne M. O’Kane Steven Gallinger David F. Schaeffer Daniel J. Renouf George Zogopoulos Oliver F. Bathe Oliver F. Bathe |
| author_facet | Jannat Pervin Mohammad Asad Shaolong Cao Gun Ho Jang Nikta Feizi Benjamin Haibe-Kains Joanna M. Karasinska Grainne M. O’Kane Steven Gallinger David F. Schaeffer Daniel J. Renouf George Zogopoulos Oliver F. Bathe Oliver F. Bathe |
| author_sort | Jannat Pervin |
| collection | DOAJ |
| description | Background: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease characterized by a diverse tumor microenvironment. The heterogeneous cellular composition of PDAC makes it challenging to study molecular features of tumor cells using extracts from bulk tumor. The metabolic features in tumor cells from clinical samples are poorly understood, and their impact on clinical outcomes are unknown. Our objective was to identify the metabolic features in the tumor compartment that are most clinically impactful.Methods: A computational deconvolution approach using the DeMixT algorithm was applied to bulk RNASeq data from The Cancer Genome Atlas to determine the proportion of each gene’s expression that was attributable to the tumor compartment. A machine learning algorithm designed to identify features most closely associated with survival outcomes was used to identify the most clinically impactful metabolic genes.Results: Two metabolic subtypes (M1 and M2) were identified, based on the pattern of expression of the 26 most important metabolic genes. The M2 phenotype had a significantly worse survival, which was replicated in three external PDAC cohorts. This PDAC subtype was characterized by net glycogen catabolism, accelerated glycolysis, and increased proliferation and cellular migration. Single cell data demonstrated substantial intercellular heterogeneity in the metabolic features that typified this aggressive phenotype.Conclusion: By focusing on features within the tumor compartment, two novel and clinically impactful metabolic subtypes of PDAC were identified. Our study emphasizes the challenges of defining tumor phenotypes in the face of the significant intratumoral heterogeneity that typifies PDAC. Further studies are required to understand the microenvironmental factors that drive the appearance of the metabolic features characteristic of the aggressive M2 PDAC phenotype. |
| first_indexed | 2024-03-11T14:46:47Z |
| format | Article |
| id | doaj.art-ec92d12728a044ddbf9be245d2f7b7e0 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-11T14:46:47Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-ec92d12728a044ddbf9be245d2f7b7e02023-10-30T11:09:19ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-10-011410.3389/fgene.2023.12828241282824Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC)Jannat Pervin0Mohammad Asad1Shaolong Cao2Gun Ho Jang3Nikta Feizi4Benjamin Haibe-Kains5Joanna M. Karasinska6Grainne M. O’Kane7Steven Gallinger8David F. Schaeffer9Daniel J. Renouf10George Zogopoulos11Oliver F. Bathe12Oliver F. Bathe13Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, CanadaDepartment of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, CanadaDepartment of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Centre, Houston, TX, United StatesOntario Institute for Cancer Research, Toronto, ON, CanadaPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, CanadaPrincess Margaret Cancer Centre, University Health Network, Toronto, ON, CanadaPancreas Centre BC, Vancouver, BC, CanadaUniversity Health Network, University of Toronto, Toronto, ON, CanadaOntario Institute for Cancer Research, Toronto, ON, CanadaDepartment of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, CanadaDepartment of Medicine, University of British Columbia, Vancouver, BC, Canada0Department of Surgery, McGill University Health Centre, McGill University, Montreal, QC, CanadaDepartment of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada1Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, CanadaBackground: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease characterized by a diverse tumor microenvironment. The heterogeneous cellular composition of PDAC makes it challenging to study molecular features of tumor cells using extracts from bulk tumor. The metabolic features in tumor cells from clinical samples are poorly understood, and their impact on clinical outcomes are unknown. Our objective was to identify the metabolic features in the tumor compartment that are most clinically impactful.Methods: A computational deconvolution approach using the DeMixT algorithm was applied to bulk RNASeq data from The Cancer Genome Atlas to determine the proportion of each gene’s expression that was attributable to the tumor compartment. A machine learning algorithm designed to identify features most closely associated with survival outcomes was used to identify the most clinically impactful metabolic genes.Results: Two metabolic subtypes (M1 and M2) were identified, based on the pattern of expression of the 26 most important metabolic genes. The M2 phenotype had a significantly worse survival, which was replicated in three external PDAC cohorts. This PDAC subtype was characterized by net glycogen catabolism, accelerated glycolysis, and increased proliferation and cellular migration. Single cell data demonstrated substantial intercellular heterogeneity in the metabolic features that typified this aggressive phenotype.Conclusion: By focusing on features within the tumor compartment, two novel and clinically impactful metabolic subtypes of PDAC were identified. Our study emphasizes the challenges of defining tumor phenotypes in the face of the significant intratumoral heterogeneity that typifies PDAC. Further studies are required to understand the microenvironmental factors that drive the appearance of the metabolic features characteristic of the aggressive M2 PDAC phenotype.https://www.frontiersin.org/articles/10.3389/fgene.2023.1282824/fullpancreatic ductal adenocarcinomapancreatic cancermetabolismdeconvolutionprognosis |
| spellingShingle | Jannat Pervin Mohammad Asad Shaolong Cao Gun Ho Jang Nikta Feizi Benjamin Haibe-Kains Joanna M. Karasinska Grainne M. O’Kane Steven Gallinger David F. Schaeffer Daniel J. Renouf George Zogopoulos Oliver F. Bathe Oliver F. Bathe Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC) Frontiers in Genetics pancreatic ductal adenocarcinoma pancreatic cancer metabolism deconvolution prognosis |
| title | Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC) |
| title_full | Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC) |
| title_fullStr | Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC) |
| title_full_unstemmed | Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC) |
| title_short | Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC) |
| title_sort | clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma pdac |
| topic | pancreatic ductal adenocarcinoma pancreatic cancer metabolism deconvolution prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1282824/full |
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