Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC)

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

doi:10.3389/fgene.2023.1282824

Clinically impactful metabolic subtypes of pancreatic ductal adenocarcinoma (PDAC)

Front Genet. 2023 Oct 30:14:1282824. doi: 10.3389/fgene.2023.1282824. eCollection 2023.

Authors

Affiliations

¹ Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
² Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada.
³ Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Centre, Houston, TX, United States.
⁴ Ontario Institute for Cancer Research, Toronto, ON, Canada.
⁵ Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
⁶ Pancreas Centre BC, Vancouver, BC, Canada.
⁷ University Health Network, University of Toronto, Toronto, ON, Canada.
⁸ Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
⁹ Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
¹⁰ Department of Surgery, McGill University Health Centre, McGill University, Montreal, QC, Canada.
¹¹ Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

Abstract

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.

Keywords: deconvolution; metabolism; pancreatic cancer; pancreatic ductal adenocarcinoma; prognosis.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This research was supported through philanthropic donations, and funding from the Terry Fox Research Institute.