Plasma Metabolome Profiling Identifies Metabolic Subtypes of Pancreatic Ductal Adenocarcinoma

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Developing biomarkers for early detection and chemotherapeutic response prediction is crucial to improve the dismal prognosis of PDAC patients. However, molecular cancer signatures based on transcriptome analysis do not reflect intratumoral heterogeneity. To explore a more accurate stratification of PDAC phenotypes in an easily accessible matrix, plasma metabolome analysis using MxP® Global Profiling and MxP® Lipidomics was performed in 361 PDAC patients. We identified three metabolic PDAC subtypes associated with distinct complex lipid patterns. Subtype 1 was associated with reduced ceramide levels and a strong enrichment of triacylglycerols. Subtype 2 demonstrated increased abundance of ceramides, sphingomyelin and other complex sphingolipids, whereas subtype 3 showed decreased levels of sphingolipid metabolites in plasma. Pathway enrichment analysis revealed that sphingolipid-related pathways differ most among subtypes. Weighted correlation network analysis (WGCNA) implied PDAC subtypes differed in their metabolic programs. Interestingly, a reduced expression among related pathway genes in tumor tissue was associated with the lowest survival rate. However, our metabolic PDAC subtypes did not show any correlation to the described molecular PDAC subtypes. Our findings pave the way for further studies investigating sphingolipids metabolisms in PDAC.

Metabolomic analyses redefine the biological classification of pancreatic cancer and correlate with clinical outcomes

“…As the downstream of systems biology, metabolomics can directly reflect the real life activities in pancreatic cancer cells, and any biological event associated with different phenotypes of pancreatic cancer must be presented in the changes of the metabolites. Mahajan et al identified three metabolic PDAC subtypes associated with distinct complex lipid patterns through PDAC plasma metabolome analysis 31 . Daemen et al applied an integrative analysis on transcriptomic and genomic data of glycolytic genes in PDA to identified two glycolytic and two nonglycolytic metabolic PDA subtypes 32 .…”

Deconstructing Pancreatic Cancer Using Next Generation-Omic Technologies–From Discovery to Knowledge-Guided Platforms for Better Patient Management

“…Metabolomic profiling of 361 PDAC blood plasma samples identified three subtypes, with sphingolipid metabolism exhibiting differential enrichment between subtypes. Integrative analysis revealed that the 3 identified subtypes do not overlap with previously defined transcriptomic subtyping schemes and are not associated with clinical outcome (Mahajan et al, 2021). While identifying sphingolipid metabolites as potentially important biomarkers in PDAC, the utility of serum based metabolic profiling for patient stratification remains unclear.…”

“…Best estimates suggest that CTCs exist at a ratio of one to ten cancer cells per 10 billion normal blood cells in a mL of blood ( Miller et al, 2010 ). CTCs are therefore typically enriched using phenotypic properties, such as size or density, or based on specific cell surface markers ( Lianidou et al, 2014 ; Martini et al, 2019 ; J.; Lee et al, 2019 ). Enrichment of CTCs by flow cytometry or microfluidic systems provides sufficient material for omic analysis including WGS ( Jiang et al, 2015 ), RNA-seq ( Yu et al, 2012 ), or single-cell sequencing ( Ting et al, 2014 ).…”

Defining Blood Plasma and Serum Metabolome by GC-MS

“…Metabolomics quickly responds to public challenges: over 200 publications on COVID-19 metabolomics appeared in less than two years [214][215][216]. The spectrum of application of metabolic knowledge solo or as a part of multi-omics is broad: from microbial stains engineering [217] to precise and personalized health monitoring [218][219][220][221].…”