A metabolomics investigation of non-genotoxic carcinogenicity in the rat

Area of Science:

• Toxicology
• Metabolomics
• Carcinogenesis

Background:

• Non-genotoxic carcinogens (NGCs) promote cancer via gene expression changes, not DNA mutation, evading standard mutagenesis assays.
• Current identification relies on lengthy, costly, and debated rat/mouse long-term bioassays.
• Biomarkers for carcinogenic potential exist, but definitive NGC identification remains challenging.

Purpose of the Study:

• To evaluate metabolomics and lipidomics for profiling perturbations caused by known rat non-genotoxic hepatocarcinogens (NGCs) and non-hepatocarcinogens (non-NGCs).
• To assess the potential of metabolic profiling for early screening of non-genotoxic carcinogenic potential.
• To differentiate NGCs from non-NGCs using metabolic signatures.

Main Methods:

• Ten compounds (NGCs, non-NGCs, and a genotoxic hepatocarcinogen) were administered to male Fisher 344 rats at maximum tolerated doses for 7, 28, and 91 days.
• Liver metabolite concentrations were profiled using metabolomics and lipidomics, alongside pathology and clinical chemistry.
• Leave-one-out cross-validation was used to measure predictive ability for differentiating NGCs from non-NGCs.

Main Results:

• Liver metabolite changes effectively differentiated treated groups across time points.
• Peroxisome proliferator activated receptor alpha (PPAR-α) agonists significantly influenced metabolic profiles.
• Metabolic profiling achieved 87% and 77% predictive accuracy for NGCs and non-NGCs, respectively, after 28 days.
• Discriminatory metabolites included free fatty acids, phospholipids, triacylglycerols, eicosanoid precursors, and reactive oxygen species products.

Conclusions:

• Metabolic profiling can identify xenobiotic pharmacological modes of action and detect inflammation, proliferation, and oxidative stress.
• This approach shows promise for early screening of non-genotoxic carcinogenic potential, offering an alternative to traditional bioassays.
• Specific metabolic signatures, including lipid profiles, can effectively distinguish between different classes of carcinogens.