Metabolite profiling stratifies pancreatic ductal adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors

TLDR

Targeting cancer metabolism holds promise, but clinical success hinges on accurately identifying tumor subtypes with distinct metabolic requirements. Pharmacogenomic screening of ~200 non‑PDAC cell lines confirmed that mesenchymal status predicts metabolic drug response across cancers. Metabolite profiling identified three PDAC subtypes—glycolytic, lipogenic, and low‑proliferation—whose distinct metabolic signatures predict differential sensitivity to glycolysis, glutamine, lipid, and redox inhibitors and correlate with epithelial or mesenchymal phenotypes.

Abstract

Although targeting cancer metabolism is a promising therapeutic strategy, clinical success will depend on an accurate diagnostic identification of tumor subtypes with specific metabolic requirements. Through broad metabolite profiling, we successfully identified three highly distinct metabolic subtypes in pancreatic ductal adenocarcinoma (PDAC). One subtype was defined by reduced proliferative capacity, whereas the other two subtypes (glycolytic and lipogenic) showed distinct metabolite levels associated with glycolysis, lipogenesis, and redox pathways, confirmed at the transcriptional level. The glycolytic and lipogenic subtypes showed striking differences in glucose and glutamine utilization, as well as mitochondrial function, and corresponded to differences in cell sensitivity to inhibitors of glycolysis, glutamine metabolism, lipid synthesis, and redox balance. In PDAC clinical samples, the lipogenic subtype associated with the epithelial (classical) subtype, whereas the glycolytic subtype strongly associated with the mesenchymal (QM-PDA) subtype, suggesting functional relevance in disease progression. Pharmacogenomic screening of an additional ∼ 200 non-PDAC cell lines validated the association between mesenchymal status and metabolic drug response in other tumor indications. Our findings highlight the utility of broad metabolite profiling to predict sensitivity of tumors to a variety of metabolic inhibitors.