Developement and Application of Constraint-Based Modeling Methods to Study Vulnerabilities Associated to Lipid Metabolism in Prostate Cancer

It is increasingly clear that significant alterations in the lipid profile of cancer cells accompany tumor progression and metastasis. These changes are induced by a metabolic reprogramming that enhances the malignant phenotype of cancer cells. In this context, genome-scale metabolic models (GEM) have emerged as a valuable platform to integrate different omic data to study cancer metabolism from a holistic perspective.

Here we present a constraint-based model-driven approach integrating transcriptomic data to study the metabolic profile of two clonal sub-populations from a prostate cancer cell line (PC-3): PC-3/M and PC-3/S, representing pre and post Epithelial-Mesenchimal-Transition stages. This model-driven analysis and experimental validations unveiled a marked metabolic reprogramming in long-chain fatty acids metabolism.

While PC-3/M cells showed an enhanced entry of long-chain fatty acids into the mitochondria, PC-3/S cells used this pool as precursors of eicosanoid metabolism. This metabolic reprogramming endows PC-3/M cells with augmented energy metabolism for fast proliferation and PC-3/S cells with increased eicosanoid production impacting angiogenesis, cell adhesion and invasion.

These findings highlight the relevance of lipid metabolism in cancer development and progression. However, lipid-associated pathways are poorly annotated in human GEMs which limits the use of this tool. To overcome this limitation, we developed an algorithm-based metabolic network building method to enrich existing GEMs with lipid-associated pathways. This algorithm was applied in the study of the metabolic profile of another prostate cancer cell line (DU145) in response to a chronic exposure to different endocrine disruptors inducing malignancy and alterations in the lipid profile. The resulting lipid-enriched GEM covered 98% of the altered lipids compared to only 5% in the original model. Thus, this approach improves lipidomic data integration into GEM reconstructions, enabling a more in-depth study of the mechanisms underlying diseases with a strong metabolic component such as cancer.