Convergent Approaches to Delineate the Metabolic Regulation of Tumor Invasion by Hyaluronic Acid Biosynthesis

• Published in: Advanced healthcare materials Vol. 12; no. 14; p. e2202224
• Main Authors: Shimpi, Adrian A, Tan, Matthew L, Vilkhovoy, Michael, Dai, David, Roberts, LaDeidra Monet, Kuo, Joe Chin-Hun, Huang, Lingting, Varner, Jeffrey D, Paszek, Matthew, Fischbach, Claudia
• Format: Journal Article
• Language: English
• Published: Germany 01.06.2023
• Subjects: Cell Line, Tumor; Humans; Hyaluronic Acid - pharmacology; Neoplasms - pathology; Neoplastic Stem Cells - metabolism; metabolism; cancer stem-like cells; flux balance analysis; tumor invasion; hyaluronic acid
• ISSN: 2192-2659
• DOI: 10.1002/adhm.202202224

Metastasis is the leading cause of breast cancer-related deaths and is often driven by invasion and cancer-stem like cells (CSCs). Both the CSC phenotype and invasion are associated with increased hyaluronic acid (HA) production. How these independent observations are connected, and which role metabolism plays in this process, remains unclear due to the lack of convergent approaches integrating engineered model systems, computational tools, and cancer biology. Using microfluidic invasion models, metabolomics, computational flux balance analysis, and bioinformatic analysis of patient data, the functional links between the stem-like, invasive, and metabolic phenotype of breast cancer cells as a function of HA biosynthesis are investigated. These results suggest that CSCs are more invasive than non-CSCs and that broad metabolic changes caused by overproduction of HA play a role in this process. Accordingly, overexpression of hyaluronic acid synthases (HAS) 2 or 3 induces a metabolic phenotype that promotes cancer cell stemness and invasion in vitro and upregulates a transcriptomic signature predictive of increased invasion and worse patient survival. This study suggests that HA overproduction leads to metabolic adaptations to satisfy the energy demands for 3D invasion of breast CSCs highlighting the importance of engineered model systems and multidisciplinary approaches in cancer research.