RESUMEN
Hyaluronic acid (HA), the primary component of brain extracellular matrix, is increasingly used to model neuropathological processes, including glioblastoma (GBM) tumor invasion. While elastic hydrogels based on crosslinked low-molecular-weight (LMW) HA are widely exploited for this purpose and have proven valuable for discovery and screening, brain tissue is both viscoelastic and rich in high-MW (HMW) HA, and it remains unclear how these differences influence invasion. To address this question, hydrogels comprised of either HMW (1.5 MDa) or LMW (60 kDa) HA are introduced, characterized, and applied in GBM invasion studies. Unlike LMW HA hydrogels, HMW HA hydrogels relax stresses quickly, to a similar extent as brain tissue, and to a greater extent than many conventional HA-based scaffolds. GBM cells implanted within HMW HA hydrogels invade much more rapidly than in their LMW HA counterparts and exhibit distinct leader-follower dynamics. Leader cells adopt dendritic morphologies, similar to invasive GBM cells observed in vivo. Transcriptomic, pharmacologic, and imaging studies suggest that leader cells exploit hyaluronidase, an enzyme strongly enriched in human GBMs, to prime a path for followers. This study offers new insight into how HA viscoelastic properties drive invasion and argues for the use of highly stress-relaxing materials to model GBM.
RESUMEN
Metastasis, the leading cause of death in cancer patients, requires the invasion of tumor cells through the stroma in response to migratory cues, in part provided by the extracellular matrix (ECM). Recent advances in proteomics have led to the identification of hundreds of ECM proteins, which are more abundant in tumors relative to healthy tissue. Our goal was to develop a pipeline to easily predict which ECM proteins are more likely to have an effect on cancer invasion and metastasis. We evaluated the effect of four ECM proteins upregulated in breast tumor tissue in multiple human breast cancer cell lines in three assays. There was no linear relationship between cell adhesion to ECM proteins and ECM-driven 2D cell migration speed, persistence, or 3D invasion. We then used classifiers and partial-least squares regression analysis to identify which metrics best predicted ECM-driven 2D migration and 3D invasion responses. We find that ECM-driven 2D cell migration speed or persistence did not predict 3D invasion in response to the same cue. However, cell adhesion, and in particular cell elongation and shape irregularity, accurately predicted the magnitude of ECM-driven 2D migration and 3D invasion. Our models successfully predicted the effect of novel ECM proteins in a cell-line specific manner. Overall, our studies identify the cell morphological features that determine 3D invasion responses to individual ECM proteins. This platform will help provide insight into the functional role of ECM proteins abundant in tumor tissue and help prioritize strategies for targeting tumor-ECM interactions to treat metastasis.
