Abstract
In Glioblastoma (GBM), tumor spreading is driven by tumor cells’ ability to infiltrate healthy brain parenchyma, which prevents complete surgical resection and contributes to tumor recurrence. GBM molecular subtypes, classical, proneural and mesenchymal, were shown to strongly correlate with specific genetic alterations (Mesenchymal: NF1; Classical: EGFRVIII; Proneural: PDGFRA). Here we tested the hypothesis that a key mechanistic difference between GBM molecular subtypes is that proneural cells are slow migrating and mesenchymal cells are fast migrating. Using Sleeping Beauty transposon system, immune-competent murine brain tumors were induced by SV40-LgT antigen in combination with either NRASG12V (NRAS) or PDGFB (PDGF) overexpression. Cross-species transcriptomic analysis revealed NRAS and PDGF-driven tumors correlate with human mesenchymal and proneural GBM, respectively. Similar to human GBM, CD44 expression was higher in NRAS tumors and, consistent with migration simulations of varying CD44 levels, ex vivo brain slice live imaging showed NRAS tumors cells migrate faster than PDGF tumors cells (random motility coefficient = 30µm2/hr vs. 2.5µm2/hr, p < 0.001). Consistent with CD44 function as an adhesion molecule, migration phenotype was independent of the tumor microenvironment. NRAS and human PDX/MES tumor cells were found to migrate faster and have larger cell spread area than PDGF and human PDX/PN tumors cells, respectively, in healthy mouse brain slices. Furthermore, traction force microscopy revealed NRAS tumor cells generate larger traction forces than PDGF tumors cells which further supports our theoretical mechanism driving glioma migration. Despite increased migration, NRAS cohort had better survival than PDGF which was attributed to enhanced antitumoral immune response in NRAS tumors, consistent with increased immune cell infiltration found in human mesenchymal GBM. Overall our work identified a potentially actionable difference in migration mechanics between GBM subtypes and establishes an integrated biophysical modeling and experimental approach to mechanically parameterize and simulate distinct molecular subtypes in preclinical models of cancer.
Generative Biophysical Modeling of Dynamical Networks in the Olfactory System
