RESUMEN
BACKGROUND: Glioblastoma (GBM) is the deadliest and most common brain tumor in adults, with poor survival and response to aggressive therapy. Limited access of drugs to tumor cells is one reason for such grim clinical outcomes. A driving force for therapeutic delivery is interstitial fluid flow (IFF), both within the tumor and in the surrounding brain parenchyma. However, convective and diffusive transport mechanisms are understudied. In this study, we examined the application of a novel image analysis method to measure fluid flow and diffusion in GBM patients. METHODS: Here, we applied an imaging methodology that had been previously tested and validated in vitro, in silico, and in preclinical models of disease to archival patient data from the Ivy Glioblastoma Atlas Project (GAP) dataset. The analysis required the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which is readily available in the database. The analysis results, which consisted of IFF flow velocity and diffusion coefficients, were then compared to patient outcomes such as survival. RESULTS: We characterized IFF and diffusion patterns in patients. We found strong correlations between flow rates measured within tumors and in the surrounding parenchymal space, where we hypothesized that velocities would be higher. Analyzing overall magnitudes indicated a significant correlation with both age and survival in this patient cohort. Additionally, we found that neither tumor size nor resection significantly altered the velocity magnitude. Lastly, we mapped the flow pathways in patient tumors and found a variability in the degree of directionality that we hypothesize may lead to information concerning treatment, invasive spread, and progression in future studies. CONCLUSIONS: An analysis of standard DCE-MRI in patients with GBM offers more information regarding IFF and transport within and around the tumor, shows that IFF is still detected post-resection, and indicates that velocity magnitudes correlate with patient prognosis.
RESUMEN
Cell motility is a critical aspect of several processes, such as wound healing and immunity; however, it is dysregulated in cancer. Current limitations of imaging tools make it difficult to study cell migration in vivo. To overcome this, and to identify drivers from the microenvironment that regulate cell migration, bioengineers have developed 2D (two-dimensional) and 3D (three-dimensional) tissue model systems in which to study cell motility in vitro, with the aim of mimicking elements of the environments in which cells move in vivo. However, there has been no systematic study to explicitly relate and compare cell motility measurements between these geometries or systems. Here, we provide such analysis on our own data, as well as across data in existing literature to understand whether, and which, metrics are conserved across systems. To our surprise, only one metric of cell movement on 2D surfaces significantly and positively correlates with cell migration in 3D environments (percent migrating cells), and cell invasion in 3D has a weak, negative correlation with glioblastoma invasion in vivo. Finally, to compare across complex model systems, in vivo data, and data from different labs, we suggest that groups report an effect size, a statistical tool that is most translatable across experiments and labs, when conducting experiments that affect cellular motility.
