From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response

Glioblastomas are aggressive primary brain tumors known for their inter- and intratumor heterogeneity. This disease is uniformly fatal, with intratumor heterogeneity the major reason for treatment failure and recurrence. Just like the nature vs nurture debate, heterogeneity can arise from intrinsic or environmental influences. Whilst it is impossible to clinically separate observed behavior of cells from their environmental context, using a mathematical framework combined with multiscale data gives us insight into the relative roles of variation from different sources. To better understand the implications of intratumor heterogeneity on therapeutic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor kinetics and the individual cellular behavior. We track single cells as agents, cell density on a coarser scale, and growth factor diffusion and dynamics on a finer scale over time and space. Our model parameters were fit utilizing serial MRI imaging and cell tracking data from ex vivo tissue slices acquired from a growth-factor driven glioblastoma murine model. When fitting our model to serial imaging only, there was a spectrum of equally-good parameter fits corresponding to a wide range of phenotypic behaviors. When fitting our model using imaging and cell scale data, we determined that environmental heterogeneity alone is insufficient to match the single cell data, and intrinsic heterogeneity is required to fully capture the migration behavior. The wide spectrum of in silico tumors also had a wide variety of responses to an application of an anti-proliferative treatment. Recurrent tumors were generally less proliferative than pre-treatment tumors as measured via the model simulations and validated from human GBM patient histology. Further, we found that all tumors continued to grow with an anti-migratory treatment alone, but the anti-proliferative/anti-migratory combination generally showed improvement over an anti-proliferative treatment alone. Together our results emphasize the need to better understand the underlying phenotypes and tumor heterogeneity present in a tumor when designing therapeutic regimens.Author summary: Glioblastoma, the most common primary brain tumor, is an aggressive and difficult to treat cancer. A key reason is that the tumors can be very heterogeneous, consisting of many different mutants driving distinct cell behaviors. We believe that treatments for this disease could be significantly improved by understanding and quantifying the functional impact of heterogeneity within the tumor. From a clinical standpoint, the larger tissue-scale dynamics, like growth rate, can be informed from serial MRI imaging, while the cell-scale heterogeneity, can be informed by analysis of biopsies. In this work, we combined information from both scales using a mathematical framework and multiscale data from an animal model of glioblastoma. We found that a wide range of potential tumor compositions matched imaging data alone, as a result the model predicts a wide variety of responses to treatment. Using both imaging and cell-scale data narrowed the range of possible tumor compositions and better predicted responses to treatment. The mathematical model also predicted that while targeting migration alone did not slow tumor growth (in fact it drove a more proliferative tumor), an anti-proliferative/anti-migratory treatment combination improved treatment response.