From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response
Welcome to the multiscaleGBM wiki!
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 heritable 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 inherited and environmental 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.
We asked several questions that we used the model to answer:
- How does serial imaging correspond to underlying single cell heterogeneity and phenotypic behavior?
- How do large scale differences in growth dynamics and smaller scale differences in phenotypic behavior correlate with responses to an anti-proliferative treatment?
- Is environmental heterogeneity alone able to produce observed single cell variation, or is inheritable, cell-intrinsic heterogeneity needed?
- Do some tumors respond better to anti-proliferative treatments, anti-migratory treatments, or anti-proliferative/anti-migratory combination treatments?
Together our results emphasize the need to understand the underlying phenotypes and tumor heterogeneity in designing therapeutic regimens. See the sidebar to access the preprint, code, and results on our interactive website ->
