From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response
Fig 7
Comparison of long-term responses of heterogeneous and homogeneous in-silico tumors to an anti-proliferative drug.
The drug was applied continuously at 14d until 42d. A-D) We compare the cohort fit to all 16 metrics to the same cohort without heterogeneity. A) Growth dynamics. Top: The full cohort is shown as a shaded error plot. Bottom: The best fit from the previous figure is averaged over 10 runs and shown. B) Top: Tumor rim size (dr distance from tumor core to 1% cellular density) vs. tumor core diameter (dc average diameter of 50% density) prior to treatment. Bottom: Change in dr vs. change in dc after treatment. C) Top: Standard deviation in measured proliferation rate (σ) vs. average measured proliferation rate (p) prior to treatment. Bottom: Change in σ vs. change in p after treatment. D) Top: Standard deviation in potential σ vs. average potential p prior to treatment. Bottom: Change in potential σ vs. change in potential p after treatment. E) The spatial distribution for the recurrent heterogeneous tumor example before and after treatment shown as densities, measured phenotype combinations and potential phenotype combinations. Phenotypes are colored according to their combination of proliferation (P) and migration (M) rates according to the color key. Movies are available at jillagal.github.io/multiscaleGBM.