NEIM-08 MODELING COMPETITION BETWEEN SUBPOPULATIONS WITH VARIABLE DNA CONTENT IN RESOURCE LIMITED MICROENVIRONMENTS

Resource limitations shape the outcome of competitions between heterogeneous pre-malignant cells. One example of such heterogeneity is in the ploidy (DNA content) of pre-malignant cells. High-ploidy cells need more resources, to synthesize increasing amounts of DNA, RNA and proteins. To model how subpopulations with variable DNA-content compete in the resource limited environment of the human brain we developed a stochastic state-space model of the brain (S3MB). The model discretizes the brain into voxels, whereby the state of each voxel is defined by 8+ variables that are updated over time, including stiffness, glucose and proliferating cells of various DNA content. Well established Fokker-Planck partial differential equations govern the spatial distribution of resources and cells. We applied S3MB on sequencing and imaging data obtained from a primary GBM patient. We sequenced four surgical specimens collected during the 1st and 2nd surgeries of the GBM and used HATCHET to quantify its clonal composition and how it changes between the surgeries. HATCHET identified two aneuploid subpopulations of ploidy 1.98 and 2.29 respectively. The low-ploidy clone was dominant at the time of the first surgery and became even more dominant upon recurrence. MRI images were available before and after each surgery and registered to MNI space. T1 post and T2 flair scans acquired after the 1st surgery informed tumor cell densities per voxel. Magnetic Resonance Elastography scans and PET/CT scans informed stiffness and Glucose access per voxel. We performed a parameter search to recapitulate the GBM’s tumor cell density and ploidy composition before the 2nd surgery. Results suggest that the high-ploidy subpopulation had a higher Glucose-dependent proliferation rate, but a lower Glucose-dependent death rate, resulting in spatial differences in the distribution of the two subpopulations. Our results contribute to a better understanding of how genomics and microenvironments interact to shape cell fate decisions.