Exploring the Glioblastoma-Immune Dynamics with Mathematical Modeling and Transcriptome Sequencing

Glioblastoma (GBM) is a deadly, heterogeneous disease with poor overall survival. Adding to the complexity of the disease, the tumor-immune environment is also heterogeneous. Glioma-associated macrophages and microglia (GAMMs) can exhibit either a tumor-suppressive or tumor-permissive response, resulting in a variety of different GBM growth patterns. However, the mechanism by which GAMMs affect GBM growth remains unclear. To explore the potential dynamics of these tumor-GAMM interactions, we created four candidate mathematical models, each capturing a different biological mechanism for the conversion between GAMM phenotypes. To better understand the parameters influential on tumor growth, we performed a sensitivity analysis. Initial analyses indicate that, beyond the growth kinetics of the tumor, the initial population of tumor-suppressive GAMMs is influential on tumor velocity. This preliminary finding is somewhat surprising, as it suggests that changes to the relative abundance of immune populations over time would not significantly impact the tumor growth. In future work, we plan to utilize deconvolution techniques on RNAseq from image-localized biopsies to identify relative cellular-subtype compositions and investigate if tumor growth kinetics are dependent on current GAMM composition. By developing a better understanding of the tumor-immune interface, we can aid in identifying potential immunotherapy strategies and in assessing their effectiveness.