Developing mathematical model driven optimized recurrent glioblastoma therapies

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Abstract High-grade gliomas, including GBM, are the most common primary brain tumors in adults. GBM treatment is not curative, and recurrent high-grade glioma (rHGG) remains fatal, despite aggressive therapy. Part of the challenge in treating glioma is its localization within the naturally immunosuppressive central nervous system. Hypofractionated stereotactic radiotherapy (HFSRT) combined with immunotherapy has shown promising antitumor activity in both preclinical and clinical studies in rHGG. Radiation induces an immunogenic cancer cell death and promotes the presentation of tumor-derived antigens to antitumor T cells, and acts synergistically with immunotherapy to enhance the immune response against tumor cells. Treatment response depends on a myriad of factors, including patient, tumor, and treatment parameters. Thus, how to best combine radiation with chemotherapy or immunotherapeutics remains unknown. Current protocols of combining radiation with different therapies are applied without considering evolutionary dynamics, and every patient's tumor develops resistance and eventually progresses. We hypothesize that evolutionary principle- guided therapies must be explored to pro-actively counteract the development of resistance. Mathematical modeling may provide the necessary tools to decipher the complex evolutionary dynamics during rHGG therapy. Trained and tested mathematical and computational algorithms can simulate a variety of treatment protocols in all possible combinations. Our innovative approach and goals are to integrate mathematical modeling to learn from past clinical studies to design a prospective clinical trial in rHGG. Using mathematical and computational algorithms to exhaustively explore different treatment protocols holds the key to improved, clinically-testable protocols, and ultimately improved rHGG outcomes. This interdisciplinary team science approach combines our expertise in neuro-oncology and radiation oncology with mathematical oncology and statistics. Moffitt Cancer Center has a rich culture of interdisciplinary research across conventional department barriers, as evidenced by a strong history of translating mathematical and computational concepts into experimental biology as well as clinical trial and practice. Here we build on robust preliminary data to harness our expertise and explore evolutionary principles-guided therapies for the first time in rHGG.