Agent-Based Modelling Reveals the Role of the Tumor Microenvironment on the Short-Term Success of Combination...

Glioblastoma is the most common and deadly primary brain tumor in adults. All glioblastoma patients receiving standard-of-care surgery-radiotherapy-chemotherapy (i.e., temozolomide (TMZ)) recur, with an average survival time of only 15 months. New approaches to the treatment of glioblastoma, including immune checkpoint blockade and oncolytic viruses, offer the possibility of improving glioblastoma outcomes and have as such been under intense study. Unfortunately, these treatment modalities have thus far failed to achieve approval. Recently, in an attempt to bolster efficacy and improve patient outcomes, regimens combining chemotherapy and immune checkpoint inhibitors have been tested in trials. Unfortunately, these efforts have not resulted in significant increases to patient survival. To better understand the various factors impacting treatment outcomes of combined TMZ and immune checkpoint blockade, we developed a systems-level, computational model that describes the interplay between glioblastoma, immune, and stromal cells with this combination treatment. Initializing our model to spatial resection patient samples labeled using imaging mass cytometry, our model's predictions show how the localization of glioblastoma cells, influence therapeutic success. We further validated these predictions in samples of brain metastases from patients given they generally respond better to checkpoint blockade compared with primary glioblastoma. Ultimately, our model provides novel insights into the mechanisms of therapeutic success of immune checkpoint inhibitors in brain tumors and delineates strategies to translate combination immunotherapy regimens more effectively into the clinic. SIGNIFICANCE STATEMENT: Extending survival times for glioblastoma patients remains a critical challenge. Although immunotherapies in combination with chemotherapy hold promise, clinical trials have not shown much success. Here, systems models calibrated to and validated against patient samples can improve preclinical and clinical studies by shedding light on the factors distinguishing responses/failures. By initializing our model with imaging mass cytometry visualization of patient samples, we elucidate how factors such as localization of glioblastoma cells and CD8+ T cell infiltration impact treatment outcomes.