TMOD-13. MAXIMIZING THE POWER OF PATIENT TUMOR-DERIVED ORTHOTOPIC XENOGRAFT (PDOX) MODELS OF PEDIATRIC BRAIN TUMORS TO PREDICT DRUG RESPONSES IN HUMANS. (23rd April 2019) - Injac, Sarah; Du, Yuchen; Kogiso, Mari; Braun, Frank; Zhang, Huiyuan; Qi, Lin; Lindsay, Holly; Zhao, Sibo; Li, Xiao-nan
Abstract: Brain tumors are the leading cause of cancer-related death in children. A major challenge in the development of new therapies is the failure of many model systems to accurately predict drug responses patients. We identified two major weakness common to the majority of available models of pediatric brain tumors. First, while patients receive multi-modal therapy, pre-clinical testing typically involves the comparison of single agent therapy to placebo or other single agents. Two, while the majority of early phase clinical trials are carried out in relapsed, frequently heavily pre-treated patients; pre-clinical models almost exclusively represent untreated disease. To begin to address these issues, our lab has leveraged our large panel (>80) orthotopic xenograft mouse models of brain tumors which have undergone extensive molecular characterization. We designed treatment schedules based on original patient treatment data for 8 pediatric glioblastoma models and 9 medulloblastoma models and treated them accordingly. We are able to demonstrate the feasibility of combining radiation and multi-agent cytotoxic chemotherapy our mouse models. Furthermore, our results correlate with what has been seen in large scale human clinical trials with glioblastoma and DIPG models showing little benefit from standard treatments while medulloblastoma models show a significant increase in survival time. Going forward, these survival data will provide a more appropriate basis for the comparison of novel compounds allowing for increased efficiency in translating promising new treatments. Furthermore by harvesting the tumors which progressed after being exposed to patient based therapies, we have generated a resource that will allow for the establishment of more accurate models of brain tumor relapse in the future.