EXTH-61. A MULTI-TARGETED APPROACH TO EVALUATE THERAPEUTIC SELECTION AND EFFICACY IN PRECLINICAL GBM MODELS

Abstract

Abstract Glioblastoma (GBM) is a devastating primary brain cancer with approximately 10,000 new US diagnoses annually. The current standard of care for GBM includes surgical resection followed by radiation therapy (RT) and temozolomide (TMZ), however there is near universal recurrence and development of resistance after treatment. Relapse in disease is tightly linked with dynamic changes in gene expression during tumor evolution, highlighting the need for stronger preclinical GBM models. Here we report developing a pair of patient-derived xenograft (PDX) models from surgical resections obtained from initial GBM incidence in a patient (CRT00433) and subsequent recurrence following treatment (CRT00435) in order to study disease progression and novel treatment strategies. Recent studies have suggested the utilization of combination therapy approaches for GBM patients to address TMZ resistance. To this end, Certis has developed a personalized, AI-based approach to predict and test combination therapies for CRT00433/435. Accuracy of the predicted sensitivities to treatment was then evaluated in vivo using both subcutaneously and orthotopically engrafted mouse modeling. Luciferase-tagged CRT00433 and CRT00435 spheroid cell lines were transduced with firefly luciferase and implanted intracranially by stereotactic surgery for orthotopic monitoring. Optical bioluminescence imaging (BLI) and MRI were used to assess therapeutic response of AI-predicted combination therapies and standard of care (RT and/or TMZ). Both in-life imaging and terminal histologic analysis of the tumors confirmed high fidelity to several of the therapies tested. In all, these results demonstrate a novel, combinatorial platform with which to assess therapeutic efficacy in GBM and exploit the overall ability of this deadly disease to adapt to an ever-changing molecular and genetic landscape.