Abstract 6905: A multi-targeted approach to evaluate therapeutic selection and efficacy in preclinical GBM models

Abstract Glioblastoma (GBM) is a devastating primary brain cancer with approximately 10, 000 new US diagnoses annually. The current standard of care (SOC) 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 (BarneyOI™ Cancer Model CRT00433) and subsequent recurrence following treatment (BarneyOI Cancer Model CRT00435) 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 in preclinical cancer models. Accuracy of the predicted sensitivities to treatment was evaluated in vivo using both subcutaneous (SC) and orthotopic (OT) mouse models. Luciferase-tagged CRT00433 and CRT00435 spheroid cell lines were transduced with firefly luciferase and implanted intracranially by stereotactic surgery for OT monitoring. Optical bioluminescence imaging (BLI) and murine-scale MRI from Aspect Imaging were used to assess therapeutic response of AI-predicted combination therapies and SOC treatment. Citation Format: Emily Eastwood, Bianca Carapia, Javier Rodriguez, Kristen Buck, Derrick Gorospe, Elizabeth Valencia, Bridget Corcoran, Raffaella Pippa, Yuan-Hung Chien, Warren Andrews, Long Do, Jonathan K. Nakashima, Jantzen Sperry. A multi-targeted approach to evaluate therapeutic selection and efficacy in preclinical GBM models abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 6905.