DDDR-65. Dynamic and multi-omic profiling of glioblastoma to guide personalized medicine

Abstract BACKGROUND Glioblastoma (GBM) is the most prevalent and aggressive primary malignant brain tumor in adults, with a median survival of only 15 months. Despite rigorous multimodal therapy including surgery, radiation, and temozolomide (TMZ), 96% of patients experience relapse within seven to nine months post diagnosis. Currently there is no standardized treatment for recurrent GBM (rGBM) and treatment failure is driven by extensive intertumoral and intratumoral heterogeneity. While the biology of treatment-naïve primary GBM (pGBM) is well studied, the evolution of GBM under therapy-induced selective pressure is not fully understood. This study utilizes a validated preclinical model to predict the molecular trajectory of a patient’s recurrence and develop a personalized therapeutic regimen before relapse. METHODS We performed an integrated multi-omic analysis (whole-genome sequencing, single-cell RNA sequencing, and proteomics) on a patient’s matched primary and recurrent GBM samples. In parallel, we generated a therapy-adapted patient-derived xenograft (PDX) model of the patient’s treatment plan to predict tumor evolution. Upon establishing the pGBM PDX, we implemented a three-arm study: (1) control, (2) TMZ chemoradiotherapy and (3) TMZ chemoradiotherapy with ABT414 (anti-EGFR ADC as primary GBM had EGFR overexpression). RESULTS In vivo studies demonstrated significant survival benefits in treated mice compared to controls, however, mice receiving ABT-414 relapsed earlier. Omic profiling of rGBM revealed increased immunosuppressive macrophages and proteins that suppress the anti-GBM immune response compared to pGBM.Single-cell RNA sequencing identified Indoleamine 2,3-dioxygenase 1 (IDO1) as a key regulator of the immunosuppressive tumor microenvironment in rGBM. As IDO1 is implicated in mediating resistance to PD-1 immune checkpoint blockade, its inhibition in combination with PD-1 therapy may overcome immune resistance, presenting a personalized therapeutic target for this patient. CONCLUSION In summary, we established a predictive disease model, gaining insights into GBM’s evolution and identifying actionable targets in the patient’s rGBM, offering potential strategies to overcome treatment resistance.