JOURNAL ARTICLE

OS12.5.A SPATIALLY RESOLVED FUNCTIONAL PROFILING FOR GLIOBLASTOMA

Abstract

Abstract BACKGROUND Glioblastoma is the most common malignant primary brain tumor in adults and associated with a poor prognosis and a median survival of approximately 15 months, largely due to the lack of effective therapies and inevitable recurrence. A key challenge is the spatial and cellular heterogeneity, evident on MRI as a necrotic center, a contrast-enhancing tumor core, and a non-enhancing, T2/FLAIR-hyperintense infiltration zone. The diffuse infiltration of tumor cells into surrounding brain tissue impedes complete surgical resection and drives relapse from persisting residual cells. While regional molecular differences have been well characterized, their functional implications - particularly drug sensitivities - remain insufficiently understood. MATERIAL AND METHODS To address this, we performed MRI- and 5-aminolevulinic acid (5-ALA)-guided tissue sampling from the tumor core and from the infiltration zone of patients with glioblastoma. We utilized pharmacoscopy—a single-cell, microscopy-based drug screening platform—to evaluate region- and tumor cell type-specific drug responses. Cancer cell types were defined based on differential expression of nestin (marking stem-like cells) and S100B (marking more differentiated tumor cells). RESULTS Profiling a library of 59 drugs across region-specific tumor samples from 23 patients revealed pronounced heterogeneity in drug responses across patients, tumor regions and tumor cell states. To overcome this heterogeneity, we computationally predicted complementary drug pairs and validated 20 drug combinations in an independent cohort. The combinations demonstrated enhanced efficacy compared to single agents and effectively addressed glioblastoma’s spatial, cellular, and inter-patient heterogeneity. Notably, drug combinations of oncology and neuroactive drugs outperformed those involving agents from the same drug class (oncology + oncology or neuroactive + neuroactive). Ongoing mechanistical explorations reveal first insights into mechanisms of the complementary drug efficacy. CONCLUSION Our findings underscore the limitations of monotherapies for patients with glioblastoma and highlight the potential of rationally designed drug combinations tailored to the tumor’s spatial and functional complexity.

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Topics

Radiomics and Machine Learning in Medical Imaging
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging

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