EPCO-36. INTEGRATIVE DEEP-LEARNING AND PHARMACOGENOMIC PROFILING OF MALIGNANT GLIOMAS USING PATIENT-DERIVED TUMOR CELLS

Abstract Malignant glioma stands as a formidable cancer with limited therapeutic options due to many factors including pronounced genetic heterogeneity, the blood-brain barrier, among others. Precision medicine, tailoring treatment based on a patient’s unique molecular profiles, is a fitting approach for addressing intra- and inter-tumor heterogeneity. Previously, we have shown the advantage of using patient-derived tumor cells (PDCs) to evaluate drug efficacy across various cancer types. Unlike cancer cell lines, which significantly differ from primary tumors, PDCs capture the molecular features of primary tumors, showing predictive clinical responses in a retrospective study. Building on these encouraging results, we have expanded PDC-based drug screening to specifically target malignant gliomas. We have established an initial cohort of over 500 malignant glioma PDCs (~ 370 patients) and screened more than 100 drugs. We have complemented our cohort with molecular profiles, including genomic and transcriptomic data to infer the potential pharmacogenomic associations of malignant gliomas. We constructed a multi-modal deep-learning model that seamlessly integrates molecular features and drug structure to predict the drug sensitivity. By employing an explainable machine learning method, we retrospectively analyzed the model to investigate the potential molecular biomarkers and their contribution to drug sensitivity. Collectively, our study demonstrates the efficacy of using patient-derived tumor cells (PDCs) combined with deep-learning models to predict drug sensitivity in malignant gliomas. This integrative approach can potentially refine personalized treatment strategies and improve clinical outcomes for glioma patients.