P15.01.A Metabolic-imaging of human glioblastoma explants: a new precision-medicine model to predict treatment response early. (5th September 2022)

• Record Type: Journal Article
• Authors: Morelli, M; Lessi, F; Di Stefano, A; Santonocito, O; Gambacciani, C; Pieri, F; Aquila, F; Ferri, G; Snuderl, M; Mulholland, P; Ottaviani, D; Aretini, P; Pasqualetti, F; Franceschi, S; Mazzanti, C
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 2
• Page Start: ii83
• Page End: ii83
• Publication Date: 2022-09-05
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac174.291
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: Glioblastoma (GB) is the most severe form of brain cancer, with a 12-15 month median survival. Although cell therapies for GB are on the near horizon, surgical resection, temozolomide (TMZ) and radiotherapy (RT) remain the primary therapeutic options for GB, and no new small-molecule therapies have been introduced in recent years. This therapeutic standstill is partially because preclinical models of GB do not reflect the complexities of GB cell biology. Furthermore, the aggressive progression of GB makes it critical to identify patient-tailored therapeutic strategies early. Material and Methods: We developed a novel in-vitro 3D glioblastoma explants (GB-EXPs) model derived from patients' resected tumors maintaining cytoarchitecture seen in the tumors. We then performed metabolic-imaging by fluorescence lifetime imaging microscopy (FLIM) on live GB-EXPs to predict drug response, using TMZ as test drug. Results: The entire process was successfully completed within 1 week since surgery. A unique drug response sample stratification emerged that was well reflected at the molecular level, highlighting new targets associated with TMZ treatment and identifying a molecular signature associated with survival. Conclusion: To the best of our knowledge, this is the first time that FLIM-based metabolic imaging is used on live glioblastoma explants to test anti-neoplastic drugs. FLIM-based readouts of drug response in GB explants could accelerate precision treatment of patients with GB and the identification of new anti-GB drugs.