Metabolic-imaging of human glioblastoma explants: a new precision-medicine model to predict tumor treatment response early
Mariangela MorelliiD, Francesca LessiiD, Serena BarachiniiD, Romano LiottiiD, Nicola MontemurroiD, Paolo PerriniiD, Orazio Santo SantonocitoiD, Carlo GambaccianiiD, Matija SnuderliD, Francesco PieriiD, Filippo Aquila, Azzurra Farnesi, Antonio Giuseppe NaccaratoiD, Paolo Viacava, Francesco CardarelliiD, Gianmarco FerriiD, Paul James MulhollandiD, Diego OttavianiiD, Fabiola PaiariD, Gaetano Liberti, Francesco PasqualettiiD, Michele Menicagli, Paolo AretiniiD, Giovanni SignoreiD, Sara FranceschiiD, Chiara Maria MazzantiiD
Abstract
BackgroundGlioblastoma (GB) is the most severe form of brain cancer, with a 12-15 month median survival. Surgical resection, temozolomide (TMZ) treatment, and radiotherapy (RT) remain the primary therapeutic options for GB, and no new therapies have been introduced in recent years. This therapeutic standstill is primarily due to preclinical approaches that do not fully respect the complexity of GB cell biology and fail to test efficiently anti-cancer treatments. Therefore, better treatment screening approaches are needed. In this study, we have developed a novel functional precision medicine approach to test the response to anticancer treatments in organoids derived from the resected tumors of glioblastoma patients. MethodsGB organoids were grown for a short period of time to prevent any genetic and morphological evolution and divergence from the tumor of origin. We chose metabolic imaging by NAD(P)H fluorescence lifetime imaging microscopy (FLIM) to predict early and non-invasively ex-vivo anti-cancer treatment responses of GB organoids. TMZ was used as the benchmark drug to validate the approach. Whole-transcriptome and whole-exome analyses were then performed to characterize tumor cases stratification. ResultsOur functional precision medicine approach was completed within one week after surgery and two groups of TMZ Responder and Non Responder tumors were identified. FLIM-based metabolic tumor stratification was well-reflected at the molecular level, confirming the validity of our approach, highlighting also new target genes associated with TMZ treatment and identifying a new 17 gene molecular signature associated with survival. The number of promoter methylated tumors for the MGMT gene was higher in the responsive group, as expected, however, some non-methylated tumor cases turned out to be nevertheless responsive to TMZ, suggesting that our procedure could be synergistic with the classical MGMT methylation biomarker. ConclusionsFor the first time, FLIM-based metabolic imaging was used on ex-vivo live glioblastoma organoids. Unlike other approaches, ex-vivo patient-tailored drug response is performed at an early stage of tumor culturing with no animal involvement and with minimal tampering with the original tumor cytoarchitecture. This functional precision medicine approach can be exploited in a range of clinical and laboratory settings to improve the clinical management of GB patients and implemented on other cancers as well.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 39
- FWCI
- 4.66
- Field percentile
- top 4%
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- BIRC3: A Prognostic Predictor and Novel Therapeutic Target in TMZ-Resistant Glioblastoma Tumors
M. Morelli, S. Franceschi, … C. M. Mazzanti·bioRxiv·Aug 23, 2023
- High-Resolution Tomographic Analysis of in vitro 3D Glioblastoma Tumor Model under Long-Term Drug Treatment
M. S. Ozturk, V. K. Lee, … X. Intes·bioRxiv·Jul 11, 2019
- Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma
S. C. Massey, H. White, … K. R. Swanson·bioRxiv·Jan 2, 2019
- Functional drug susceptibility testing based on biophysical measurements predicts patient outcome in glioblastoma patient-derived neurosphere models
M. A. Stockslager, S. Malinowski, … S. R. Manalis·bioRxiv·Aug 5, 2020
- Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma
D. Panovska, P. Nazari, … F. De Smet·bioRxiv·Oct 17, 2022
- Co-Hyperpolarized Dehydroascorbate and Pyruvate MRI Predicts Treatment Response in Glioblastoma
Elizabeth Coffee, Paola Porcari, … Kayvan R. Keshari·bioRxiv·Nov 11, 2025