- Point info par Montse et Denis sur la sécurité des données.
- Aurélie Soubéran (Presentation in French, slides in english)
Brain tumoroids: revolutionizing treatment prediction and drug development for brain tumors with fast, reproducible and easy-to-use personalized models.
Abstract
Background: generation of patient avatar for preclinical therapeutic development is critically needed in neuro-oncology. Our objective was to develop a fast, reproducible and easy-to-use method of tumoroid generation and analysis, available for primary and metastatic brain tumors.
Methods: tumoroids were generated from 84 patients with primary or metastatic brain tumors. Tumoroids were compared with the in situ patient tumors for morphological, cellular, molecular and therapeutic sensitivity characteristics. Predictive factor of tumoroid generation were analyzed.
Results: tumoroids were generated from 74 gliomas, 7 brain metastases and 3 rare primary tumors. Median time of tumoroid generation was 5 days. All tumoroids had histological tumor features including tumor cells, microvascular proliferation and necrotic areas. Molecular and histological profiles of tumoroids, including methylation profiling, were similar to those of in situ patient tumors. Median generation time was 5 days and success rate was 65 %. It was higher for high grade glioma and brain metastases versus IDH mutated low grade gliomas. Neither other clinical, neuro-imaging, histological nor molecular factors were predictive of tumoroid generation success for high grade gliomas. By using MACSimaTM technology we determine the cellular spatial composition of tumoroids and stability overtime of these subsets was validated by flow cytometry. We showed that tumoroid treatment responses were correlated to those of patient. Finally we validated a dedicated 3D analysis workflow for preclinical therapeutic development.
Conclusion: patient-derived tumoroids offer a robust, user-friendly, and reproducible preclinical model valuable for therapeutic development in neuro-oncology, allowing their integration into forthcoming early-phase clinical trials.
