A Precision Oncology Framework for Adult Diffuese Giomas Using patient-Derived Organoids and Translational Drug Screening

Share

Abstract

Gliomas are the most common primary brain tumors in adults. This group of tumors includes a spectrum ranging from IDH-mutant gliomas, generally associated with lower histological grades and a more favorable clinical course, to glioblastoma (GBM), the most common and aggressive subtype. However, both are characterized by marked inter- and intratumoral heterogeneity, as well as a high capacity for infiltration, progression, and recurrence, which limits therapeutic efficacy. In GBM, the standard of care treatment, consisting of maximal surgical resection followed by radiotherapy and concomitant chemotherapy with temozolomide (TMZ), provides a median survival of approximately 15 months, and recent strategies such as immunotherapy have not achieved clinically meaningful improvements. In this context, there is a need for preclinical platforms that faithfully reproduce the individual characteristics of each tumor and enable the identification of new therapeutic alternatives. Patient-derived organoids represent a promising tool for therapeutic screening, as they preserve key features of the original tumor and may facilitate treatment selection. The aim of this thesis was to establish and validate patient-derived glioma organoids (GOs) as a translational platform and to apply a transcriptomic screening approach to prioritize therapeutic candidates. In this work, GOs were established from 61 patients with GBM (GOwts) and IDH-mutant gliomas (GOmuts). After optimizing culture conditions, their histological features and molecular patterns were analyzed, confirming their preservation relative to the parental tumors and validating the use of GOs as tumor avatars. Once model fidelity had been demonstrated in both groups, the study focused on GBM, as it is the most common subtype with the worst prognosis. In this context, an in silico screen based on transcriptomic data was performed using the DiSCoVER platform to identify candidate drugs, and the results were validated in vitro in GOs and other classical glioma models, as well as in vivo in xenograft models derived from GOs and glioma cell lines. Overall, these results support the use of GOs as a faithful and useful preclinical platform for the discovery of new therapies in gliomas. In addition, the integration of 3D models with bioinformatic strategies emerges as a promising approach to address therapeutic resistance. As a direct outcome of this study, alectinib and ruxolitinib are proposed as candidates for future preclinical and potentially clinical studies in gliomas.