A methylome-based score to predict survival after regorafenib in recurrent glioblastoma
Year: 2026
Type: article
Abstract: Background Following the encouraging results of the REGOMA Phase 2 trial, regorafenib is increasingly used for the treatment of recurrent glioblastoma, IDH-wildtype. Identifying predictive factors for response to regorafenib is of paramount importance. DNA methylation profiling is considered the gold standard for diagnosing and classifying central nervous system tumors. We explored the predictive value of methylation profiling in recurrent glioblastoma patients treated with regorafenib. Methods Thirty-three recurrent glioblastoma patients were enrolled in the study. DNA methylation profile was performed on paraffin-embedded tumor obtained at first surgery. Results Median progression-free survival (PFS) and overall survival (OS) following regorafenib treatment were 3 and 7.5 months, respectively, aligning with findings from the REGOMA trial. Methylation classes RTK2 and mesenchymal were associated with an improved survival after regorafenib. Copy number variation analysis showed that imbalances in the p53-MDM2 and cyclin-Rb pathways negatively predict survival after regorafenib treatment. Multivariable analysis demonstrated that CCND2 gene gain/amplification and chromosome 13 deletion, encompassing the RB1 gene, were associated with reduced PFS, while MDM2 gain/amplification was linked to diminished OS after regorafenib. We developed methylation-based scores for predicting PFS and OS outcomes. Notably, the PFS score exhibited a 100% negative predictive value, enabling precise patients selection. Differentially methylated region analysis suggested that resistance to regorafenib may involve disruptions in WNT signaling, cadherin-mediated adhesion, and presenilin pathways. Conclusions These findings highlight the potential of DNA methylation profiling in guiding therapeutic strategies and warrant further validation in clinical settings.
Source: Neuro-Oncology Advances
Authors: Silvia Chiesa, Evelina Miele, Sara Patrizi, Lucia Pedace, Ciro Mazzarella, Martina Offi, Antonella Martino, Maria Laura Falchetti, Giuliano Di Monaco, Rosellina Russo, Flavia Vinciarelli, Angela Mastronuzzi, Simona Gaudino, Maurizio Martini, Lucia Ricci-Vitiani, Roberto Pallini, Liverana Lauretti, Maria Antonietta Gambacorta, Franco Locatelli, Alessandro Olivi, Marco Gessi, Quintino Giorgio D’Alessandris
Language: English
Subfield: Genetics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,615
OpenAlex ID: W7160168709