Personalized Therapeutic Strategies in Glioblastoma: Bridging Molecular Markers and In Vivo Observations for Targeted Treatment
TL;DR
This review looks at how important molecular drivers, like metabolic changes caused by IDH1/2 mutations, MGMT promoter methylation status, receptor tyrosine kinase signaling involving EGFR and EGFRvIII, and changes in epigenetics seen in H3 K27M, are creating new opportunities in immunotherapy, CAR-T cell therapy, precision radiation, and nanomedicine.
Abstract
Glioblastoma (GBM) is still the most aggressive primary brain tumor in adults. It has a declining median survival rate and shows strong resistance to treatment. This resistance comes from significant differences within the tumor itself. The past failures of standard treatments have led to a shift in neuro-oncology. We now focus on molecular stratification based on genetic changes that can be targeted, instead of relying solely on histopathological classifications. This review looks at how important molecular drivers, like metabolic changes caused by IDH1/2 mutations, MGMT promoter methylation status, receptor tyrosine kinase signaling involving EGFR and EGFRvIII, and changes in epigenetics seen in H3 K27M, are creating new opportunities in immunotherapy, CAR-T cell therapy, precision radiation, and nanomedicine. We also discuss how preclinical validation through in vivo functional models, including patient-derived xenografts (PDXs) and syngeneic mouse lines, offers key insights into tumor evolution under treatment pressure, how drugs penetrate the blood–brain barrier (BBB), and the immunosuppressive nature of the tumor microenvironment (TME). However, despite the scientific backing for these therapies, recent Phase III clinical trial failures highlight a significant gap in translating these findings into effective treatments. This gap mainly results from random Darwinian clonal replacement and antigen escape. To address this issue, we explore the potential of liquid biopsies, such as cerebrospinal fluid and plasma, for real-time molecular tracking. These can help differentiate between true progression and pseudoprogression. Ultimately, we believe that closing the translational gap requires moving away from static clinical methods and adopting flexible, long-term trial designs. We also need to integrate multi-omic data with artificial intelligence (AI) to predict cellular changes and counteract resistance mechanisms before they lead to clinical recurrence.