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Updated: Mar 23, 2026
Translational Orthotopic Models of Glioblastoma Multiforme
Published on: February 17, 2023
A comprehensive profile of recurrent glioblastoma
B Campos1, L R Olsen2, T Urup3
1Division of Experimental Neurosurgery, Department of Neurosurgery, University of Heidelberg, Heidelberg, Germany.
Abstract:
In spite of relentless efforts to devise new treatment strategies, primary glioblastomas invariably recur as aggressive, therapy-resistant relapses and patients rapidly succumb to these tumors. Many therapeutic agents are first tested in clinical trials involving recurrent glioblastomas. Remarkably, however, fundamental knowledge on the biology of recurrent glioblastoma is just slowly emerging. Here, we review current knowledge on recurrent glioblastoma and ask whether and how therapies change intra-tumor heterogeneity, molecular traits and growth pattern of glioblastoma, and to which extent this information can be exploited for therapeutic decision-making. We conclude that the ability to characterize and predict therapy-induced changes in recurrent glioblastoma will determine, whether, one day, glioblastoma can be contained in a state of chronic disease.
Insights
Glioblastomas often return aggressively, resisting treatment. Understanding how therapies alter tumor biology is key to managing recurrent glioblastoma as a chronic condition.
Area of Science:
- Neuro-oncology
- Cancer Biology
- Translational Medicine
Background:
- Primary glioblastomas frequently relapse as aggressive, therapy-resistant tumors.
- Despite extensive research, the fundamental biology of recurrent glioblastoma remains poorly understood.
- Clinical trials for new glioblastoma treatments often focus on recurrent disease.
Purpose of the Study:
- To review current knowledge on recurrent glioblastoma biology.
- To investigate how therapeutic interventions impact glioblastoma's intra-tumor heterogeneity, molecular characteristics, and growth patterns.
- To assess the potential of exploiting therapy-induced changes for improved treatment decision-making.
Main Methods:
- Comprehensive literature review of studies on recurrent glioblastoma.
- Analysis of existing data on molecular and phenotypic changes in glioblastoma post-therapy.
- Synthesis of information regarding therapeutic resistance mechanisms.
Main Results:
- Emerging evidence suggests therapies can induce significant changes in glioblastoma's molecular profile and heterogeneity.
- Understanding these therapy-induced alterations is crucial for predicting treatment response.
- Current knowledge on the dynamic biology of recurrent glioblastoma is still limited.
Conclusions:
- Characterizing and predicting therapy-induced changes in recurrent glioblastoma is essential for future treatment strategies.
- Harnessing this knowledge may enable managing glioblastoma as a chronic, rather than terminal, disease.
- Further research into the adaptive biology of recurrent glioblastoma is urgently needed.
