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Updated: Dec 13, 2025
A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells
Published on: March 28, 2021
Novel approaches for glioblastoma treatment: Focus on tumor heterogeneity, treatment resistance, and computational
Silvana Valdebenito1, Daniela D'Amico1,2, Eliseo Eugenin1
1Department of Neuroscience, Cell Biology, and Anatomy, University of Texas Medical Branch (UTMB), Galveston, Texas.
Background:
Glioblastoma (GBM) is a highly aggressive primary brain tumor. Currently, the suggested line of action is the surgical resection followed by radiotherapy and treatment with the adjuvant temozolomide, a DNA alkylating agent. However, the ability of tumor cells to deeply infiltrate the surrounding tissue makes complete resection quite impossible, and, in consequence, the probability of tumor recurrence is high, and the prognosis is not positive. GBM is highly heterogeneous and adapts to treatment in most individuals. Nevertheless, these mechanisms of adaption are unknown.
Recent Findings:
In this review, we will discuss the recent discoveries in molecular and cellular heterogeneity, mechanisms of therapeutic resistance, and new technological approaches to identify new treatments for GBM. The combination of biology and computer resources allow the use of algorithms to apply artificial intelligence and machine learning approaches to identify potential therapeutic pathways and to identify new drug candidates.
Conclusion:
These new approaches will generate a better understanding of GBM pathogenesis and will result in novel treatments to reduce or block the devastating consequences of brain cancers.
Insights
Glioblastoma (GBM) is a deadly brain cancer. New AI and machine learning approaches are helping researchers understand GBM
Area of Science:
- Neuro-oncology
- Cancer Biology
- Computational Biology
Background:
- Glioblastoma (GBM) is an aggressive primary brain tumor with poor prognosis.
- Current treatments (surgery, radiotherapy, temozolomide) are limited by tumor infiltration and heterogeneity.
- Mechanisms of GBM treatment adaptation and resistance remain largely unknown.
Purpose of the Study:
- To review recent advances in understanding GBM molecular and cellular heterogeneity.
- To explore mechanisms of therapeutic resistance in GBM.
- To discuss novel technological approaches for identifying new GBM treatments.
Main Methods:
- Review of current literature on Glioblastoma (GBM) research.
- Integration of biological data with computational resources.
- Application of artificial intelligence (AI) and machine learning (ML) algorithms.
Main Results:
- Recent discoveries highlight GBM's significant molecular and cellular heterogeneity.
- Understanding treatment resistance mechanisms is crucial for therapeutic development.
- AI and ML approaches are identifying potential therapeutic pathways and drug candidates.
Conclusions:
- New approaches promise a deeper understanding of GBM pathogenesis.
- Novel treatments are expected to emerge from these advanced methodologies.
- These advancements aim to reduce the impact of brain cancers.
