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Updated: Mar 16, 2026
A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells
Published on: March 28, 2021
Predicting the cell death responsiveness and sensitization of glioma cells to TRAIL and temozolomide
Birgit C Weyhenmeyer1,2, Janis Noonan1,2, Maximilian L Würstle1,2
1Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland.
Abstract:
Genotoxic chemotherapy with temozolomide (TMZ) is a mainstay of treatment for glioblastoma (GBM); however, at best, TMZ provides only modest survival benefit to a subset of patients. Recent insight into the heterogeneous nature of GBM suggests a more personalized approach to treatment may be necessary to overcome cancer drug resistance and improve patient care. These include novel therapies that can be used both alone and with TMZ to selectively reactivate apoptosis within malignant cells. For this approach to work, reliable molecular signatures that can accurately predict treatment responsiveness need to be identified first. Here, we describe the first proof-of-principle study that merges quantitative protein-based analysis of apoptosis signaling networks with data- and knowledge-driven mathematical systems modeling to predict treatment responsiveness of GBM cell lines to various apoptosis-inducing stimuli. These include monotherapies with TMZ and TRAIL, which activate the intrinsic and extrinsic apoptosis pathways, respectively, as well as combination therapies of TMZ+TRAIL. We also successfully employed this approach to predict whether individual GBM cell lines could be sensitized to TMZ or TRAIL via the selective targeting of Bcl-2/Bcl-xL proteins with ABT-737. Our findings suggest that systems biology-based approaches could assist in personalizing treatment decisions in GBM to optimize cell death induction.
Insights
This study introduces a systems biology approach to predict glioblastoma (GBM) treatment response. By integrating protein analysis and mathematical modeling, it aims to personalize chemotherapy for better patient outcomes.
Area of Science:
- Systems biology
- Cancer research
- Molecular oncology
Background:
- Glioblastoma (GBM) treatment with temozolomide (TMZ) offers limited survival benefits due to tumor heterogeneity and drug resistance.
- Personalized treatment strategies are needed to overcome resistance and improve patient outcomes in GBM.
- Identifying molecular signatures to predict treatment response is crucial for effective therapeutic selection.
Purpose of the Study:
- To develop and validate a systems biology approach for predicting glioblastoma cell line responsiveness to apoptosis-inducing therapies.
- To integrate quantitative protein analysis of apoptosis signaling with mathematical modeling for predictive insights.
- To assess the potential of this approach in personalizing glioblastoma treatment decisions.
Main Methods:
- Quantitative protein-based analysis of apoptosis signaling networks in glioblastoma cell lines.
- Data- and knowledge-driven mathematical systems modeling to predict treatment response.
- Evaluation of monotherapies (TMZ, TRAIL) and combination therapies (TMZ+TRAIL), including sensitization with ABT-737.
Main Results:
- The study successfully demonstrated a proof-of-principle for merging quantitative proteomics and mathematical modeling to predict GBM cell line responses.
- The approach accurately predicted responses to temozolomide, TRAIL, and combination therapies.
- Predictive capabilities extended to assessing sensitization to TMZ or TRAIL by targeting Bcl-2/Bcl-xL proteins with ABT-737.
Conclusions:
- Systems biology approaches integrating molecular data and mathematical modeling can predict glioblastoma treatment responsiveness.
- This methodology holds promise for personalizing glioblastoma treatment strategies to optimize apoptosis induction.
- Further research can leverage these findings to develop more effective and tailored therapies for glioblastoma patients.
