Research Papers:

Predicting the cell death responsiveness and sensitization of glioma cells to TRAIL and temozolomide

Birgit C. Weyhenmeyer, Janis Noonan, Maximilian L. Würstle, Frank A. Lincoln, Grainne Johnston, Markus Rehm _ and Brona M. Murphy

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Oncotarget. 2016; 7:61295-61311. https://doi.org/10.18632/oncotarget.10973

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Birgit C. Weyhenmeyer1,2,*, Janis Noonan1,2,*, Maximilian L. Würstle1,2, Frank A. Lincoln1,2, Grainne Johnston1,2, Markus Rehm1,2,3,4,**, Brona M. Murphy1,2,**

1Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland

2Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland

3Institute of Cell Biology and Immunology, Faculty of Energy-, Process- and Biotechnology, University of Stuttgart, Stuttgart, Germany

4Stuttgart Research Center Systems Biology, University of Stuttgart, Stuttgart, Germany

*These authors have contributed equally to this work

**These authors share senior authorship

Correspondence to:

Markus Rehm, email: [email protected]

Brona M. Murphy, email: [email protected]

Keywords: glioblastoma, TRAIL, temozolomide, apoptosis, systems biology

Received: December 08, 2015    Accepted: July 18, 2016    Published: August 01, 2016


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.

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