Peer Reviewed

1

Document Type

Article

Publication Date

1-8-2016

Keywords

TRAIL, apoptosis, glioblastoma, systems biology, temozolomide.

Funder/Sponsor

Irish Health Research Board. RCSI Research Committee. European Union.

Comments

The original article is available at www.impactjournals.com

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.

Disciplines

Physics | Physiology

Citation

Weyhenmeyer BC, Noonan J, Würstle ML, Lincoln FA, Johnston G, Rehm M, Murphy BM. Predicting the cell death responsiveness and sensitization of glioma cells to TRAIL and temozolomide. Oncotarget. 2016;7(38):61295-61311

PubMed ID

27494880

DOI Link

10.18632/oncotarget.10973.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

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