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.
Highlights
Glioblastoma (GBM), the most common form of primary brain tumor in humans, is typically aggressive, highly infiltrative, and resistant to conventional therapy
We previously demonstrated that a knowledge- and data-driven mathematical modeling approach is capable of generating reliable predictions of melanoma cell line responsiveness to both Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)- and genotoxic druginduced cell death, outperforming classical statistical procedures [37]
TRAIL binds to death receptors, DR4 and DR5, on the surface of the cell, resulting in the recruitment of the adaptor protein, Fas-associated death domain (FADD), through death effector domain (DED) interactions
Summary
Glioblastoma (GBM), the most common form of primary brain tumor in humans, is typically aggressive, highly infiltrative, and resistant to conventional therapy. Despite improvements in surgical technique and the addition of temozolomide (TMZ) to the armamentarium, patient median survival remains dismal at 14.6 months, with most experiencing tumor relapse within 7 months of treatment onset [1] and a large proportion gaining no survival advantage to TMZ therapy at all [2, 3]. When successful, this oral alkylating drug induces glioma cell death by causing DNA double strand breaks that eventually lead to growth arrest and activation of cellular apoptosis [4]. MGMT testing has become commonplace for patient selection within clinical trials [6] [10,11,12,13] and is frequently requested as a prognostic biomarker during patient clinical workup [14]
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