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Review
. 2022 Jan 25;14(3):600.
doi: 10.3390/cancers14030600.

Advancements, Challenges, and Future Directions in Tackling Glioblastoma Resistance to Small Kinase Inhibitors

Federica Fabro  1 , Martine L M Lamfers  1 , Sieger Leenstra  1
Affiliations
Review

Advancements, Challenges, and Future Directions in Tackling Glioblastoma Resistance to Small Kinase Inhibitors

Federica Fabro et al. Cancers (Basel). .

Abstract

Despite clinical intervention, glioblastoma (GBM) remains the deadliest brain tumor in adults. Its incurability is partly related to the establishment of drug resistance, both to standard and novel treatments. In fact, even though small kinase inhibitors have changed the standard clinical practice for several solid cancers, in GBM, they did not fulfill this promise. Drug resistance is thought to arise from the heterogeneity of GBM, which leads the development of several different mechanisms. A better understanding of the evolution and characteristics of drug resistance is of utmost importance to improve the current clinical practice. Therefore, the development of clinically relevant preclinical in vitro models which allow careful dissection of these processes is crucial to gain insights that can be translated to improved therapeutic approaches. In this review, we first discuss the heterogeneity of GBM, which is reflected in the development of several resistance mechanisms. In particular, we address the potential role of drug resistance mechanisms in the failure of small kinase inhibitors in clinical trials. Finally, we discuss strategies to overcome therapy resistance, particularly focusing on the importance of developing in vitro models, and the possible approaches that could be applied to the clinic to manage drug resistance.

Keywords: cell culture models; drug resistance; glioblastoma; overcoming resistance; small kinase inhibitors.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Summary of temozolomide resistance mechanisms in GBM. The main mechanisms can be grouped as follows: DNA repair, drug efflux, autophagy, molecular pathways, epigenetics, metabolism, glioma stem cells (GSC), and tumor microenvironment (TME). O6meG: O6-methylguanine, N7meG: N7-methylguanine, N3meG: N3-methylguanine, MGMT: O6-methylguanine-DNA methyltransferase, MMR: mismatch repair, BER: base excision repair, ABC transporter: ATP-binding cassette transporter, RTK: tyrosine kinase receptor, TGF-β: tumor growth factor β, Wnt: wingless-related integration site, JAK: Janus kinase, STAT: signal transducer and activator of transcription; PI3K: phosphoinositide-3-kinase, Akt: protein kinase B (PKB), Ras: rat sarcoma virus, MAPK: mitogen-activated protein kinases, NF-κB: nuclear factor kappa-light-chain-enhancer of activated B cells, miR: microRNA, ROS: reactive oxygen species, GSH: glutathione, CD133: cluster of differentiation 133, CD44: cluster of differentiation 44, SOX2: (sex-determining region Y)-box 2, VEGF: vascular endothelial growth factor, TAM: tumor-associated macrophages, ECM: extracellular matrix, TMZ: temozolomide.
Figure 2
Figure 2
Summary of small kinase inhibitor resistance mechanisms. The mechanisms can be grouped as follows: mutations on the extracellular domain of RTKs, coactivation and transactivation of RTKs, adaption, and alternative routes. SKI: small kinase inhibitor, EGFR: epidermal growth factor receptor, PDGFR: platelet-derived growth factor receptor, c-KIT: stem cell factor receptor, c-MET: mesenchymal epithelial transition factor, RTK: tyrosine kinase receptor, PTEN: phosphatase and tensin homolog, FGFR: fibroblast growth factor receptor, IGF-1R: insulin-like growth factor 1 receptor, NF-κB: nuclear factor kappa-light-chain-enhancer of activated B cells, ERK: extracellular signal-regulated kinase.
Figure 3
Figure 3
Three strategies to approach drug resistance in GBM. (A) Detection of the tumor at early stages would allow a more effective eradication of the tumor and prevention of drug resistance. (B) Deepening the drug response by optimizing the chemosensitivity prediction would bring about a more effective and safe therapeutic effect. (C) Constant monitoring of the therapy would allow the early detection of resistance which can subsequently be tackled by a second therapy intervention.
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