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An in vivo model of glioblastoma radiation resistance identifies long noncoding RNAs and targetable kinases
Christian T. Stackhouse, … , G. Yancey Gillespie, Christopher D. Willey
Christian T. Stackhouse, … , G. Yancey Gillespie, Christopher D. Willey
Published July 19, 2022
Citation Information: JCI Insight. 2022;7(16):e148717. https://doi.org/10.1172/jci.insight.148717.
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Research Article Oncology

An in vivo model of glioblastoma radiation resistance identifies long noncoding RNAs and targetable kinases

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Abstract

Key molecular regulators of acquired radiation resistance in recurrent glioblastoma (GBM) are largely unknown, with a dearth of accurate preclinical models. To address this, we generated 8 GBM patient-derived xenograft (PDX) models of acquired radiation therapy–selected (RTS) resistance compared with same-patient, treatment-naive (radiation-sensitive, unselected; RTU) PDXs. These likely unique models mimic the longitudinal evolution of patient recurrent tumors following serial radiation therapy. Indeed, while whole-exome sequencing showed retention of major genomic alterations in the RTS lines, we did detect a chromosome 12q14 amplification that was associated with clinical GBM recurrence in 2 RTS models. A potentially novel bioinformatics pipeline was applied to analyze phenotypic, transcriptomic, and kinomic alterations, which identified long noncoding RNAs (lncRNAs) and targetable, PDX-specific kinases. We observed differential transcriptional enrichment of DNA damage repair pathways in our RTS models, which correlated with several lncRNAs. Global kinomic profiling separated RTU and RTS models, but pairwise analyses indicated that there are multiple molecular routes to acquired radiation resistance. RTS model–specific kinases were identified and targeted with clinically relevant small molecule inhibitors. This cohort of in vivo RTS patient-derived models will enable future preclinical therapeutic testing to help overcome the treatment resistance seen in patients with GBM.

Authors

Christian T. Stackhouse, Joshua C. Anderson, Zongliang Yue, Thanh Nguyen, Nicholas J. Eustace, Catherine P. Langford, Jelai Wang, James R. Rowland IV, Chuan Xing, Fady M. Mikhail, Xiangqin Cui, Hasan Alrefai, Ryan E. Bash, Kevin J. Lee, Eddy S. Yang, Anita B. Hjelmeland, C. Ryan Miller, Jake Y. Chen, G. Yancey Gillespie, Christopher D. Willey

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Figure 5

Kinase signaling alterations in RTS targeted with SMIs.

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Kinase signaling alterations in RTS targeted with SMIs.
PCA demonstrates...
PCA demonstrates separation of kinomic signal signatures across 3 components (A), colored by RTS (red) and RTU (blue) with the first 2 components plotted (B). Kinases altered in RTS compared with RTU for JX14P (C), JX14T (D), and JX39P (E) were modeled with GeneGo MetaCore direct-interaction or auto-expand < 20 node networks. Uploaded kinases are indicated with circles as RTS increased (red) or decreased (blue). Lines between nodes indicate interactions with color indicating type (green; positive, red; negative; gray; other). PDX tumor cells were grown as neurospheres, and viability was measured after 7-day treatment (500 nM sitravatinib or brigatinib) with CellTiter-Glo (CTG) (F) and displayed as percentage of vehicle control with bars for SEM, with ***P < 0.0001 calculated using 2-way ANOVA.

Copyright © 2023 American Society for Clinical Investigation
ISSN 2379-3708

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