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An in vivo model of glioblastoma radiation resistance identifies long noncoding RNAs and targetable kinases
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
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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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 1

GBM PDX RT-selected models exhibit differences in survival and molecular diversity.

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GBM PDX RT-selected models exhibit differences in survival and molecular...
(A) Initial RT sensitivity status and median doubling times for tumors after RT. (B) Serial in vivo RT selection methodology. (C) RTS (red dashed line) and RTU (blue solid line) combined endpoint probabilities following initiation of radiation treatment. P value calculated from log-rank test. (D) Changes in median doubling time pre- and post-RT selection. The box plots depict the minimum and maximum values (whiskers), the upper and lower quartiles, and the median. The length of the box represents the interquartile range. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. P values calculated by 2-sided t test. (E) Basic characteristics of PDXs used for RTS with patient sex (M, male; F, female), patient age, and original source. UAB, University of Alabama at Birmingham. (F) Log2 fold change copy number calls from patternCNV for GBM hallmark genes. Median survival intracranial (n = 6). Median doubling time (n = 5) per pair (n = 80 total).

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