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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish
Article numbere148717
JournalJCI insight
Volume7
Issue number16
DOIs
StatePublished - Aug 22 2022

Bibliographical note

Publisher Copyright:
© 2022 American Society for Clinical Investigation. All rights reserved.

ASJC Scopus subject areas

  • General Medicine

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