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

• Type of Material: Article
• Authors: Christian T Stackhouse, The University of Alabama at Birmingham; Joshua C Anderson, The University of Alabama at Birmingham; Zongliang Yue, The University of Alabama at Birmingham; Thanh Nguyen, The University of Alabama at Birmingham; Nicholas J Eustace, The University of Alabama at Birmingham; Catherine P Langford, The University of Alabama at Birmingham; Jelai Wang, The University of Alabama at Birmingham; James R Rowland IV, The Ohio State University; Chuan Xing, The University of Alabama at Birmingham; Fady Mikhail, Department of Genetics; Xiangqin Cui, Emory University; Hasan Alrefai, The University of Alabama at Birmingham; Ryan E Bash, The University of Alabama at Birmingham; Kevin J Lee, The University of Alabama at Birmingham; Eddy S Yang, The University of Alabama at Birmingham; Anita B Hjelmeland, Department of Cell, Developmental and Integrative Biology; C Ryan Miller, The University of Alabama at Birmingham; Jake Y Chen, The University of Alabama at Birmingham; G Yancey Gillespie, The University of Alabama at Birmingham; Christopher D Willey, The University of Alabama at Birmingham
• Language: English
• Date: 2022-08-22
• Publisher: American Society for Clinical Investigation
• Publication Version: Final Published Version
• Copyright Statement: © 2022 Stackhouse et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1172/jci.insight.148717
• Title of Journal or Parent Work: JCI Insight
• Volume: 7
• Issue: 16
• Keywords: Brain cancer; Oncology; Glioblastoma; Genomics; Noncoding RNAs; RNA, Long Noncoding; Animals; Radiation therapy; Humans; Xenograft Model Antitumor Assays; Disease Models, Animal; Neoplasm Recurrence, Local

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.