Patient-derived glioblastoma cultures as a tool for small-molecule drug discovery
Ling F. Ye, Eduard Reznik, Joshua M. Korn, Fallon Lin, Guizhi Yang, Kimberly Malesky, Hui Gao, Alice Loo, Raymond Pagliarini, Tom Mikkelsen, Donald C. Lo, Ana C. deCarvalho, Brent R. Stockwell
Abstract
// Ling F. Ye 1 , Eduard Reznik 1 , Joshua M. Korn 2 , Fallon Lin 2 , Guizhi Yang 2 , Kimberly Malesky 2 , Hui Gao 2 , Alice Loo 2 , Raymond Pagliarini 2 , Tom Mikkelsen 4 , Donald C. Lo 3 , Ana C. deCarvalho 4 and Brent R. Stockwell 1 , 5 1 Department of Biological Sciences, Columbia University, New York, NY 10027, USA 2 Novartis Institutes for BioMedical Research, Cambridge, MA 02139, USA 3 Center for Drug Discovery and Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA 4 Departments of Neurosurgery, Henry Ford Hospital, Detroit, MI 48202, USA 5 Department of Chemistry, Columbia University, New York, NY 10027, USA Correspondence to: Ana C. deCarvalho, email: [email protected] Brent R. Stockwell, email: [email protected] Keywords: cell death; chemical biology; glioma; cancer Received: November 30, 2019     Accepted: January 04, 2020     Published: January 28, 2020 ABSTRACT There is a compelling need for new therapeutic strategies for glioblastoma multiforme (GBM). Preclinical target and therapeutic discovery for GBMs is primarily conducted using cell lines grown in serum-containing media, such as U-87 MG, which do not reflect the gene expression profiles of tumors found in GBM patients. To address this lack of representative models, we sought to develop a panel of patient-derived GBM models and characterize their genomic features, using RNA sequencing (RNA-seq) and growth characteristics, both when grown as neurospheres in culture, and grown orthotopically as xenografts in mice. When we compared these with commonly used GBM cell lines in the Cancer Cell Line Encyclopedia (CCLE), we found these patient-derived models to have greater diversity in gene expression and to better correspond to GBMs directly sequenced from patient tumor samples. We also evaluated the potential of these models for targeted therapy, by using the genomic characterization to identify small molecules that inhibit the growth of distinct subsets of GBMs, paving the way for precision medicines for GBM.