Comparison of pharmacological inhibitors of lysine-specific demethylase 1 in glioblastoma stem cells reveals inhibitor-specific efficacy profiles - Stitzlein, Lea M.; Gangadharan, Achintyan; Walsh, Leslie M.; Nam, Deokhwa; Espejo, Alexsandra B.; Singh, Melissa M.; Patel, Kareena H.; Lu, Yue; Su, Xiaoping; Ezhilarasan, Ravesanker; Gumin, Joy; Singh, Sanjay; et al. (Frontiers in Neurology, 2023)
Introduction Improved therapies for glioblastoma (GBM) are desperately needed and require preclinical evaluation in models that capture tumor heterogeneity and intrinsic resistance seen in patients. Epigenetic alterations have been well documented in GBM and lysine-specific demethylase 1 (LSD1/KDM1A) is amongst the chromatin modifiers implicated in stem cell maintenance, growth and differentiation. Pharmacological inhibition of LSD1 is clinically relevant, with numerous compounds in various phases of preclinical and clinical development, but an evaluation and comparison of LSD1 inhibitors in patient-derived GBM models is lacking. Methods To assess concordance between knockdown of LSD1 and inhibition of LSD1 using a prototype inhibitor in GBM, we performed RNA-seq to identify genes and biological processes associated with inhibition. Efficacy of various LSD1 inhibitors was assessed in nine patient-derived glioblastoma stem cell (GSC) lines and an orthotopic xenograft mouse model. Results LSD1 inhibitors had cytotoxic and selective effects regardless of GSC radiosensitivity or molecular subtype. In vivo, LSD1 inhibition via GSK-LSD1 led to a delayed reduction in tumor burden; however, tumor regrowth occurred. Comparison of GBM lines by RNA-seq was used to identify genes that may predict resistance to LSD1 inhibitors. We identified five genes that correlate with resistance to LSD1 inhibition in treatment resistant GSCs, in GSK-LSD1 treated mice, and in GBM patients with low LSD1 expression. Conclusion Collectively, the growth inhibitory effects of LSD1 inhibition across a panel of GSC models and identification of genes that may predict resistance has potential to guide future combination therapies.