Chronically radiation-exposed survivor glioblastoma cells display poor response to Chk1 inhibition

Abstract

ABSTRACTGlioblastoma is the most common type of primary brain tumor with an aggressive clinical course, and one of the cornerstones in its treatment regimen is radiotherapy. However, tumor cells surviving after radiation is an indicator of treatment failure; therefore, better understanding of the molecular mechanisms regulating radiotherapy response is of utmost importance. In this study, we generated multiple clinically relevant irradiation exposed models, where we applied fractionated radiotherapy over a long period of time and selected irradiation-survivor (IR-Surv) glioblastoma cell populations. In these cells, we examined the transcriptomic alterations, cell cycle and growth rate changes as well as responses to secondary radiotherapy and DNA damage response (DDR) modulators. Accordingly, IR-Surv cells exhibited slower growth and partly retained their ability to resist secondary irradiation. Transcriptomic analysis revealed that IR-Surv cells upregulated the expression of DDR-related genes, such as CHK1, ATM, ATR, MGMT, and had better DNA repair capacity as an adaptive mechanism. Separately, we report IR-Surv cells to display downregulation of hypoxic signature and the lower induction of hypoxia target genes and not exhibiting the same level of hypoxia-induced changes with naïve glioblastoma cells, as gauged by exposing cells to different hypoxia conditions. We also showed that Chk1 inhibition alone or in combination with irradiation significantly reduces cell viability in both naïve and IR-Surv cells. However, IR-Surv cells were markedly less sensitive to Chk1 inhibition under hypoxic conditions. In conclusion, consistent with previous reports, we demonstrate the utility of combining DDR inhibitors and irradiation as a successful approach for both naïve and IR-Surv glioblastoma cells as long as cells are refrained from hypoxic conditions. Thus, our findings with clinically relevant radiation survivor models will have future translational implications and benefit the optimization of combination therapies for glioblastoma patients.