Impact of developmental state, p53 status, and interferon signaling on glioblastoma cell response to radiation and temozolomide treatment - Artem Berezovsky; Oluwademilade Nuga; Indrani Datta; Kimberly Bergman; Thais Sabedot; Katherine Gurdziel; Susan Irtenkauf; Laura Hasselbach; Yuling Meng; Claudius Mueller; Emanuel F. . Petricoin; Stephen Brown; et al. (PLoS ONE, 2025)
Glioblastoma (GBM) tumors exhibit extensive genomic, epigenomic, and transcriptional diversity, with significant intratumoral heterogeneity, complicating standard treatment approaches involving radiation (RT) and the DNA-alkylating agent temozolomide (TMZ). In this study, we employed an integrative multi-omics approach, including targeted proteomics, transcriptomics, genomics, and DNA methylation profiling, to investigate the response of a representative panel of GBM patient-derived cancer stem cells (CSCs) to astrocytic differentiation and RT and TMZ treatments. Differentiated CSC progenies retained the expression of key stemness genes and survival pathways, while activating the BMP-Smad signaling pathway and upregulating extracellular matrix components. This was associated with increased resistance to TMZ, though not to RT, across all models. We identified TP53 status as a critical determinant of transcriptional response to both RT and TMZ, which was also modulated by the differentiation state and treatment modality in wildtype (wt) p53 GBM cells. Both mutant and wt p53 models exhibited significant activation of the DNA-damage associated interferon (IFN) response in CSCs and differentiated cells, implicating this pathway in the GBM response to therapy. We observed that activation of NF-κB was positively correlated with the levels of O-6-methylguanine-DNA methyltransferase (MGMT) protein, a direct DNA repair enzyme leading to TMZ resistance, regardless of MGMT promoter methylation status, further supporting the clinical potential for inhibition of NF-kB signaling in GBM treatment. Our integrative analysis of the impact of GBM cell developmental states, in the context of genomic and molecular diversity of patient-derived models, provides valuable insights for pre-clinical studies aimed at optimizing treatment strategies.