Abstract

Glioblastoma (GBM) remains the most frequently diagnosed primary malignant brain cancer in adults. Despite recent progress in understanding the biology of GBM, the clinical outcome for patients remains poor, with a median survival of approximately one year after diagnosis. One factor contributing to failure in clinical trials is the fact that traditional models used in GBM drug discovery poorly recapitulate patient tumors. Previous studies have shown that monensin (MON) analogs, namely esters and amides on C-26 were potent towards various types of cancer cell lines. In the present study we have investigated the activity of these molecules in GBM organoids, as well as in a host:tumor organoid model. Using a mini-ring cell viability assay we have identified seven analogs (IC50 = 91.5 ± 54.4–291.7 ± 68.8 nM) more potent than parent MON (IC50 = 612.6 ± 184.4 nM). Five of these compounds induced substantial DNA fragmentation in GBM organoids, suggestive of apoptotic cell death. The most active analog, compound 1, significantly reduced GBM cell migration, induced PARP degradation, diminished phosphorylation of STAT3, Akt and GSK3β, increased ɣH2AX signaling and upregulated expression of the autophagy associated marker LC3-II. To investigate the activity of MON and compound 1 in a tumor microenvironment, we developed human cerebral organoids (COs) from human induced pluripotent stem cells (iPSCs). The COs showed features of early developing brain such as multiple neural rosettes with a proliferative zone of neural stem cells (Nestin+), neurons (TUJ1 +), primitive ventricular system (SOX2 +/Ki67 +), intermediate zone (TBR2 +) and cortical plate (MAP2 +). In order to generate host:tumor organoids, we co-cultured RFP-labeled U87MG cells with fully formed COs. Compound 1 and MON reduced U87MG tumor size in the COs after four days of treatment and induced a significant reduction of PARP expression. These findings highlight the therapeutic potential of MON analogs towards GBM and support the application of organoid models in anti-cancer drug discovery.

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