Glioblastoma (GBM) is the most common malignant and aggressive adult brain tumour characterized by its clinical behaviour, with high growth rate, diffuse invasiveness, and low response to therapies. Despite of multimodal treatment, which consists in extensive surgery followed by radiotherapy and chemotherapy (temozolomide, TMZ) the mean life expectancy for patients with GBM is less than 2 years. Although remarkable research efforts have been done in the last decades, no significant improvement in patient survival have been obtained from 2005, also due to the lack of appropriate models to study the role of cellular heterogeneity and microenvironment in GBM growth, invasiveness and drug response. Among the main causes of therapeutic failure there is the ability of GBM cells to rapidly invade the brain parenchyma, greatly limiting successful surgical tumour debulking and the presence of cancer stem cells (CSC, also called tumour-initiating cells, TICs) which were identified over a decade ago also in GBM. Cancer stem cells are responsible for the malignant properties of tumours, have stem properties (self-renewal and differentiation), chemo- /radio-resistance and are able to expand to re-initiate tumours, promoting tissue infiltration, metastasis and relapse. CSC display cellular plasticity that is the ability to move between cell states to efficiently adapt to signals from the tumour microenvironment, such as terminal differentiation into a nontumorigenic state, transition into an invasive mesenchymal phenotype (epithelial–mesenchymal transition, EMT), or trans-differentiation into endothelial-like cells, leading to tumour angiogenesis. Conventional chemotherapies can eliminate bulk tumour cells while CSC evade most therapies, thus in order to GBM eradication, it will be crucial to find compounds able to effectively target CSC. Metformin, a widely used antidiabetic drug shows an antiproliferative effect on GBM CSC (GSC), via the inhibition of the CLIC-1 (Chloride Intracellular Channel 1) mediated ion current. Since other biguanides (both linear or cyclic) have demonstrated to act via CLIC-1 inhibition, this mechanism of action has been proposed to be a pharmacological class effect. Thus, we tested novel biguanide derivatives to enhance the metformin antitumour effect and pharmacological profile. Firstly, we performed a screening of the antiproliferative activity (by MTT assay and cell count) of the novel biguanide in-vitro, on patient-derived GSC and to assess the absence of off-target activity we used umbilical cord mesenchymal stem cells. We identified two compounds, Q54 (IC50 0.43 mM) and Q48 (IC50 0.083 mM) which exhibit a more potent antiproliferative effect as compared to metformin, the absence of off-target activity and the selectivity towards CLIC-1 (tested by electrophysiology recordings). Conversely, Q46 which didn’t show any significant effect was chosen as a positive control. By Boyden chambers assay and Matrigel™ invasion assay, we assessed the impairment of migration and invasion. In zebrafish, Q54 nor Q48 display aspecific toxicity, but Q54 was able to reduce the proliferation of GSCs xenotransplanted in their hindbrain. Moreover, we characterized two 3D models (GSC 3D cultures and tumoroids) by assessing cell proliferation (by EdU labelling), cell subpopulations and drug response. Q54 and Q48 were able to inhibit cell proliferation on GSC 3D cultures. By screening our GSC cultures for the CLIC-1 protein content we found that 2 cultures which spontaneously express low CLIC-1, were able to grow in vivo and to retain stem-like phenotype and functional features in vitro, but in these cultures, Q48 and Q54 displayed reduced potency and efficacy as antiproliferative agents as compared to high CLIC-1-expressing tumours. Thus, this data highlight the potential of Q48 and Q54 to target GSC with a better pharmacological profile as compared to metformin in CLIC-1 expressive culture; indeed, CLIC-1 acts as a booster for GSC proliferation but it is not required for GBM development. In addition, our compounds were tested on three different models that allow us to obtain different information that integrate each other, aiming to obtain more predictive results of what could happen in a GBM. We suggest that this approach could be useful in order to try to overcome the lack of reliable models in GBM research.
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