Loic Pierre Deleyrolle

Glioblastoma (GBM) remains the most lethal primary brain cancer due to its remarkable metabolic plasticity and therapeutic resistance. Here, we identify cholesterol dependency as a therapeutically exploitable vulnerability in GBM using two FDA approved drugs: the H1 histamine antagonist clemastine and the retinoid X receptor agonist bexarotene. Combined treatment induces potent synergistic anti tumor activity across patient-derived glioma models, suppressing proliferation, stemness, and survival at sub IC50 concentrations. Mechanistically, this therapy disrupts cholesterol biosynthesis, transport, and homeostasis, triggering endoplasmic reticulum stress and activation of the unfolded protein response, ultimately leading to autophagy and apoptotic cell death. Orthotopic patient derived glioma models recapitulate these mechanisms in vivo, where local intracranial administration significantly reduces tumor progression and prolongs survival using fourfold lower doses than systemic intraperitoneal delivery. Single cell RNA sequencing revealed activation of regeneration and plasticity programs, accompanied by immune microenvironment remodeling and enhanced inflammatory signaling. Importantly, syngeneic models preserved immune cell composition, supporting future integration with immunotherapeutic strategies. Together, these findings establish cholesterol dysregulation induced metabolic collapse as a promising therapeutic approach for GBM.

Tumor-associated macrophages (TAMs) are abundant in the tumor microenvironment (TME) and dampen the immune response, negatively affecting patient survival. Therefore, targeting TAMs could address the limitations of current cancer treatments. However, drug development in this area remains limited. The leukocyte-associated Ig-like receptor 1 (LAIR1), also called CD305, is prominently expressed on the surface of TAMs. We have uncovered what we believe to be a previously unrecognized immunosuppressive LAIR1/factor XIII A/collagen IV pathway across various cancer types. Inhibition of LAIR1, either through knockout ( Lair1 ^–/– ), antibody blockade (anti-Lair1 antibody), or a chimeric antigen receptor (CAR) design (3-in-1 CAR by combining tumor targeting, T cell trafficking, and remodeling of the immunosuppressive TME in 1 CAR construct) provided an enhanced antitumor response. LAIR1 inhibition enhanced peripheral and intratumoral CD8 memory T cell populations, induced a phenotypic shift of M2-like macrophages toward M1 macrophages, and normalized tumor collagen IV and structural components in the TME, facilitating effective tumor–T cell interactions and tumor suppression. Enhanced antitumor responses were observed when Lair1 ^–/– or anti-Lair1 antibody was used alone or in combination with CAR T cells or when the 3-in-1 CAR T cells were used solely in tumor models resistant to chemotherapy–radiation–programmed cell death protein 1 (PD-1) blockade. These findings position LAIR1 inhibition as a promising strategy for cancer immunotherapies.

Protein arginine methyltransferase 5 (PRMT5), which symmetrically dimethylates cytosolic and nuclear proteins, has been demonstrated as an important cancer therapeutic target. In recent years, many advanced achievements in PRMT5 inhibitor development have been made. Most PRMT5 inhibitors in the clinical trial focus on targeting the C-terminal catalytic domain, whereas developing small molecules to interrupt the PRMT5/pICLn (methylosome subunit) protein-protein interface is also of great importance for inhibiting PRMT5. Here, we describe a machine-learning-based virtual screening method and use this novel pipeline to screen small-molecule inhibitors of the PRMT5/pICLn interaction. 18 compounds were manually selected for experimental testing. One compound, Z319334062, showed surface plasmon resonance-binding affinity to the target (K _D = 21.5 μM) and dose-dependently inhibited symmetric dimethylation levels in patient-derived glioblastoma cell lines.

Motivation: Analyzing the local microenvironment of tumor cells can provide significant insights into their complex interactions with their cellular surroundings, including immune cells. By quantifying the prevalence and distances of certain immune cells in the vicinity of tumor cells through a neighborhood analysis, patterns may emerge that indicate specific associations between cell populations. Such analyses can reveal important aspects of tumor-immune dynamics, which may inform therapeutic strategies. This method enables an in-depth exploration of spatial interactions among different cell types, which is crucial for research in oncology, immunology, and developmental biology. Results: We introduce an R Markdown script called SNAQ ^TM ( S ingle-cell Spatial N eighborhood A nalysis and Q uantification), which conducts a neighborhood analysis on immunofluorescent images without the need for extensive coding knowledge. As a demonstration, SNAQ ^TM was used to analyze images of pancreatic ductal adenocarcinoma. Samples stained for DAPI, PanCK, CD68, and PD-L1 were segmented and classified using QuPath. The resulting CSV files were exported into RStudio for further analysis and visualization using SNAQ ^TM . Visualizations include plots revealing the cellular composition of neighborhoods around multiple cell types within a customizable radius. Additionally, the analysis includes measuring the distances between cells of certain types relative to others across multiple regions of interest. Availability and implementation: The R Markdown files that comprise the SNAQ ^TM algorithm and the input data from this paper are freely available on the web at https://github.com/AryehSilver1/SNAQ . Visual Created with BioRender.com.

Glioblastoma (GBM) poses a significant challenge in clinical oncology due to its aggressive nature, heterogeneity, and resistance to therapies. Cancer stem cells (CSCs) play a critical role in GBM, particularly in treatment resistance and tumor relapse, emphasizing the need to comprehend the mechanisms regulating these cells. Also, their multifaceted contributions to the tumor microenvironment (TME) underline their significance, driven by their unique properties. This study aimed to characterize glioblastoma stem cells (GSCs), specifically slow-cycling cells (SCCs), in an immunocompetent murine GBM model to explore their similarities with their human counterparts. Using the KR158 mouse model, we confirmed that SCCs isolated from this model exhibited key traits and functional properties akin to human SCCs. KR158 murine SCCs, expanded in the gliomasphere assay, demonstrated sphere forming ability, self-renewing capacity, positive tumorigenicity, enhanced stemness and resistance to chemotherapy. Together, our findings validate the KR158 murine model as a framework to investigate GSCs and SCCs in GBM pathology, and explore specifically the SCC–immune system communications, understand their role in disease progression, and evaluate the effect of therapeutic strategies targeting these specific connections.

Glioblastoma (GBM) poses a significant challenge in clinical oncology due to its aggressive nature, heterogeneity, and resistance to therapies. Cancer stem cells (CSCs) play a critical role in GBM, particularly in treatment-resistance and tumor relapse, emphasizing the need to comprehend the mechanisms regulating these cells. Also, their multifaceted contributions to the tumor-microenvironment (TME) underline their significance, driven by their unique properties. This study aimed to characterize glioblastoma stem cells (GSCs), specifically slow-cycling cells (SCCs), in an immunocompetent murine GBM model to explore their similarities with their human counterparts. Using the KR158 mouse model, we confirmed that SCCs isolated from this model exhibited key traits and functional properties akin to human SCCs. KR158 murine SCCs, expanded in the gliomasphere assay, demonstrated sphere forming ability, self-renewing capacity, positive tumorigenicity, enhanced stemness and resistance to chemotherapy. Together, our findings validate the KR158 murine model as a framework to investigate GSCs and SCCs in GBM-pathology, and explore specifically the SCC-immune system communications, understand their role in disease progression, and evaluate the effect of therapeutic strategies targeting these specific connections.

We greatly appreciate the interest, careful reading, and appraisal by Mahajan and Schmidt

Tumor Treating Fields (TTFields) have been shown to extend the survival of glioblastoma (GBM) patients. TTFields interfere with a broad range of cellular processes which may contribute to their efficacy. Among these, TTFields disrupt primary cilia stability on GBM cells. Here we asked if concomitant treatment of TTFields with other agents that interfere with GBM ciliogenesis can further suppress GBM cell proliferation in vitro. Aurora Kinase A (AURKA) promotes both cilia disassembly and GBM growth in vitro and in xenograft models. Inhibitors of AURKA such as Alisertib have been previously demonstrated to inhibit cilia disassembly and increase the frequency of cilia in various cell types. However, here we show that physiological concentrations of Alisertib treatment significantly reduced GBM cilia frequency in gliomaspheres across multiple patient derived cell lines, and in patient biopsies treated ex vivo with Alisertib. This activity of Alisertib seems to be glioma cell specific as it did not reduce neuronal or glial cilia frequencies in mixed primary cell cultures from mouse forebrain. Furthermore, Alisertib depletion of glioma cilia appears specific to AURKA inhibition, as a potent AURKB inhibitor, AZD1152, had no effect on GBM ciliary frequency. Treatment of two different GBM patient-derived cell lines with TTFields and Alisertib resulted in a significant reduction in cell proliferation compared to either treatment alone. However, this effect was not cilia-dependent as the combined treatment reduced proliferation in cilia-depleted cell lines lacking, ARL13b , or U87MG cells which are naturally devoid of ARL13B ⁺ cilia. This result is not surprising given the wide range of pathways regulated by AURKA in addition to cilia. Nonetheless, Alisertib-mediated effects on glioma cilia may be a useful biomarker of drug efficacy within tumor tissue. Considering Alisertib has been shown to cross the blood brain barrier and inhibit intracranial growth of xenografted tumor models, our data warrant future studies to explore whether concomitant Alisertib and TTFields exposure prolongs survival of brain tumor-bearing animals in vivo.