Treating glioblastoma often makes a MES

IDHwt glioblastomas can be stratified by their transcriptional response to standard treatment, with implications for targeted therapy

BACKGROUND

Glioblastoma (GBM) brain tumors lacking IDH1 mutations (IDHwt) have the worst prognosis of all brain neoplasms. Patients receive surgery and chemoradiotherapy but tumors almost always fatally recur.

RESULTS

Using RNA sequencing data from 107 pairs of pre- and post-standard treatment locally recurrent IDHwt GBM tumors, we identify two responder subtypes based on longitudinal changes in gene expression. In two thirds of patients, a specific subset of genes is upregulated from primary to recurrence (Up responders), and in one third, the same genes are downregulated (Down responders), specifically in neoplastic cells. Characterization of the responder subtypes indicates subtype-specific adaptive treatment resistance mechanisms that are associated with distinct changes in the tumor microenvironment. In Up responders, recurrent tumors are enriched in quiescent proneural GBM stem cells and differentiated neoplastic cells, with increased interaction with the surrounding normal brain and neurotransmitter signaling, whereas Down responders commonly undergo mesenchymal transition. ChIP-sequencing data from longitudinal GBM tumors suggests that the observed transcriptional reprogramming could be driven by Polycomb-based chromatin remodeling rather than DNA methylation.

CONCLUSIONS

We show that the responder subtype is cancer-cell intrinsic, recapitulated in in vitro GBM cell models, and influenced by the presence of the tumor microenvironment. Stratifying GBM tumors by responder subtype may lead to more effective treatment.

Single-cell profiling and zebrafish avatars reveal LGALS1 as immunomodulating target in glioblastoma

Glioblastoma (GBM) remains the most malignant primary brain tumor, with a median survival rarely exceeding 2 years. Tumor heterogeneity and an immunosuppressive microenvironment are key factors contributing to the poor response rates of current therapeutic approaches. GBM-associated macrophages (GAMs) often exhibit immunosuppressive features that promote tumor progression. However, their dynamic interactions with GBM tumor cells remain poorly understood. Here, we used patient-derived GBM stem cell cultures and combined single-cell RNA sequencing of GAM-GBM co-cultures and real-time in vivo monitoring of GAM-GBM interactions in orthotopic zebrafish xenograft models to provide insight into the cellular, molecular, and spatial heterogeneity. Our analyses revealed substantial heterogeneity across GBM patients in GBM-induced GAM polarization and the ability to attract and activate GAMs-features that correlated with patient survival. Differential gene expression analysis, immunohistochemistry on original tumor samples, and knock-out experiments in zebrafish subsequently identified LGALS1 as a primary regulator of immunosuppression. Overall, our work highlights that GAM-GBM interactions can be studied in a clinically relevant way using co-cultures and avatar models, while offering new opportunities to identify promising immune-modulating targets.

Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis

Glioblastoma (GBM) is the most frequent malignant brain tumor, the relapse of which is unavoidable following standard treatment. However, the effective treatment for recurrent GBM is lacking, necessitating the understanding of key mechanisms driving tumor recurrence and the identification of new targets for intervention. Here, we integrated single-cell RNA-sequencing data spanning 36 patient-matched primary and recurrent GBM (pGBM and rGBM) specimens, with 6 longitudinal GBM spatial transcriptomics to explore molecular alterations at recurrence, with each cell type characterized in parallel. Genes involved in extracellular matrix (ECM) organization are preferentially enriched in rGBM cells, and MAFK is highlighted as a potential regulator. Notably, we uncover a unique subpopulation of GBM cells that is much less detected in pGBM and highly expresses ECM and mesenchyme related genes, suggesting it may contribute to the molecular transition of rGBM. Further regulatory network analysis reveals that transcription factors, such as NFATC4 and activator protein 1 members, may function as hub regulators. All non-tumor cells alter their specific sets of genes as well and certain subgroups of myeloid cells appear to be physically associated with the mesenchyme-like GBM subpopulation. Altogether, our study provides new insights into the molecular understanding of GBM relapse and candidate targets for rGBM treatment.