Logo: to the web site of Uppsala University

Glioblastoma (GBM) is the most common primary brain cancer. It causes death mainly by local invasion via several routes, including infiltration of white matter tracts and penetration of perivascular spaces. However, the pathways that mediate these invasion routes are only partly known. Here, we conduct an integrative study to identify cell states and central drivers of route-specific invasion in GBM. Combining single-cell profiling and spatial protein detection in patient-derived xenograft models and clinical tumor samples, we demonstrate a close association between the differentiation state of GBM cells and their choice of invasion route. Computational modeling identifies ANXA1 as a driver of perivascular involvement in GBM cells with mesenchymal differentiation and the transcription factors RFX4 and HOPX as orchestrators of growth and differentiation in diffusely invading GBM cells. Ablation of these targets in tumor cells alters their invasion route, redistributes the cell states, and extends survival in xenografted mice. Our results define a close association between GBM cell differentiation states and invasion routes, identify functional biomarkers of route-specific invasion, and point toward targeted modulation of specific invasive cell states as a therapeutic strategy in GBM.

The brain tumor glioblastoma is characterized by extensive tumor cell plasticity, a property increasingly recognized as a therapeutic vulnerability. However, our ability to monitor plastic changes in living cells remains limited, hindering efforts to identify the genetic dependencies that govern state changes. Here, we present CRISPR-tag, a method for endogenously tagging cell-state proteins, enabling real-time, single-cell monitoring of GB state dynamics in vitro and ex vivo. Guided by integrative transcriptomic analysis, we generated a panel of fluorescent reporters capturing the major axes of GB cellular variation. These reporters reveal diverse phenomena, including oscillatory expression, lineage-coupled state inheritance, and spatially dependent drug responses in explant assays. Leveraging CRISPR-tag with genome-wide perturbations, we identify regulators of cellular states. Together, CRISPR-tag establishes a scalable platform for resolving and causally perturbing cell state trajectories in glioblastoma, providing a basis for the mechanistic analysis of tumor plasticity and the development of state-targeted therapies.

Background: Patient-derived xenograft (PDX) models of glioblastoma (GBM) are a central tool for neuro-oncology research and drug development, enabling the detection of patient-specific differences in growth, and in vivo drug response. However, existing PDX models are not well suited for large-scale or automated studies. Thus, here, we investigate if a fast zebrafish-based PDX model, supported by longitudinal, AI-driven image analysis, can recapitulate key aspects of glioblastoma growth and enable case-comparative drug testing.

Methods: We engrafted 11 GFP-tagged patient-derived GBM IDH wild-type cell cultures (PDCs) into 1-day-old zebrafish embryos, and monitored fish with 96-well live microscopy and convolutional neural network analysis. Using light-sheet imaging of whole embryos, we analyzed further the invasive growth of tumor cells.

Results: Our pipeline enables automatic and robust longitudinal observation of tumor growth and survival of individual fish. The 11 PDCs expressed growth, invasion and survival heterogeneity, and tumor initiation correlated strongly with matched mouse PDX counterparts (Spearman R = 0.89, p < 0.001). Three PDCs showed a high degree of association between grafted tumor cells and host blood vessels, suggesting a perivascular invasion phenotype. In vivo evaluation of the drug marizomib, currently in clinical trials for GBM, showed an effect on fish survival corresponding to PDC in vitro and in vivo marizomib sensitivity.

Conclusions: Zebrafish xenografts of GBM, monitored by AI methods in an automated process, present a scalable alternative to mouse xenograft models for the study of glioblastoma tumor initiation, growth, and invasion, applicable to patient-specific drug evaluation.