Cross-species Comparison Reveals Therapeutic Vulnerabilities Halting Glioblastoma Progression

The growth of a tumor is tightly linked to the distribution of its cells along a continuum of activation states. Here, we systematically decode the activation state architecture (ASA) in a glioblastoma (GBM) patient cohort through comparison to adult murine neural stem cells. Modelling of these data forecast how tumor cells organize to sustain growth and identifies rate of activation as the main predictor of growth. Accordingly, patients with a higher quiescence fraction exhibit improved outcomes. Further, DNA methylation arrays enable ASA-related patient stratification. Comparison of healthy and malignant gene expression dynamics reveals dysregulation of the Wnt-antagonist SFRP1 at the quiescence to activation transition. SFRP1 overexpression renders GBM quiescent and increases overall survival of tumor-bearing mice. Surprisingly, it does so through reprogramming the tumor’s stem-like methylome into an astrocyte-like one. Our findings offer a framework for patient stratification, biomarker identification, and development of therapeutic avenues to halt GBM progression. Pre-print on bioRxiv Data confocal_raw.zip contains the max-projected stainings of the ROIs. The 3D confocal images that were used to segment the nuclei are not included due to their size, but can be made available on request. The voxel size is (1., 0.142, 0.142) um/pixel. confocal_segmentation.zip contains the nuclei segmentation that was obtained using mesmer and a custom 3D stitching approach. resolve_raw.zip contains the raw data as produced by Resolve Bioscience, i.e. raw 3D transcript counts and a 2D DAPI stain. resolve_registered.zip contains the spatial transcript counts after 3D registration, so their coordinate system is aligned to the confocal images and segmentations. segmentation_pipeline.zip contains the code that was used to process the data from raw images and transcript counts to the final segmented cells. segmentation_result.zip contains the final result of the segmentation, as well as the code that does the celltype assignment and neighbourhood enrichment. The code that was used to process and segment the spatial data can also be found on GitHub (anders-biostat/ResolveTools). Parameters The original transcripts are aligned with the Resolve DAPI images, and their voxels have size (0.3125, 0.138, 0.138) microns (zyx, respectively). Our confocal images have a voxel size of (1., 0.142, 0.142) microns (zyx, respectively).