Spatiotemporal heterogeneity of glioblastoma is dictated by microenvironmental interference

Glioblastomas are highly malignant tumors of the central nervous system. Evidence suggests that these tumors display large intra- and inter-patient heterogeneity hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies1-3. However, the source for dynamic reorganization of cellular states within their spatial context remains elusive. Here, we in-depth characterized glioblastomas by spatially resolved transcriptomics, metabolomics and proteomics. By deciphering exclusive and shared transcriptional programs across patients, we inferred that glioblastomas develop along defined neural lineages and adapt to inflammatory or metabolic stimuli reminiscent of reactive transformation in mature astrocytes. Metabolic profiling and imaging mass cytometry supported the assumption that tumor heterogeneity is dictated by microenvironmental alterations. Analysis of copy number variation (CNV) revealed a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. Deconvolution of age-dependent transcriptional programs in malignant and non-malignant specimens identified the aging environment as the major driver of inflammatory transformation in GBM, suggesting that tumor cells adopt transcriptional programs similar to inflammatory transformation in astrocytes. Glioblastoma stem cells implanted into human neocortical slices of varying age levels, independently confirmed that the ageing environment dynamically shapes the intratumoral heterogeneity towards reactive transcriptional programs. Our findings provide insights into the spatial architecture of glioblastoma, suggesting that both locally inherent tumor as well as global alterations of the tumor microenvironment shape its transcriptional heterogeneity. Global age-related inflammation in the human brain is driving distinct transcriptional transformation in glioblastomas, which requires an adjustment of the currently prevailing glioma models.