Abstract Glioblastomas (GBMs) are biologically heterogeneous within and between patients. Many previous attempts to characterize this heterogeneity have classified tumors according to their omics similarities. These discrete classifications have predominantly focused on characterizing malignant cells, neglecting the immune and other cell populations that are known to be present. We leverage a manifold learning algorithm to define a low-dimensional transcriptional continuum along which heterogeneous GBM samples organize. This reveals three polarized states: invasive, immune/inflammatory, and proliferative. The location of each sample along this continuum correlates with the abundance of eighteen malignant, immune, and other cell populations. We connect these cell abundances with magnetic resonance imaging and find that the relationship between contrast enhancement and tumor composition varies with patient sex and treatment status. These findings suggest that GBM transcriptional biology is a predictably constrained continuum that contains a limited spectrum of viable cell cohabitation ecologies. Since the relationships between this ecological continuum and imaging vary with patient sex and tumor treatment status, studies that integrate imaging features with tumor biology should incorporate these variables in their design.

Glioblastoma states are defined by cohabitating cellular populations with progression-, imaging- and sex-distinct patterns