Identifying clinically relevant cell state interactions in the tumor microenvironment of IDH-mutant gliomas using CSI-TME.

Tumor microenvironment (TME) is characterized by a milieu of distinct cell types that exist in heterogenous transcriptional states across tumors. Functional interactions among these cell states drive tumor progression and therapy response. Systematic characterization of functional cell-state interactions (CSIs) remains challenging due to the paucity of scRNA-seq cohorts with clinical information on one hand, and the lack of cellular context in bulk RNA-seq cohorts on the other. We present CSI-TME, a computational pipeline that extends the concept of gene interactions, such as synthetic lethality, to cell states, to infer prognostic CSIs by directly leveraging large cohorts of bulk transcriptomic datasets. Applied CSI-TME to IDH-mutant gliomas, we identified a highly reproducible cell-state interaction network (CSIN) that is predominantly pro-tumor and differentially activated in IDH-mut astrocytoma versus oligodendroglioma. Malignant cell states within the CSIN resemble multiple neuronal lineages, including astrocyte-like and oligodendrocyte-progenitor-like programs, and reveal key interactions between glioma stem cells and T cells. CSIN stratifies patient response to immune-checkpoint blockade therapy. Roughly 20% of CSIs involve direct ligand-receptor communication, and co-localize in spatial-transcriptomic datasets, most notably for a pro-tumorigenic interaction between tip-like endothelial cells and hypoxic malignant cells supported by multiple ligand-receptor interactions. Interestingly, anti-tumor CSIs correlated with oncogenic mutations are preferentially active in early stages of cancer, hinting at tissue homeostatic response. Overall, CSI-TME is a novel approach that, leveraging clinical bulk transcriptomic data, identifies prognostic CSIs and therapeutic ligand-receptor targets, while providing novel insight into how interactions among the cell states shape the TME in IDH-mutant glioma.