(573h) Multi-Modal Single-Cell Characterization As a Basis for Precision Therapy for Intratumoral Heterogeneity in Glioblastoma

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and the matched recurrent tumor collected at autopsy. We analyzed the single-cell, multi-omic data with the Mechanistic Inference of Node-Edge Relationships (MINER) to identify a network of 52 regulators that mediated treatment-induced shifts in xenograft tumor-cell states, several of which were also reflected in recurrence. By integrating MINER-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that subtend cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, we identified potential drugs that target regulatory network modules that were active at distinct stages of disease/post-treatment progression. Together, these results support a framework for applying single-cell-based approaches that would enable precision medicine applications to an individual patient in a concurrent, adjuvant, or recurrent setting.