EPCO-23. A SINGLE-CELL BASED PRECISION MEDICINE APPROACH USING GLIOBLASTOMA PATIENT-SPECIFIC MODELS

Glioblastoma is a heterogeneous tumor made up of cell states that evolve over time. We modeled tumor evolutionary trajectories during standard-of-care treatment using multimodal single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multimodal data with single cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that regulate subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches in a concurrent, neo-adjuvant, or recurrent setting. Our proof-of-concept work herein provides the basis for the development of a modeling and analytical system that enables single-cell characterization of an individual patient’s tumor and inferred therapeutic vulnerabilities. Although further validation is required, in the form of in vivo studies of these putative druggable targets, our preliminary analysis and results suggest that systems biology techniques can be used to infer and predict therapeutic vulnerabilities that are either selected or induced during standard-of-care treatment. Ultimately, the information gathered from such systematic modeling and analysis of individual tumors may inform clinical treatment in a more targeted manner and enable a rational, tailored precision medicine that accounts for intratumoral cell heterogeneity.