PROJECT SUMMARY/ABSTRACT The standard of care for glioblastoma (GBM), the most lethal malignant brain tumor in adults, has remained unchanged since 2005 and only achieves a median overall survival of 14.6 months. Although several subgroups of GBM based on mutational landscape and gene expression have been identified, they have no therapeutic implications. Among these subgroups of GBM, tumors with neurofibromin 1 (NF1) mutations and deletions have the worst overall survival compared to NF1 wild-type (WT) GBM. NF1 alterations have been shown to have significant biological effects in several contexts, NF1 loss in GBM has, for example, been associated with an immunosuppressive tumor microenvironment (TME). Thus, I propose that identifying NF1- associated features of GBM may hold the key for effective therapeutic targeting of this subgroup. Despite the urgent need for therapeutic target discovery in the field of oncology, in general, there are no standardized approaches, and most studies only use a single omics technology, failing to simultaneously study both the genomics of cancer cells and the functional status of the TME, features that influence one another. Given these shortcomings, I will test the hypothesis that there are NF1-specific topological patterns in cell phenotypes and RAS pathway activity in NF1 altered GBM tumors. This has the potential to identify novel therapeutic targets for this GBM subset while providing a framework for universal therapeutic target discovery using integrated multiplexed immunofluorescence (mpIF), a method for in situ single-cell profiling, and spatial transcriptomics (ST). I have curated a cohort of 84 GBM patients (42 NF1 altered and 42 NF1 WT) with targeted panel-based genome sequencing, and I will perform mpIF and ST on adjacent formalin-fixed paraffin- embedded tumors from these patients. In Aim 1, I will optimize a computational model for cell phenotype assignment in mpIF data and use spatial co-clustering of mpIF and ST to strengthen inferences. I will then apply this model to mpIF and ST data collected on my GBM cohort to define the spatial topology of the TME relative to NF1 alteration. In Aim 2, I will define the spatial topology of RAS pathway activity, which has yet to be explored at the single-cell level in GBM and in the context of NF1. I will then relate RAS pathway activity in cancer cells to the functional state of the TME using spatial clustering. My overall goal is to integrate mpIF and ST data and develop a standardized statistical workflow for analysis of these data to identify novel therapeutic targets for NF1 altered GBM and also present a blueprint for future cancer target discovery research.