Glioblastoma (GBM) is the most common primary adult brain tumor. Despite extensive efforts, the median survival for GBM patients is approximately 14 months. GBM therapy could benefit greatly from patient-specific targeted therapies that maximize treatment efficacy. Here we report a platform termed SynergySeq to identify drug combinations for the treatment of GBM by integrating information from The Cancer Genome Atlas (TCGA) and the Library of Integrated Network-Based Cellular Signatures (LINCS). We identify differentially expressed genes in GBM samples and devise a consensus gene expression signature for each compound using LINCS L1000 transcriptional profiling data. The SynergySeq platform computes disease discordance and drug concordance to identify combinations of FDA-approved drugs that induce a synergistic response in GBM. Collectively, our studies demonstrate that combining disease-specific gene expression signatures with LINCS small molecule perturbagen-response signatures can identify preclinical combinations for GBM, which can potentially be tested in humans.

Bromodomain and extraterminal domain (BET) proteins have emerged as therapeutic targets in multiple cancers, including the most common primary adult brain tumor glioblastoma (GBM). Although several BET inhibitors have entered clinical trials, few are brain penetrant. We have generated UM-002, a novel brain penetrant BET inhibitor that reduces GBM cell proliferation in vitro and in a human cerebral brain organoid model. Since UM-002 is more potent than other BET inhibitors, it could potentially be developed for GBM treatment. Furthermore, UM-002 treatment reduces the expression of cell-cycle related genes in vivo and reduces the expression of invasion related genes within the non-proliferative cells present in tumors as measured by single cell RNA-sequencing. These studies suggest that BET inhibition alters the transcriptional landscape of GBM tumors, which has implications for designing combination therapies. Importantly, they also provide an integrated dataset that combines in vitro and ex vivo studies with in vivo single-cell RNA-sequencing to characterize a novel BET inhibitor in GBM.

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Dysregulation of Sonic hedgehog (SHH) signaling drives the growth of distinct cancer subtypes, including medulloblastoma (MB). Such cancers have been treated in the clinic with a number of clinically relevant SHH inhibitors, the majority of which target the upstream SHH regulator, Smoothened (SMO). Despite considerable efficacy, many of these patients develop resistance to these drugs, primarily due to mutations in SMO. Therefore, it is essential to identify druggable, signaling components downstream of SMO to target in SMO inhibitor resistant cancers. We utilized an integrated functional genomics approach to identify epigenetic regulators of SHH- signaling and identified a novel complex of Ubiquitin-like with PHD and RING finger domains 1 (UHRF1), DNA methyltransferase 1 (DNMT1), and GLI proteins. We show that this complex is distinct from previously described UHRF1/DNMT1 complexes, suggesting that it works in concert to regulate GLI activity in SHH driven tumors. Importantly, we show that UHRF1/DNMT1/GLI complex stability is targeted by a repurposed FDA-approved therapy, with a subsequent reduction in the growth of SHH-dependent MB ex vivo and in vivo. Implications: This work describes a novel, druggable UHRF1/DNMT1/GLI complex that regulates SHH-dependent tumor growth, and highlights an FDA-approved drug capable of disrupting this complex to attenuate tumor growth.

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Glioblastoma (GBM) is the most common and malignant adult brain tumor. Despite years of research, few advancements have been made in its management. One promising avenue of research has been treatment with BRD4 inhibitors, which decrease oncogene expression in GBM cells. However, resistance to these inhibitors is rapidly acquired. Kinome reprogramming is thought to underlie this resistance, suggesting a need for combination therapy with kinase inhibitors. The goal of this study is to determine whether transcriptomic and kinomic profiling of GBM tumors will identify synergistic drug pairs for GBM treatment. We profiled the active kinome on a set of three newly-diagnosed GBM patient-derived xenograft (PDX) tumors and three recurrent tumors using quantitative SILAC mass spectrometry. Additionally, kinome reprogramming following BET inhibition was profiled in vitro using the BET inhibitor JQ1. Kinome activity was uploaded into our novel computational platform, SynergySeq, to assess the synergistic potential of kinase inhibitors with JQ1. Additionally, single cell RNA-sequencing of a GBM tumor was used to determine the cell populations affected by each inhibitor. To quantify synergy in response to combination therapy, cells were treated with a combination matrix of JQ1 and a kinase inhibitor in variable concentrations and cell death was quantified via ATP levels. Our results showed that newly-diagnosed tumors were predicted to be sensitive to combined BET inhibition with Bcr/Abl, EGFR, and FGFR kinase inhibitors. Recurrent tumors were sensitive to combined Bcr/Abl and FGFR inhibitors but were not sensitive to EGFR inhibitors. We screened inhibitors in vitro and found a synergistic effect for the combination of JQ1 and TAS120, a pan-FGFR inhibitor. These data suggest that clinically a brain penetrant BET inhibitor should be effective in combination with a brain penetrant FGFR inhibitor. Importantly, our computational platform is a novel informatics-based approach for targeted therapy in a patient-specific and disease-specific manner.