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.

Glioblastoma multiforme (GBM) is the most malignant primary adult brain tumor. The current standard of care is surgical resection, radiation, and chemotherapy treatment, which extends life in most cases. Unfortunately, tumor recurrence is nearly universal and patients with recurrent glioblastoma typically survive <1 year. Therefore, new therapies and therapeutic combinations need to be developed that can be quickly approved for use in patients. However, in order to gain approval, therapies need to be safe as well as effective. One possible means of attaining rapid approval is repurposing FDA approved compounds for GBM therapy. However, candidate compounds must be able to penetrate the blood-brain barrier (BBB) and therefore a selection process has to be implemented to identify such compounds that can eliminate GBM tumor expansion. We review here psychiatric and non-psychiatric compounds that may be effective in GBM, as well as potential drugs targeting cell death pathways. We also discuss the potential of data-driven computational approaches to identify compounds that induce cell death in GBM cells, enabled by large reference databases such as the Library of Integrated Network Cell Signatures (LINCS). Finally, we argue that identifying pathways dysregulated in GBM in a patient specific manner is essential for effective repurposing in GBM and other gliomas.

Chromatin-modifying enzymes, and specifically the protein arginine methyltransferases (PRMTs), have emerged as important targets in cancer. Here, we investigated the role of CARM1 in normal and malignant hematopoiesis. Using conditional knockout mice, we show that loss of CARM1 has little effect on normal hematopoiesis. Strikingly, knockout of Carm1 abrogates both the initiation and maintenance of acute myeloid leukemia (AML) driven by oncogenic transcription factors. We show that CARM1 knockdown impairs cell-cycle progression, promotes myeloid differentiation, and ultimately induces apoptosis. Finally, we utilize a selective, small-molecule inhibitor of CARM1 to validate the efficacy of CARM1 inhibition in leukemia cells in vitro and in vivo. Collectively, this work suggests that targeting CARM1 may be an effective therapeutic strategy for AML.

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.

In the originally published version of this paper, author Anna M. Jermakowicz's name was spelled incorrectly as ''Anna McGrew-Jermacowicz'' and author Daniel G. Tenen's name was spelled incorrectly as ''Daniel Tenen.'' The correct spellings of the names are ''Anna M. Jermakowicz'' and ''Daniel G. Tenen.

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.

Glioblastoma (GBM) remains the most common adult brain tumor, with poor survival expectations, and no new therapeutic modalities approved in the last decade. Our laboratories have recently demonstrated that the integration of a transcriptional disease signature obtained from The Cancer Genome Atlas’ GBM dataset with transcriptional cell drug-response signatures in the LINCS L1000 dataset yields possible combinatorial therapeutics. Considering the extreme intra-tumor heterogeneity associated with the disease, we hypothesize that the utilization of single-cell RNA-sequencing (scRNA-seq) of patient tumors will further strengthen our predictive model by providing insight on the unique transcriptomes of the cellular niches present within these tumors, and into the transcriptional dynamics of these same cellular niches. By sequencing single-cell transcriptomes from recurrent GBM tumors resected from patients at the University of Miami, and integrating our datasets with previously published scRNA-seq data from primary GBM tumors, we are able to gain additional insight into the differences between these clinical distinctions. We have analyzed the differential expression of kinases both across and within distinct cell populations of primary and recurrent GBM tumors. This transcriptional map of kinase expression represents the heterogeneity of potential targets within individual tumors and between recurrent and primary GBM. Additionally, by generating disease signatures unique to each cellular population, and integrating these with transcriptional drug-response signatures from LINCS, we are able to predict compounds to target specific cell populations within GMB tumors. Additional computational techniques such as RNA velocity analysis and cell cycle scoring elucidate temporal insights to further prioritize these cell-type specific therapeutics, and reveal the intra-cellular dynamics present within these tumors. Collectively, our studies suggest that we have developed a novel omics pipeline based on the single cell RNA-sequencing of individual GBM cells that addresses intra-tumor heterogeneity, and may lead to novel therapeutic combinations for the treatment of this incurable disease.

Glioblastoma (GBM) is the most common and aggressive adult brain tumor. Despite years of research, clinical trials have not improved the outcome for GBM. Standard of care for newly diagnosed GBM includes surgical resection, followed by radiation and chemotherapy. Tumor recurrence is inevitable and since most patients are not candidates for a second surgical resection, there is an urgent need to identify resistance mechanisms that arise in recurrent GBM. We postulated that examining the differences of activated kinases between newly diagnosed and recurrent GBM may provide insight to resistance mechanisms. To map the kinome landscape of newly diagnosed (nGBM) and recurrent GBM (rGBM) patient derived xenograft tumors, we used Multiplexed Inhibitor Beads and Mass Spectrometry (MIB-MS). We performed pathway analysis of kinases that differed in MIB-binding between nGBM and rGBM to identify kinase-driven signaling pathways. We also analyzed transcriptional profiles to determine the overlap in signaling pathways seen using proteomics or transcriptomics. Using MIB-MS kinome profiling, we found key differences in kinase-driven signaling pathways that may account for the increase in aggressive behavior seen in recurrent GBM. This included a shift in pathways driving cell invasion and proliferation, as well as upregulation of signaling pathways that drive GBM stem-cell like cell differentiation. Analysis of RNA-sequencing showed no statistically significant differences between enriched gene ontologies in nGBM and rGBM, demonstrating the importance of MIB-MS kinome profiling. Collectively, these studies suggest that kinome profiling may inform future clinical trials for kinase inhibitors in GBM.