Komotar, Ricardo J.

Abstract Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity. GBM tumors contain neoplastic cells within a spectrum of different transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we developed a novel framework, ISOSCELES ( I nferred cell S ensitivity O perating on the integration of S ingle- C ell E xpression and L 1000 E xpression S ignatures), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identified compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states. We validated the significance of these findings in vitro, ex vivo, and in vivo , and identified a novel combination of an OLIG2 inhibitor and Depatux-M for GBM. Our studies suggest that ISOSCELES identifies cell states sensitive and resistant to targeted therapies in GBM and that it can be applied to identify new synergistic combinations. Highlights Integration of GBM single-cell RNA sequencing data with L1000-derived drug response signatures facilitates clustering of tumor cells and small molecules on cell-drug connectivity. Cell-drug connectivity predicts the identities of drug-sensitive and resistant cell states. In silico perturbation analysis using cell-drug connectivity predicts drug-induced changes in the cell-drug connectivity landscape in vivo. In silico perturbation analysis to predict drug-induced changes in the tumor cell-drug connectivity landscape predicts drug combinations that synergize in vivo to extend survival.

Abstract Glioblastoma (GBM) remains the most common and lethal adult primary brain cancer. Two of the most significant issues preventing the development of effective GBM treatments are inter- and intra-tumor heterogeneity. To address these issues, we developed a novel platform termed ISOSCELES (Inferred cell Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures). ISOSCELES integrates single-cell gene expression data in individual GBM tumors with perturbation-response data derived from the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) L1000 dataset to predict sensitive and resistant tumor cell populations. Importantly, we analyzed the predictive power of ISOSCELES in an in vivo xenograft model and demonstrated that ISOSCELES reveals the GBM cell identities primed for lineage expansion during treatment with the aurora kinase inhibitor alisertib. These studies suggest that ISOSCELES can be used to identify sensitive and resistant cell populations to targeted therapies in GBM, which can inform treatment decisions in ongoing and future clinical trials.

Abstract Glioblastoma (GBM) remains the most common adult brain cancer, with a dismal average patient survival of less than two years. No new treatments have been approved for GBM since the introduction of the alkylating agent temozolomide in 2005. Even then, temozolomide treatment only increases the average survival of GBM patients by a few months. Thus, novel therapeutic options are direly needed. The aurora kinases A and B are targetable and overexpressed in GBM, and their expression is highly correlated with patient survival outcomes. Our lab has found that small molecule aurora kinase inhibition reduces GBM tumor growth in vitro and in vivo, however, eventually tumors still grow. Computational analysis integrating compound transcriptional response signatures from the LINCS L1000 dataset with the single-cell RNA-sequencing data of patient GBM tumors resected at the University of Miami predicts that aurora inhibition targets a subset of cells present within any GBM tumor. Results of in vivo single-cell perturbation experiments with the aurora kinase inhibitor alisertib coincide with our predictions and reveal a cellular transcriptional phenotype resistant to aurora kinase inhibition, characterized by a mesenchymal expression program. We find that small molecules that are predicted to target different cell populations from alisertib, including this resistant mesenchymal population, synergize with alisertib to kill GBM cells. As a whole, we have identified the cellular population resistant to aurora kinase inhibition and have developed an analytical framework that identifies synergistic small molecule combinations by identifying compounds that target transcriptionally distinct cellular populations within GBM tumors.

AbstractIntroductionSince the introduction of wearable head‐up displays, there has been much interest in the surgical community adapting this technology into routine surgical practice.MethodsWe used the keywords augmented reality OR wearable device OR head‐up display AND surgery using PubMed, EBSCO, IEEE and SCOPUS databases. After exclusions, 74 published articles that evaluated the utility of wearable head‐up displays in surgical settings were included in our review.ResultsAcross all studies, the most common use of head‐up displays was in cases of live streaming from surgical microscopes, navigation, monitoring of vital signs, and display of preoperative images. The most commonly used head‐up display was Google Glass. Head‐up displays enhanced surgeons' operating experience; common disadvantages include limited battery life, display size and discomfort.ConclusionsDue to ergonomic issues with dual‐screen devices, augmented reality devices with the capacity to overlay images onto the surgical field will be key features of next‐generation surgical head‐up displays.

Significance Glioblastoma Multiforme (GBM) is the most common and deadliest primary brain tumor in adults. As the median survival is approximately 14 mo there is an urgent need for novel therapies. Epigenetic modulators such as bromodomain and extraterminal (BET) proteins are important therapeutic targets in GBM. Bromodomain inhibitors (including I-BET151) suppress proliferation by repressing oncogenes and inducing tumor suppressor genes through unidentified pathways. Here we demonstrate that HOTAIR (HOX transcript antisense RNA) is overexpressed in GBM, where it is crucial to sustain tumor cell proliferation, and that inhibition of HOTAIR by I-BET151 is necessary to induce cell cycle arrest in GBM cells. Our study outlines the mechanism of action underlying the antiproliferative activity of I-BET151, showing for the first time, to our knowledge, that the oncogenic long noncoding RNA HOTAIR is a major target.