Bryce K. Allen

BET bromodomain proteins are required for glioblastoma cell proliferation

Epigenetic proteins have recently emerged as novel anticancer targets. Among these, bromodomain and extra terminal domain (BET) proteins recognize lysine-acetylated histones, thereby regulating gene expression. Newly described small molecules that inhibit BET proteins BRD2, BRD3, and BRD4 reduce proliferation of NUT (nuclear protein in testis)-midline carcinoma, multiple myeloma, and leukemia cells in vitro and in vivo. These findings prompted us to determine whether BET proteins may be therapeutic targets in the most common primary adult brain tumor, glioblastoma (GBM). We performed NanoString analysis of GBM tumor samples and controls to identify novel therapeutic targets. Several cell proliferation assays of GBM cell lines and stem cells were used to analyze the efficacy of the drug I-BET151 relative to temozolomide (TMZ) or cell cycle inhibitors. Lastly, we performed xenograft experiments to determine the efficacy of I-BET151 in vivo. We demonstrate that BRD2 and BRD4 RNA are significantly overexpressed in GBM, suggesting that BET protein inhibition may be an effective means of reducing GBM cell proliferation. Disruption of BRD4 expression in glioblastoma cells reduced cell cycle progression. Similarly, treatment with the BET protein inhibitor I-BET151 reduced GBM cell proliferation in vitro and in vivo. I-BET151 treatment enriched cells at the G1/S cell cycle transition. Importantly, I-BET151 is as potent at inhibiting GBM cell proliferation as TMZ, the current chemotherapy treatment administered to GBM patients. Since I-BET151 inhibits GBM cell proliferation by arresting cell cycle progression, we propose that BET protein inhibition may be a viable therapeutic option for GBM patients suffering from TMZ resistant tumors.

Large-Scale Computational Screening Identifies First in Class Multitarget Inhibitor of EGFR Kinase and BRD4

Inhibition of cancer-promoting kinases is an established therapeutic strategy for the treatment of many cancers, although resistance to kinase inhibitors is common. One way to overcome resistance is to target orthogonal cancer-promoting pathways. Bromo and Extra-Terminal (BET) domain proteins, which belong to the family of epigenetic readers, have recently emerged as promising therapeutic targets in multiple cancers. The development of multitarget drugs that inhibit kinase and BET proteins therefore may be a promising strategy to overcome tumor resistance and prolong therapeutic efficacy in the clinic. We developed a general computational screening approach to identify novel dual kinase/bromodomain inhibitors from millions of commercially available small molecules. Our method integrated machine learning using big datasets of kinase inhibitors and structure-based drug design. Here we describe the computational methodology, including validation and characterization of our models and their application and integration into a scalable virtual screening pipeline. We screened over 6 million commercially available compounds and selected 24 for testing in BRD4 and EGFR biochemical assays. We identified several novel BRD4 inhibitors, among them a first in class dual EGFR-BRD4 inhibitor. Our studies suggest that this computational screening approach may be broadly applicable for identifying dual kinase/BET inhibitors with potential for treating various cancers.

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: Integrated access to diverse large-scale cellular perturbation response data

Abstract The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content.

Epigenetic Pathways and Glioblastoma Treatment: Insights From Signaling Cascades

There is an urgent need to identify novel therapies for glioblastoma (GBM) as most therapies are ineffective. A first step in this process is to identify and validate targets for therapeutic intervention. Epigenetic modulators have emerged as attractive drug targets in several cancers including GBM. These epigenetic regulators affect gene expression without changing the DNA sequence. Recent studies suggest that epigenetic regulators interact with drivers of GBM cell and stem‐like cell proliferation. These drivers include components of the Notch, Hedgehog, and Wingless (WNT) pathways. We highlight recent studies connecting epigenetic and signaling pathways in GBM. We also review systems and big data approaches for identifying patient specific therapies in GBM. Collectively, these studies will identify drug combinations that may be effective in GBM and other cancers. J. Cell. Biochem. 116: 351–363, 2015.

Identification of a Novel Class of BRD4 Inhibitors by Computational Screening and Binding Simulations

Computational screening is a method to prioritize small-molecule compounds based on the structural and biochemical attributes built from ligand and target information. Previously, we have developed a scalable virtual screening workflow to identify novel multitarget kinase/bromodomain inhibitors. In the current study, we identified several novel N-[3-(2-oxo-pyrrolidinyl)phenyl]-benzenesulfonamide derivatives that scored highly in our ensemble docking protocol. We quantified the binding affinity of these compounds for BRD4(BD1) biochemically and generated cocrystal structures, which were deposited in the Protein Data Bank. As the docking poses obtained in the virtual screening pipeline did not align with the experimental cocrystal structures, we evaluated the predictions of their precise binding modes by performing molecular dynamics (MD) simulations. The MD simulations closely reproduced the experimentally observed protein–ligand cocrystal binding conformations and interactions for all compounds. These results suggest a computational workflow to generate experimental-quality protein–ligand binding models, overcoming limitations of docking results due to receptor flexibility and incomplete sampling, as a useful starting point for the structure-based lead optimization of novel BRD4(BD1) inhibitors.

Abstract 775: Combinatorial compound stratification based on integration of LINCS, TCGA and PubChem data

Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA The purpose of this study is to leverage the large number of perturbation signatures from the NIH Library of Integrative Network-based Cellular Signatures (LINCS) and integrate them with patient data from The Cancer Genome Atlas (TCGA) and PubChem bioactivity data in an effort to prioritize compounds based on their synergistic effect in cancer treatment. From the large number of LINCS L1000 transcriptional data capturing cellular responses after chemical or genetic perturbations, we extracted gene expression signatures that were indicative of specific LINCS compounds. Moreover, we compared the L1000 transcriptional profiles with ones from TCGA in order to identify characteristic signatures of major cancer types and prioritized small molecule compounds with discordant expression profiles to those cancer types. We then linked the above compounds to protein target annotations and therefore produced a compound-specific protein target profile. For this, we utilized biochemical data produced by the LINCS KinomeScan and KiNative assays and also biochemical data obtained through PubChem. Using the above information, we obtained pairs of compounds that would inhibit unlinked gene sub-networks that were produced through processing of the TCGA transcriptional data. The above process can be used as a means to suggest compound combinations towards specific cancer types and to prioritize the development of compounds with targeted polypharmacology. Citation Format: Vasileios Stathias, Bryce Allen, Jennifer Clarke, Nagi G. Ayad, Stephan Schurer. Combinatorial compound stratification based on integration of LINCS, TCGA and PubChem data. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 775.

The Epigenetics of Medulloblastoma

Medulloblastoma is the most common malignant pediatric brain tumor arising in the cerebellum or medulla/brain stem. Despite recent treatment advances, approximately 40% of children experience tumor recurrence and 30% will die from the disease. Several recent studies have shown that various epigenetic enzymes are either mutated or overexpressed in medulloblastoma. Thus, these enzymes are considered drug targets in medulloblastoma. Similarly, small RNAs named microRNAs are attractive for therapeutic intervention since they control expression of medulloblastoma tumor suppressors or oncogenes. Interestingly, strategies to modulate epigenetic enzymes and microRNAs simultaneously may be particularly attractive for medulloblastoma treatment. We highlight that the knowledge of epigenetic enzymes, microRNAs, in addition to kinase and ubiquitin ligase networks is especially important for designing combination therapies in medulloblastoma.