313 research outputs found
Abstract 416: Identification of therapeutic combinations in glioblastoma using personalized gene expression networks
No full textAbstract
The goal of our study was to identify patient-specific gene expression networks from Glioblastoma Patient-Derived Xenografts (PDXs) and determine novel therapeutic compound combinations using those networks.
Glioblastoma is the most common malignant primary adult brain tumor with a standard of care consisting of maximal surgical resection followed by radiotherapy and adjuvant temozolomide (TMZ) chemotherapy. However, despite medical advances in the field, recurrence is almost universal, suggesting the need for more personalized and targeted therapeutic approaches.
For this, we obtained, transcriptional data from Glioblastoma PDXs and used them to identify their respective differentially expressed genes. Patient-specific gene expression networks were then created and their biological relevance was supplemented by integrating them with TCGA Glioblastoma transcriptional data. In order to identify compound combinations specific for those networks, we used the extensive chemical perturbation signatures from the Library of Network-based Cellular Signatures (LINCS). From the large number of L1000 transcriptional data we extracted gene expression signatures that were indicative of specific LINCS compounds. We then compared those signatures to the patient-specific networks in order to prioritize compound combinations that were inducing discordant transcriptional changes in distinct sub-networks of the PDXs transcriptome. The most orthogonal compound combinations were then chosen and used in in-vitro cell viability assays of Glioblastoma PDXs in order to evaluate their effectiveness. The above process can be used to prioritize compound combinations with potential therapeutic effect in a patient-specific manner.
Citation Format: Vasileios Stathias, Michele Forlin, Bryce Allen, Stephan Schürer, Nagi G. Ayad. Identification of therapeutic combinations in glioblastoma using personalized gene expression networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 416. doi:10.1158/1538-7445.AM2017-416</jats:p
Abstract A33: Discovery of novel anti-cancer therapeutic agents for Notch activation complex kinase (NACK) targeting the Notch pathway
No full textAbstract The Notch signaling pathway has been found to play an important role in multiple human cancers by regulating transcriptional programs. However, the mechanism by which Notch drives target gene transcription is still elusive. In our previous study, we have identified and characterized a novel Notch activation complex kinase, NACK, which acts as a Notch transcriptional co-activator and an essential regulator of Notch-mediated tumorigenesis and development. In this regard, NACK could become a putative drug target in anti-cancer therapies. The lack of three-dimensional (3D) structure of NACK hinders the designing of potential drug inhibitors. Therefore, computational methods are adopted to elucidate the structural and functional features of NACK, which further aid in designing new NACK inhibitors. Molecule docking (Glide) is utilized to obtain potential hit inhibitors for NACK, which will be validated using in-vitro and in-vivo assays. This will open avenues for the development of new therapies for Notch-dependent cancers. Citation Format: Xiaoxia Zhu, Zhiqiang Wang, Ke Jin, Luisana Astudillo, Wen Zhou, Jinshui Chen, Peter Buchwald, Stephan C. Schürer, Anthony J. Capobianco. Discovery of novel anti-cancer therapeutic agents for Notch activation complex kinase (NACK) targeting the Notch pathway. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr A33
Abstract 5103: The dark cancer kinome - untapped opportunities for the development of novel drugs
No full textAbstract Kinases are firmly established drug targets in cancer. There are currently 44 FDA approved kinase drug and hundreds of compounds are in clinical development. However, less than 10% of the Kinome is currently targeted and a large proportion is considered understudied by the NIH Illuminating the Druggable Genome Program (https://druggablegenome.net/). No small molecule inhibitors are known for these “dark” proteins, yet many may be opportune novel cancer targets.We developed a computational pipeline to identify and prioritize understudied kinases as cancer drug targets. We analyzed the complete set of tumors in The Cancer Genome Atlas (TCGA). For 33 different cancers we performed differential expression analysis and identified 39 dark kinases that exhibit significant upregulation in at least four types. Using co-expression analysis we built functional networks prioritizing drug targets. To identify small molecules that reverse their expression levels, we leveraged transcriptional response signatures obtained from dozens of human cancer cell lines exposed to tens of thousands of small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). To identify small molecules that directly bind to and inhibit dark kinases, we have have combined an advanced AI (artificial intelligence) model trained on activity data from across the Kinome with structure-based simulations.Using the computational pipeline, we identified the dark Ca2+/Calmodulin dependent kinase PNCK as the most differentially overexpressed kinase in kidney cancer patients. Our analyses have demonstrated statistically significant correlation between PNCK mRNA levels and various clinical and pathological outcomes, including histologic grade, clinical staging and overall survival. We have confirmed high levels of PNCK expression in 5 renal cell carcinoma cell lines (Caki-1, ACHN, 786-O, A704 and A498). Knockdown and overexpression studies have suggested PNCK and the CaMK pathway may contribute to cellular proliferation and cell cycle progression. We have applied our AI-based screening pipeline to a library of >20 million commercially available compounds and confirmed three PNCK inhibiting chemotypes. In summary, using a novel computational pipeline, we have identified and experimentally validated PNCK as a prospective novel drug target in an understudied pathway that is highly upregulated in kidney cancer. We identified first in class small molecules that target this previously dark kinase as prospective starting points for optimization into a clinical candidate. Citation Format: Derek J. Essegian, Rimpi Khurana, Vasileios Stathias, Valery Chavez, Jaime R. Merchan, Stephan Schürer. The dark cancer kinome - untapped opportunities for the development of novel drugs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5103
Korridor, Kabel und einige Kippmomente der kollektiven Imagination von Dominanz und Dienlichkeit
No full textDas Aufgehen der Dienlichkeit von Dienstboten, Zugehfrauen und vergleichbaren sozialen Rollen in Geräte ist primär eine Entwicklung des 20. Jahrhunderts. Doch die dabei vorausgesetzte kulturelle Tendenz, menschliche Agentenschaft an Dinge zu delegierest älter, wie Mark Jarzombek in seiner historischen Genese des Korridors zeigt. Er hebt heraus, wie die Agenden von menschlichen Handelnden, den Meldeläufern, zum architektonischen Programm einer spezialisierten Art von Zimmern werden: Ein Korridor verbindet, verteilt, grenzt ab und ermöglicht Kommunikation. Dabei wird die Dienlichkeit einer von Menschen erbrachten Dienstleistung teilweise von einer spezifischen räumlichen Konfiguration der Architektur übernommen. Indem bestimmte Attribute der Dienlichkeit von Mensch auf Zimmer übertragen werden, transformieren die kulturellen Programme beider
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Theoretical Insight into Mechanisms of Natural and Artificial Metalloproteases
No full textIn this study, theoretical and computational approaches have been utilized to investigate the mechanisms of natural and artificial metalloproteases. The active sites of most natural metalloproteases contain a tetrahedral zinc center, coordinated by three amino acid residues combinated from His(N), Cys(S), Glu(O), and Asp(O) with a water molecule as the fourth ligand. However, the roles played by the ligands environment in the catalytic functions of enzyme are not clear. In this study, the effects of different ligand combinations (NS2, N2S, N2O, N3, S3, NO2 and NSO) in the mechanism were investigated energy barriers were compared. The machanism and energetics of the substrate bound artificial metalloproteases Ni(II)cyclen (cyclen: 1,4,7,10-tetraazacyclododecane) and Cd(II)cyclen have been investigated. In addition, the mechanism of hydrolysis of Phe-Phe peptide bond catalyzed by another artificial metalloprotease [Pd(H2O)4]2+ has also been studied.</p
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Multimodal Data-Driven Computational Models for Kinase Inhibitor Discovery and Cellular Drug Response Prediction
No full textProtein kinase inhibitors have ushered in a paradigm shift in targeted cancer therapy and precision oncology. Despite their clinical success, resistance frequently arises through adaptive kinome reprogramming, in which cancer cells dynamically rewire signaling pathways to bypass inhibition. This, combined with the vast complexity of chemical space and cellular heterogeneity, necessitates scalable, data-driven strategies to identify compounds with desirable polypharmacological profiles and to predict therapeutic response across diverse biological contexts.Two conceptually aligned computational frameworks were developed to address these challenges. The first is a multi-task deep neural network trained on a harmonized, kinome-wide bioactivity dataset, enabling large-scale virtual screening, compound–target interaction profiling, and quantitative analysis of selectivity and polypharmacology. The second, DrugSSeq, is a multimodal deep learning framework that integrates compound molecular descriptors and gene expression profiles to predict drug sensitivity across diverse cancer types, supporting the prioritization of compounds in biologically responsive cellular contexts. To enable broad access to kinome-scale predictions, KNet, a web-based platform, was developed to deliver structure-informed predictions across the human kinome. When combined with molecular modeling tools such as AlphaFold and Schrödinger, these frameworks enable systematic exploration of kinase–ligand interactions across diverse scaffolds and both established and understudied targets. In summary, this work presents cohesive and extensible computational frameworks that span molecular and phenotypic levels of prediction, advance methodologies for kinase-targeted drug discovery and precision oncology, and deliver scalable tools to enable compound prioritization,support polypharmacology profiling, and inform the development of next-generation cancer therapies.</p
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Identifying Novel Therapeutic Agents in Castration-Resistant Prostate Cancer
No full textAndrogen deprivation therapy (ADT) is the standard-of-care treatment for non-localized or pre-metastatic advanced prostate cancer (PC). Over time, many new treatment options have become available for PC patients, however, these therapies only marginally extend patient survival and do not prevent the emergence of incurable castration-resistant prostate cancer (CRPC). A critical limitation in the prostate cancer field is the lack of effective targeted therapeutic options for patients due to frequent molecular adaptations driving drug resistance, aberrant AR reactivation, and constitutively active AR splice variants such as AR-V7. This thesis work focused on discovering new therapeutic agents against advanced prostate cancer through two distinct approaches: 1) Identifying serine/threonine kinase BUB1B as a critical regulator of CRPC and potential therapeutic vulnerability. 2) Developing an adaptable, streamlined, semi-automated drug screening protocol which permits users to simultaneously assess drug efficacy across the spectrum of the disease to rapidly identify cell-active therapies. To further validate BUB1B as a targetable CRPC vulnerability, we generated a stable BUB1B expression system which features three kinase deficient mutants (BUB1B D882N, BUB1B D911N, and BUB1B K795R) and wildtype BUB1B. With our in-house derived BUB1B expression system we demonstrated that BUB1B kinase activity facilitates CRPC proliferation and in collaboration demonstrated that the BUB1B kinase domain is a viable target to be exploited for future use in PC. Additionally, we demonstrated the utility of the drug screening protocol by validating the effectiveness of first-in-class BET and CBP/p300 dual inhibitor EP-31670 in ADPC and CRPC and testing ~50 predicted active compounds in a collaborative drug screening effort. Overall, this thesis work established the relevance of BUB1B kinase activity as a potential therapeutic target in CRPC and created a robust approach for identifying effective new therapeutic options for the treatment of patients with incurable CRPC. </p
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Combination Bromodomain and Kinase Inhibition Improves Therapeutic Efficacy in Glioblastoma
No full textGlioblastoma (GBM) is the most common and malignant adult brain tumor. Five- year survival following complete resection, chemotherapy, and radiotherapy is below 10%. Despite years of research, few advancements have been made in the treatment of GBM. Targeted therapies are largely ineffective due resistance pathways, including kinome reprogramming, which involves upregulation of kinases to activate alternative survival pathways. Kinome reprogramming is thought to underlie the resistance of cancers to BRD4 inhibitors, presenting a roadblock in the treatment of GBM.The active kinome of a panel of newly diagnosed and recurrent GBM patient- derived xenograft (PDX) tumors was measured using quantitative SILAC mass spectrometry with multiplexed inhibitor beads. Using this knowledge, we were able to make predictions of kinase inhibitors that synergize with BET inhibitors using our platform SynergySeq. The kinase targets were validated by measuring the BET-inhibitor induced kinome reprogramming in GBM PDX cells before and after treatment with a BRD4 inhibitor. We found that FGFR inhibitors were predicted to be synergistic, and the FGFR1 kinase was activated by BET inhibition. We found a significant synergistic effect of this combination therapy both in vitro and in vivo.By profiling the kinomes of GBM tumors and integrating these results with gene expression data, it may be possible to tailor treatments of this devastating disease using synergistic combinations of existing kinase and epigenetic target inhibitors.</p
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Large-scale Computational Screening and Machine Learning Approaches to Drug Discovery
No full textBiological information continues to grow exponentially fueled by massive data generation projects such as the Human Genome Project, The Cancer Genome Atlas (TCGA), and the Library of Integrated Network-based Cellular Signatures (LINCS). Unprecedented amounts and varieties of data (big data) have the potential to bring enormous scientific advances. Such data-driven research relies on advanced computational approaches for data integration and analysis. While bioinformatics encompasses many fields, the focus of my research has been to predict small molecule chemicals that interact with protein targets of interest and could, ultimately, become therapeutically useful drugs. Drug resistance in newly diagnosed tumors is often the major obstacle to the success of cancer chemotherapy. Understanding the molecular mechanisms underlying these conditions is necessary to develop therapeutic strategies that improve current clinical protocols. Heterogeneity in tumor cell populations challenges the efficacy of targeted therapeutics. However, research surrounding the understanding of adaptive cellular responses to targeted therapy has facilitated the development of combination therapies that disrupt these resistance mechanisms. We have developed new approaches to therapeutic discovery via molecular modeling and machine learning. This thesis presents an attempt to integrate biological and computational resources to discover novel therapeutic small molecules using ligand and structure-based modeling techniques. First, a general computational screening approach to identify novel multitarget kinase/bromodomain inhibitors from millions of commercially available small molecules is described. This pipeline identified eight novel BRD4 inhibitors, among them a first in class dual BRD4-EGFR inhibitor. To further characterize these compounds, I quantified their binding potential for BRD4 biochemically using an AlphaScreen assay and evaluated further improvements to our docking models by performing molecular dynamics (MD) simulations with those that displayed activity. Finally, to expand and improve the applicability and performance of my research to a more global predictive architecture, I applied multitask deep neural networks and single task learning methods to the problem of predicting ligand activity across the entire human kinome for which bioactivity information is available. I found that multitask deep learning improves enrichment of active compounds across all kinase targets, regardless of the amount of activity information and similarity between active kinase compounds. This research demonstrates that large-scale data-driven modeling approaches can result in novel small molecule discoveries and introduces a framework that can be utilized by the scientific community to improve computational screening and machine learning methodologies for drug discovery.</p
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Heterogeneity-Driven Therapeutic Resistance to Aurora Kinase Inhibition in Glioblastoma
No full textGlioblastoma (GBM) remains the most common and lethal adult primary brain cancer. Despite intensive research to identify vulnerabilities of this cancer, no new effective treatments have been identified in the last decade. The discovery of effective targeted therapies for GBM is complicated by complex inter- and intra-tumor heterogeneity, dynamic cellular plasticity, and the need for small molecules to cross the blood-brain-barrier. As of yet, all targeted therapies used as monotherapy to treat GBM have failed. Thus, the investigation of more effective targeted therapy combinations is essential. Aurora kinase and bromodomain inhibitor combinations are effective at attenuating tumor growth in vitro and in vivo, however the mechanism behind this observed synergy is unknown. I propose that small molecule inhibitors of Bromo- and Extra-Terminal (BET) Domain Containing Protein 4 (BRD4) may inhibit the intrinsic transcriptional plasticity of GBM cells, sensitizing them to aurora kinase inhibitors such as alisertib. Using an integrative pharmaco-transcriptomic approach, I have characterized the selective drug sensitivity of transcriptionally distinct GBM cells. Using in vivo orthotopic xenograft models of GBM, I’ve validated the sensitive and resistant cell identities to aurora kinase inhibition as predicted in silico. Further in vivo studies identify modulation of alisertib resistant cells by a novel brain-penetrant BET inhibitor, UM-002. Collectively, these studies provide a framework for combination therapy design with epigenetic inhibitors that can be applied to the fields of personalized medicine and drug resistance in GBM and have resulted in a novel computational tool termed ISOSCELES (Inferred Sensitivity Operating on the integration of Single-Cell Expression and L1000 Expression Signatures).</p
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