Vasileios Stathias

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.

Drug target ontology to classify and integrate drug discovery data

BackgroundOne of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.ResultsAs part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.ConclusionsDTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/, Github (http://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (http://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Identifying glioblastoma gene networks based on hypergeometric test analysis

Patient specific therapy is emerging as an important possibility for many cancer patients. However, to identify such therapies it is essential to determine the genomic and transcriptional alterations present in one tumor relative to control samples. This presents a challenge since use of a single sample precludes many standard statistical analysis techniques. We reasoned that one means of addressing this issue is by comparing transcriptional changes in one tumor with those observed in a large cohort of patients analyzed by The Cancer Genome Atlas (TCGA). To test this directly, we devised a bioinformatics pipeline to identify differentially expressed genes in tumors resected from patients suffering from the most common malignant adult brain tumor, glioblastoma (GBM). We performed RNA sequencing on tumors from individual GBM patients and filtered the results through the TCGA database in order to identify possible gene networks that are overrepresented in GBM samples relative to controls. Importantly, we demonstrate that hypergeometric-based analysis of gene pairs identifies gene networks that validate experimentally. These studies identify a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM and other cancers.

Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses.

BackgroundAiming to understand cellular responses to different perturbations, the NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many institutes and laboratories working on over a thousand cell lines. The community-based Cell Line Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integration.ResultsCLO has consistently represented all 1097 LINCS cell lines and included information extracted from the LINCS Data Portal and ChEMBL. Using MCF 10A cell line cells as an example, we demonstrated how to ontologically model LINCS cellular signatures such as their non-tumorigenic epithelial cell type, three-dimensional growth, latrunculin-A-induced actin depolymerization and apoptosis, and cell line transfection. A CLO subset view of LINCS cell lines, named LINCS-CLOview, was generated to support systematic LINCS cell line analysis and queries. In summary, LINCS cell lines are currently associated with 43 cell types, 131 tissues and organs, and 121 cancer types. The LINCS-CLO view information can be queried using SPARQL scripts.ConclusionsCLO was used to support ontological representation, integration, and analysis of over a thousand LINCS cell line cells and their cellular responses.

Sustainable data and metadata management at the BD2K-LINCS Data Coordination and Integration Center

The NIH-funded LINCS Consortium is creating an extensive reference library of cell-based perturbation response signatures and sophisticated informatics tools incorporating a large number of perturbagens, model systems, and assays. To date, more than 350 datasets have been generated including transcriptomics, proteomics, epigenomics, cell phenotype and competitive binding profiling assays. The large volume and variety of data necessitate rigorous data standards and effective data management including modular data processing pipelines and end-user interfaces to facilitate accurate and reliable data exchange, curation, validation, standardization, aggregation, integration, and end user access. Deep metadata annotations and the use of qualified data standards enable integration with many external resources. Here we describe the end-to-end data processing and management at the DCIC to generate a high-quality and persistent product. Our data management and stewardship solutions enable a functioning Consortium and make LINCS a valuable scientific resource that aligns with big data initiatives such as the BD2K NIH Program and concords with emerging data science best practices including the findable, accessible, interoperable, and reusable (FAIR) principles.

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.

Abstract 65: An integrated bioinformatics approach for identifying patient-specific gene networks and novel therapeutic targets in glioblastoma

The purpose of our approach is to identify highly relevant and patient specific gene networks from next generation sequencing data and use the resulting gene combinations to identify potential novel therapeutic targets. Glioblastoma multiforme (GBM) is the most common and aggressive malignant brain tumor. Despite medical advances in the field, the median survival time is still below 2 years since recurrence is nearly universal. Thus, the discovery of novel and specific molecular targets is needed. As with all cancers, GBM arises due to complex alterations in a patient9s genome. Identifying patient-specific differentially expressed genes (DEGs) though, can impose many difficulties given the discordance of different analysis methods when the sample size is low. We used the data on the large number of GBM patients within TCGA (The Cancer Genome Atlas) as filter for identifying variations in expression. With the help of our algorithm, we produced enriched single patient RNAseq DEG lists and we calculated a hypergeometric probability and a correlation coefficient for every gene pair on these lists. By using the most significant of these pairs, we generated gene association networks. To further validate the biological relevance of the gene pairs, we looked into the presence of these networks in other types of cancer and searched for published experimental data supporting the connections of our networks. In order to assess the therapeutic potential of these genes, we used complimentary experiments involving GBM cell lines recorded through the LINCS (Library of Integrated Network-based Cell Signatures) database. This study identifies a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM, and capitalizes on the LINCS database to assess the potential of novel therapeutic targets. Citation Format: Vasileios Stathias, Chiara Pastori, Ricardo Komotar, Ming Zhang, Stephan Schurer, Jennifer Clarke, Nagi G. Ayad. An integrated bioinformatics approach for identifying patient-specific gene networks and novel therapeutic targets in glioblastoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 65. doi:10.1158/1538-7445.AM2015-65