Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates.

BACKGROUND: Lung adenocarcinoma is the major cause of cancer-related deaths in the world. Given this, the importance of research on its pathophysiology and therapy remains a key health issue. To assist in this endeavor, recent oncology studies are adopting Systems Biology approaches and bioinformatics to analyze and understand omics data, bringing new insights about this disease and its treatment. METHODS: We used reverse engineering of transcriptomic data to reconstruct nontumorous lung reference networks, focusing on transcription factors (TFs) and their inferred target genes, referred as regulatory units or regulons. Afterwards, we used 13 case-control studies to identify TFs acting as master regulators of the disease and their regulatory units. Furthermore, the inferred activation patterns of regulons were used to evaluate patient survival and search drug candidates for repositioning. RESULTS: The regulatory units under the influence of ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6, and UHRF1 transcription factors were consistently associated with the pathological phenotype, suggesting that they may be master regulators of lung adenocarcinoma. We also observed that the inferred activity of FOXA2, FOXM1, and UHRF1 was significantly associated with risk of death in patients. Finally, we obtained deptropine, promazine, valproic acid, azacyclonol, methotrexate, and ChemBridge ID compound 5109870 as potential candidates to revert the molecular profile leading to decreased survival. CONCLUSION: Using an integrated transcriptomics approach, we identified master regulator candidates involved with the development and prognostic of lung adenocarcinoma, as well as potential drugs for repurposing.

Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning.

Drug discovery is a very expensive and time-consuming endeavor. Fortunately, recent omics technologies and Systems Biology approaches introduced interesting new tools to achieve this task, facilitating the repurposing of already known drugs to new therapeutic assignments using gene expression data and bioinformatics. The inherent role of transcription factors in gene expression modulation makes them strong candidates for master regulators of phenotypic transitions. However, transcription factors expression itself usually does not reflect its activity changes due to post-transcriptional modifications and other complications. In this aspect, the use of high-throughput transcriptomic data may be employed to infer transcription factors-targets interactions and assess their activity through co-expression networks, which can be further used to search for drugs capable of reverting the gene expression profile of pathological phenotypes employing the connectivity maps paradigm. Following this idea, we argue that a module-oriented connectivity map approach using transcription factors-centered networks would aid the query for new repositioning candidates. Through a brief case study, we explored this idea in bipolar disorder, retrieving known drugs used in the usual clinical scenario as well as new candidates with potential therapeutic application in this disease. Indeed, the results of the case study indicate just how promising our approach may be to drug repositioning.

Revisiting the Therapeutic Potential of Bothrops jararaca Venom: Screening for Novel Activities Using Connectivity Mapping.

Snake venoms are sources of molecules with proven and potential therapeutic applications. However, most activities assayed in venoms (or their components) are of hemorrhagic, hypotensive, edematogenic, neurotoxic or myotoxic natures. Thus, other relevant activities might remain unknown. Using functional genomics coupled to the connectivity map (C-map) approach, we undertook a wide range indirect search for biological activities within the venom of the South American pit viper Bothrops jararaca. For that effect, venom was incubated with human breast adenocarcinoma cell line (MCF7) followed by RNA extraction and gene expression analysis. A list of 90 differentially expressed genes was submitted to biosimilar drug discovery based on pattern recognition. Among the 100 highest-ranked positively correlated drugs, only the antihypertensive, antimicrobial (both antibiotic and antiparasitic), and antitumor classes had been previously reported for B. jararaca venom. The majority of drug classes identified were related to (1) antimicrobial activity; (2) treatment of neuropsychiatric illnesses (Parkinson's disease, schizophrenia, depression, and epilepsy); (3) treatment of cardiovascular diseases, and (4) anti-inflammatory action. The C-map results also indicated that B. jararaca venom may have components that target G-protein-coupled receptors (muscarinic, serotonergic, histaminergic, dopaminergic, GABA, and adrenergic) and ion channels. Although validation experiments are still necessary, the C-map correlation to drugs with activities previously linked to snake venoms supports the efficacy of this strategy as a broad-spectrum approach for biological activity screening, and rekindles the snake venom-based search for new therapeutic agents.

Using Pharmacogenomic Databases for Discovering Patient-Target Genes and Small Molecule Candidates to Cancer Therapy.

With multiple omics strategies being applied to several cancer genomics projects, researchers have the opportunity to develop a rational planning of targeted cancer therapy. The investigation of such numerous and diverse pharmacogenomic datasets is a complex task. It requires biological knowledge and skills on a set of tools to accurately predict signaling network and clinical outcomes. Herein, we describe Web-based in silico approaches user friendly for exploring integrative studies on cancer biology and pharmacogenomics. We briefly explain how to submit a query to cancer genome databases to predict which genes are significantly altered across several types of cancers using CBioPortal. Moreover, we describe how to identify clinically available drugs and potential small molecules for gene targeting using CellMiner. We also show how to generate a gene signature and compare gene expression profiles to investigate the complex biology behind drug response using Connectivity Map. Furthermore, we discuss on-going challenges, limitations and new directions to integrate molecular, biological and epidemiological information from oncogenomics platforms to create hypothesis-driven projects. Finally, we discuss the use of Patient-Derived Xenografts models (PDXs) for drug profiling in vivo assay. These platforms and approaches are a rational way to predict patient-targeted therapy response and to develop clinically relevant small molecules drugs.