CancerResource - updated database of cancer-relevant proteins, mutations and interacting drugs

Here, we present an updated version of CancerResource, freely available without registration at http://bioinformatics.charite.de/care. With upcoming information on target expression and mutations in patients’ tumors, the need for systems supporting decisions on individual therapy is growing. This knowledge is based on numerous, experimentally validated drug-target interactions and supporting analyses such as measuring changes in gene expression using microarrays and HTS-efforts on cell lines. To enable a better overview about similar drug-target data and supporting information, a series of novel information connections are established and made available as described in the following. CancerResource contains about 91 000 drug-target relations, more than 2000 cancer cell lines and drug sensitivity data for about 50 000 drugs. CancerResource enables the capability of uploading external expression and mutation data and comparing them to the database's cell lines. Target genes and compounds are projected onto cancer-related pathways to get a better overview about how drug-target interactions benefit the treatment of cancer. Features like cellular fingerprints comprising of mutations, expression values and drug-sensitivity data can promote the understanding of genotype to drug sensitivity associations. Ultimately, these profiles can also be used to determine the most effective drug treatment for a cancer cell line most similar to a patient's tumor cells

GDA, a web-based tool for Genomics and Drugs integrated analysis

Several major screenings of genetic profiling and drug testing in cancer cell lines proved that the integration of genomic portraits and compound activities is effective in discovering new genetic markers of drug sensitivity and clinically relevant anticancer compounds. Despite most genetic and drug response data are publicly available, the availability of user-friendly tools for their integrative analysis remains limited, thus hampering an effective exploitation of this information. Here, we present GDA, a web-based tool for Genomics and Drugs integrated Analysis that combines drug response data for >50 800 compounds with mutations and gene expression profiles across 73 cancer cell lines. Genomic and pharmacological data are integrated through a modular architecture that allows users to identify compounds active towards cancer cell lines bearing a specific genomic background and, conversely, the mutational or transcriptional status of cells responding or not-responding to a specific compound. Results are presented through intuitive graphical representations and supplemented with information obtained from public repositories. As both personalized targeted therapies and drug-repurposing are gaining increasing attention, GDA represents a resource to formulate hypotheses on the interplay between genomic traits and drug response in cancer. GDA is freely available at http://gda.unimore.it/

canSAR: an integrated cancer public translational research and drug discovery resource

canSAR is a fully integrated cancer research and drug discovery resource developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface at http://cansar.icr.ac.uk provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects

Literature mining and network analysis in Biology

Η παρούσα διπλωματική παρουσιάζει το OnTheFly2.0, ένα διαδικτυακό εργαλείο που επικεντρώνεται στην εξαγωγή και επακόλουθη ανάλυση βιοϊατρικών όρων από μεμονωμένα αρχεία. Συγκεκριμένα, το OnTheFly2.0 υποστηρίζει πολλούς διαφορετικούς επιτρέποντας τον παράλληλο χειρισμό τους. Μέσω της ενσωμάτωσης της υπηρεσίας EXTRACT υλοποιείται η Αναγνώριση Ονοματικών Οντοτήτων (Named Entity Recognition) για γονίδια/πρωτεΐνες, χημικές ουσίες, οργανισμούς, ιστούς, περιβάλλοντα, ασθένειες, φαινοτύπους και όρους οντολογίας γονιδίων (Gene Ontology terms), καθώς και η δημιουργία αναδυόμενων παραθύρων που παρέχουν πληροφορίες για τον αναγνωρισμένο όρο, συνοδευόμενες από σύνδεσμο για διάφορες βάσεις δεδομένων. Οι αναγνωρισμένες πρωτεΐνες, τα γονίδια και οι χημικές ουσίες μπορούν να επεξεργαστούν περαιτέρω μέσω αναλύσεων εμπλουτισμού για τη λειτουργικότητα και τη βιβλιογραφία ή να συσχετιστούν με ασθένειες και πρωτεϊνικές δομές. Τέλος, είναι δυνατή η απεικόνιση αλληλεπιδράσεων μεταξύ πρωτεϊνών ή μεταξύ πρωτεϊνών και χημικών ουσιών μέσω της δημιουργίας διαδραστικών δικτύων από τις βάσεις STRING και STITCH αντίστοιχα. Το OnTheFly2.0 υποστηρίζει 197 διαφορετικά είδη οργανισμών και είναι διαθέσιμο στον παρακάτω σύνδεσμο: http://onthefly.pavlopouloslab.info.The particular thesis presents OnTheFly2.0, a web-based, versatile tool dedicated to the extraction and subsequent analysis of biomedical terms from individual files. More specifically, OnTheFly2.0 supports different file formats, enabling simultaneous file handling. The integration of the EXTRACT tagging service allows the implementation of Named Entity Recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and Gene Ontology terms, as well as the generation of popup windows which provide concise, context related information about the identified term, accompanied by links to various databases. Once named entities, such as proteins, genes and chemicals are identified, they can be further explored via functional and publication enrichment analysis or be associated with diseases and protein domains reporting from protein family databases. Finally, visualization of protein-protein and protein-chemical associations is possible through the generation of interactive networks from the STRING and STITCH services, respectively. OnTheFly2.0 currently supports 197 species and is available at http://onthefly.pavlopouloslab.info

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro-in vivo -clinical data recorded and suitability for modelling, read-across or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data

APPROACH TO EVALUATE THE RISK OF CANCER FOR DIFFERENT NUMBER OF TUMOR SUPPRESSOR GENES IN THE INDIVIDUAL

Significant progress in understanding the mechanisms of carcinogenesis is associated with the discovery of tumor suppressor genes (or antioncogenes). It is known that in the human population, the number of normally functioning suppressor genes varies in different individuals at birth. The aim of the study was to assess the probability of cancer development in an individual with a different initial number of undamaged normally functioning antioncogenes. On the basis of the probabilistic mathematical model of carcinogenesis, the most probable age of cancer development depending on the number of intact antioncogenes was assessed. As a result of the studies, the probability of cancer development depending on the age of the patient is estimated. The dependence of the probability of cancer development in an individual on the number of undamaged antioncogenes is also investigated. The analysis of the significance of the number of tumor suppressor genes, the damage of which may be the cause of initiation of cell malignancy, has been carried out

High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome

Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-Drug Interaction Database (PDID) that includes the putative drug targets generated by these three methods for the entire structural human proteome. To address the fact that only a limited set of proteins has known structures, we study the similarity-based methods that do not require this information. We review a comprehensive set of 35 high-impact similarity-based predictors and develop a novel, high-quality benchmark database. We group these predictors based on three types of similarities and their combinations that they use. We discuss and compare key architectural aspects of these methods including their source databases, internal databases and predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually or in all possible combinations. We assess predictive quality at the database-wide drug-protein interaction level and we are the first to also include evaluation across individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures AUC of 0.93. We offer a first-of-its-kind analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets

Light-Responsive Micelles Loaded With Doxorubicin for Osteosarcoma Suppression

The enhancement of tumor targeting and cellular uptake of drugs are significant factors in maximizing anticancer therapy and minimizing the side effects of chemotherapeutic drugs. A key challenge remains to explore stimulus-responsive polymeric nanoparticles to achieve efficient drug delivery. In this study, doxorubicin conjugated polymer (Poly-Dox) with light-responsiveness was synthesized, which can self-assemble to form polymeric micelles (Poly-Dox-M) in water. As an inert structure, the polyethylene glycol (PEG) can shield the adsorption of protein and avoid becoming a protein crown in the blood circulation, improving the tumor targeting of drugs and reducing the cardiotoxicity of doxorubicin (Dox). Besides, after ultraviolet irradiation, the amide bond connecting Dox with PEG can be broken, which induced the responsive detachment of PEG and enhanced cellular uptake of Dox. Notably, the results of immunohistochemistry in vivo showed that Poly-Dox-M had no significant damage to normal organs. Meanwhile, they showed efficient tumor-suppressive effects. This nano-delivery system with the light-responsive feature might hold great promises for the targeted therapy for osteosarcoma