ObjectivesWe examined major issues associated with sharing of individual clinical trial data and developed a consensus document on providing access to individual participant data from clinical trials, using a broad interdisciplinary approach.Design and methodsThis was a consensus-building process among the members of a multistakeholder task force, involving a wide range of experts (researchers, patient representatives, methodologists, information technology experts, and representatives from funders, infrastructures and standards development organisations). An independent facilitator supported the process using the nominal group technique. The consensus was reached in a series of three workshops held over 1 year, supported by exchange of documents and teleconferences within focused subgroups when needed. This work was set within the Horizon 2020-funded project CORBEL (Coordinated Research Infrastructures Building Enduring Life-science Services) and coordinated by the European Clinical Research Infrastructure Network. Thus, the focus was on non-commercial trials and the perspective mainly European.OutcomeWe developed principles and practical recommendations on how to share data from clinical trials.ResultsThe task force reached consensus on 10 principles and 50 recommendations, representing the fundamental requirements of any framework used for the sharing of clinical trials data. The document covers the following main areas: making data sharing a reality (eg, cultural change, academic incentives, funding), consent for data sharing, protection of trial participants (eg, de-identification), data standards, rights, types and management of access (eg, data request and access models), data management and repositories, discoverability, and metadata.ConclusionsThe adoption of the recommendations in this document would help to promote and support data sharing and reuse among researchers, adequately inform trial participants and protect their rights, and provide effective and efficient systems for preparing, storing and accessing data. The recommendations now need to be implemented and tested in practice. Further work needs to be done to integrate these proposals with those from other geographical areas and other academic domains.

Mining of integrated public transcriptomic and ChIP-Seq (cistromic) datasets can illuminate functions of mammalian cellular signaling pathways not yet explored in the research literature. Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates community classifications of signaling pathway nodes (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules. We then mapped over 10,000 public transcriptomic or cistromic experiments to their pathway node or biosample of study. To enable prediction of pathway node-gene target transcriptional regulatory relationships through SPP, we generated consensus ‘omics signatures, or consensomes, which ranked genes based on measures of their significant differential expression or promoter occupancy across transcriptomic or cistromic experiments mapped to a specific node family. Consensomes were validated using alignment with canonical literature knowledge, gene target-level integration of transcriptomic and cistromic data points, and in bench experiments confirming previously uncharacterized node-gene target regulatory relationships. To expose the SPP knowledgebase to researchers, a web browser interface was designed that accommodates numerous routine data mining strategies. SPP is freely accessible at https://www.signalingpathways.org.

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Integrated mining of public transcriptomic and ChIP-Seq datasets has the potential to illuminate facets of mammalian cellular signaling pathways not yet explored in the research literature.Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates stable community classifications of the four major categories of signaling pathway node (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules (BSMs). We then mapped over 10,000 public transcriptomic or cistromic experiments to their relevant signaling pathway node, BSM or biosample of study. To provide for prediction of pathway node-target transcriptional regulatory relationships, we generated consensus 'omics signatures, or consensomes, based on measures of significant differential expression of genomic targets across all underlying transcriptomic experiments. To expose the SPP knowledgebase to researchers, a web browser interface accommodates a variety of routine data mining strategies. Consensomes were validated using alignment with literature-based knowledge, gene target-level integration of transcriptomic and ChIP-Seq data points, and in bench experiments that confirmed previously uncharacterized node-gene target regulatory relationships. SPP is freely accessible at https://beta.signalingpathways.org.Individual dataset pages enable integration of SPP with the research literature via digital object identifier (DOI)-driven links from external sites, as well as for citation of datasets to enhance their FAIR status 3,4 .

C-reactive protein (CRP) is an acute phase reactant protein considered to be the prototypic marker for inflammation and its associated diseases. However, little is known about how CRP affects the immune system. In this study, we investigated the effect of CRP on dendritic cell (DC) differentiation, activation and biological functions. CD14 + monocytes were purified from PBMC and differentiated into DC in vitro. CRP (10 lg/ mL) substantially down-regulated expression of DC-SIGN (CD209) and the costimulatory molecules CD40 and CD86 during DC differentiation. This inhibitory effect was more pronounced when CRP was added at the early stage (0-2 days) of DC differentiation. The inhibitory effect of CRP could be specifically blocked by an anti-CD32 Ab. In addition, CRP dramatically down-regulated expression of the antigenuptake molecules CD205 and CD206, resulting in reduced DC endocytosis. Furthermore, CRP down-regulated expression of the costimulatory molecules CD40, CD80 and CD86 as well as the DC maturation marker CD83 after lipopolysaccharideinduced DC maturation. CRP-treated DC also showed an inhibitory effect on allogeneic T cell proliferation in a mixed leukocyte reaction. CRP treatment of activated DC preferentially decreased production of the proinflammatory and inflammatory cytokines IL-6, IL-8, IL-12, TNF-a, MIP-1a, MIP-1b and MCP-1. This work reveals a new role for CRP in modulating the immune system by inhibiting DC differentiation, maturation and functions mainly through FccRIIa/CD32.

The benefits of reusing EHR data for clinical research studies are numerous. They portend the opportunity to bring new therapies to patients sooner, potentially at a lower cost, and to accelerate learning health cycles—through faster data acquisition in clinical research studies. Metrics have proven that time can be saved, workflow and processes streamlined, and data quality increased significantly. Pilot projects and now actual investigational trials used for regulatory submissions have shown that these benefits support the transformation of clinical research by leveraging EHRs for research. Panelists at a recent collaborative focused on bridging clinical research and clinical care offered varying perspectives on how the latest standards and technologies could be leveraged to facilitate data transfer from EHR systems into clinical research databases, as well as the associated improvements in data quality. Panelists also discussed other avenues to leverage EHR in clinical research. Improvements and exciting possibilities notwithstanding, much work remains. Data ownership and access, attention to metadata and structured data for data sharing, and broader adoption of global standards are key areas for collaboration. With the steady increase in adoption of EHRs around the world, this is an excellent time for all stakeholders to work together and create an environment such that EHRs can be used more readily for research. The capacity for research can thus be increased to provide more high‐quality information that will contribute to rapid continuous learning health systems from which all patients can benefit.

Interest in real-world data (RWD) and real-world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third-party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single-arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness "big" RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient-derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.

The nuclear receptor (NR) superfamily of ligand-regulated transcription factors directs ligand- and tissue-specific transcriptomes in myriad developmental, metabolic, immunological, and reproductive processes. The NR signaling field has generated a wealth of genome-wide expression data points, but due to deficits in their accessibility, annotation, and integration, the full potential of these studies has not yet been realized. We searched public gene expression databases and MEDLINE for global transcriptomic datasets relevant to NRs, their ligands, and coregulators. We carried out extensive, deep reannotation of the datasets using controlled vocabularies for RNA Source and regulating molecule and resolved disparate gene identifiers to official gene symbols to facilitate comparison of fold changes and their significance across multiple datasets. We assembled these data points into a database, Transcriptomine (http://www.nursa.org/transcriptomine), that allows for multiple, menu-driven querying strategies of this transcriptomic "superdataset," including single and multiple genes, Gene Ontology terms, disease terms, and uploaded custom gene lists. Experimental variables such as regulating molecule, RNA Source, as well as fold-change and P value cutoff values can be modified, and full data records can be either browsed or downloaded for downstream analysis. We demonstrate the utility of Transcriptomine as a hypothesis generation and validation tool using in silico and experimental use cases. Our resource empowers users to instantly and routinely mine the collective biology of millions of previously disparate transcriptomic data points. By incorporating future transcriptome-wide datasets in the NR signaling field, we anticipate Transcriptomine developing into a powerful resource for the NR- and other signal transduction research communities.