Getting ready for the European Health Data Space (EHDS): IDERHA's plan to align with the latest EHDS requirements for the secondary use of health data.

Objective: The European Health Data Space (EHDS) shapes the digital transformation of healthcare in Europe. The EHDS regulation will also accelerate the use of health data for research, innovation, policy-making, and regulatory activities for secondary use of data (known as EHDS2). The Integration of heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IDERHA) project builds one of the first pan-European health data spaces in alignment with the EHDS2 requirements, addressing lung cancer as a pilot. Methods: In this study, we conducted a comprehensive review of the EHDS regulation, technical requirements for EHDS2, and related projects. We also explored the results of the Joint Action Towards the European Health Data Space (TEHDAS) to identify the framework of IDERHA's alignment with EHDS2. We also conducted an internal webinar and an external workshop with EHDS experts to share expertise on the EHDS requirements and challenges. Results: We identified the lessons learned from the existing projects and the minimum-set of requirements for aligning IDERHA infrastructure with EHDS2, including user journey, concepts, terminologies, and standards. The IDERHA framework (i.e., platform architecture, standardization approaches, documentation, etc.) is being developed accordingly. Discussion: The IDERHA's alignment plan with EHDS2 necessitates the implementation of three categories of standardization for: data discoverability: Data Catalog Vocabulary (DCAT-AP), enabling semantics interoperability: Observational Medical Outcomes Partnership (OMOP), and health data exchange (DICOM and FHIR). The main challenge is that some standards are still being refined, e.g., the extension of the DCAT-AP (HealthDCAT-AP). Additionally, extensions to the Observational Health Data Sciences and Informatics (OHDSI) OMOP Common Data Model (CDM) to represent the patient-generated health data are still needed. Finally, proper mapping between standards (FHIR/OMOP) is a prerequisite for proper data exchange. Conclusions: The IDERHA's plan and our collaboration with other EHDS initiatives/projects are critical in advancing the implementation of EHDS2.

A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures.

Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.

CyFi-MAP: an interactive pathway-based resource for cystic fibrosis.

Cystic fibrosis (CF) is a life-threatening autosomal recessive disease caused by more than 2100 mutations in the CF transmembrane conductance regulator (CFTR) gene, generating variability in disease severity among individuals with CF sharing the same CFTR genotype. Systems biology can assist in the collection and visualization of CF data to extract additional biological significance and find novel therapeutic targets. Here, we present the CyFi-MAP-a disease map repository of CFTR molecular mechanisms and pathways involved in CF. Specifically, we represented the wild-type (wt-CFTR) and the F508del associated processes (F508del-CFTR) in separate submaps, with pathways related to protein biosynthesis, endoplasmic reticulum retention, export, activation/inactivation of channel function, and recycling/degradation after endocytosis. CyFi-MAP is an open-access resource with specific, curated and continuously updated information on CFTR-related pathways available online at https://cysticfibrosismap.github.io/ . This tool was developed as a reference CF pathway data repository to be continuously updated and used worldwide in CF research.

Author Correction: Introducing PIONEER: a project to harness big data in prostate cancer research.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Introducing PIONEER: a project to harness big data in prostate cancer research.

Prostate Cancer Diagnosis and Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer, consisting of 32 private and public stakeholders from 9 countries across Europe. Launched by the Innovative Medicines Initiative 2 and part of the Big Data for Better Outcomes Programme (BD4BO), the overarching goal of PIONEER is to provide high-quality evidence on prostate cancer management by unlocking the potential of big data. The project has identified critical evidence gaps in prostate cancer care, via a detailed prioritization exercise including all key stakeholders. By standardizing and integrating existing high-quality and multidisciplinary data sources from patients with prostate cancer across different stages of the disease, the resulting big data will be assembled into a single innovative data platform for research. Based on a unique set of methodologies, PIONEER aims to advance the field of prostate cancer care with a particular focus on improving prostate-cancer-related outcomes, health system efficiency by streamlining patient management, and the quality of health and social care delivered to all men with prostate cancer and their families worldwide.

cd2sbgnml: bidirectional conversion between CellDesigner and SBGN formats.

MOTIVATION: CellDesigner is a well-established biological map editor used in many large-scale scientific efforts. However, the interoperability between the Systems Biology Graphical Notation (SBGN) Markup Language (SBGN-ML) and the CellDesigner's proprietary Systems Biology Markup Language (SBML) extension formats remains a challenge due to the proprietary extensions used in CellDesigner files. RESULTS: We introduce a library named cd2sbgnml and an associated web service for bidirectional conversion between CellDesigner's proprietary SBML extension and SBGN-ML formats. We discuss the functionality of the cd2sbgnml converter, which was successfully used for the translation of comprehensive large-scale diagrams such as the RECON Human Metabolic network and the complete Atlas of Cancer Signalling Network, from the CellDesigner file format into SBGN-ML. AVAILABILITY AND IMPLEMENTATION: The cd2sbgnml conversion library and the web service were developed in Java, and distributed under the GNU Lesser General Public License v3.0. The sources along with a set of examples are available on GitHub (https://github.com/sbgn/cd2sbgnml and https://github.com/sbgn/cd2sbgnml-webservice, respectively). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer.

The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis. We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.