RAD51predict, implementation of a multi-centric research platform for federated data governance

RAD51predict is a research project that evaluates RAD51 test as a predictive tool to improve diagnosis, classify tumors and help guide treatment decision-making. Genomcore provides a secure and compliant federated data repository to store and analyze multimodal datasets within the project.

RAD51predict, implementation of a multi-centric research platform for federated data governance

RAD51predict is a research project that evaluates RAD51 test as a predictive tool to improve diagnosis, classify tumors and help guide treatment decision-making. Genomcore provides a secure and compliant federated data repository to store and analyze multimodal datasets within the project.


To create a centralized data repository that ensures standardized metadata and data collection, facilitates intra-consortia information sharing, and provides the tools and resources needed to support data discovery, exploitation and reuse. 


A GDPR and HIPAA compliant platform for the management, standardization and integration of multiple data origins under a federated framework for the RAD51 international consortia.

Conclusion in numbers

Our multimodal unified datastore enabled cross-institutional collaboration and information exchange between the clinical and research domains. As a result, RAD51 test efficacy is being evaluated in an ongoing clinical trial.

About VHIO

Founded in 2006, the Vall d’Hebron Institute of Oncology (VHIO) is a reference center of excellence for personalized medicine in oncology. Thanks to its pioneering model of multidisciplinary and translational research and its participation in consortia and projects with other prestigious centers around the world, it has positioned itself as one of the most important comprehensive centers in Europe capable of transforming, in record time, the latest research discoveries in the laboratory to clinical practice. VHIO is dedicated to delivering on the promise of ‘precision’ medicine in oncology – turning cancer discovery into more effective treatments and better practice for the care of our patients. VHIO collaborates closely with the Vall d”Hebron University Hospital’s Oncology Department, driven by a shared determination to both advance and accelerate personlized and targeted therapies against cancer. 

About the project

Not all cancers respond to treatments in the same way. For this reason, it is essential to have tools that make it possible to differentiate each cancer subtype and thus guide physicians in choosing the best therapy for each patient. This is the ultimate goal of RAD51predict project, led by Dr. Violeta Serra, head of the Experimental Therapeutics Group at the Vall d’Hebron Institute of Oncology (VHIO). Tumours with DNA repair defects, such as those from BRCA1/BRCA2 mutation carriers, respond very well to certain chemotherapies and to new targeted drugs named PARP inhibitors. Dr. Serra’s team has developed a new test based on the DNA repair protein RAD51 that makes it possible to determine if tumour cells have DNA repair defects. This project aims to establish the predictive value of the RAD51 test in four major cancer types – that is breast, ovarian, prostate and endometrial – to diagnose and classify tumours and help choose the most appropriate treatment.

VHIO counts with the collaboration of several international reference institutions such as the University Clinic of Giessen and Marburg (Germany), Institut Gustave Roussy – INSERM (France), the German Breast Group Forschungs GmbH (Germany) and the Université Laval (CHUQ, Canada). The project is funded by the 2019 ERA PerMed Joint Transnational Call under the ERA-NET Cofund scheme of the Horizon 2020 Research and Innovation Framework Programme of the European Commission Research, along with the support from the Spanish Association Against Cancer (AECC) and La Caixa Foundation.


The idea of a lone scientist or research team working in isolation is no longer accurate in today’s world of scientific research, especially in the life sciences field. The trend is towards large, international consortia to organize large-scale, multi-disciplinary projects, reflecting the shift towards more data-intensive approaches. Typically, these consortia operate through fixed-term contracts and employ strict governance frameworks that pose specific problems regarding public and private participant involvement, data sharing, harmonization and transparency, and ‘legacy plans’ or what happens to data after the project is finished.

Decentralizing, democratizing, and productizing data is a transformative approach in data architecture that unleashes immense potential for experimentation and innovation. The key to success lies in utilizing a federated data governance approach, which strikes a delicate balance between decentralization of data sources – allowing for innovation on a large scale – and centralization of data governance – ensuring consistency and collaboration across the consortia.

However, implementing a federated data governance framework can present a number of challenges. First, there may be differences in the ways that each institution manages and stores their data, which can create difficulties when trying to integrate and harmonize data across different sources. For this it is crucial to set up data formats, definitions and metadata standards in advance as well as ensure data security and privacy across all datasets. In addition, data will be accessed and analyzed across different institutions, which may have varying levels of technical expertise and access to resources. This may require the development of common data governance policies and procedures, as well as the establishment of data sharing agreements and protocols to ensure that data is used ethically and in compliance with legal and regulatory requirements.

Adherence to the FAIR principles of data management – which require that data is Findable, Accessible, Interoperable, and Reusable – presents another challenge. The Horizon 2020 Framework is dedicated to enhancing and maximizing access to and re-use of research data generated by funded projects. To achieve this goal, the framework has implemented strict guidelines to ensure that the research data is FAIR. This may involve the development of standardized metadata and data sharing protocols, as well as the creation of tools and resources to support data discovery and reuse. Additionally, ensuring that data is accessible and can be used by a wide range of users requires the adoption of new technologies and platforms with analysis tools under cloud-based data storage.


To help implement a federated data governance approach for RAD51predict project, Genomcore provided its multimodal platform especially designed for managing collaborative research projectsThe platform offers a GDPR and HIPAA-compliant framework for the store, management, standardization and integration of all kinds of data. This centralized data storage and analysis environment in the cloud is accessible from anywhere and can be adapted to the unique needs of each project so that participants can share data, metadata, analysis tools, and results, facilitating seamless collaboration and unlocking the full potential of research data.

Genomcore’s platform provides an intrinsic metadata system to ensure data is organized and structured effectively. This enables customized data models to be defined for each type of data, such as clinical and personal data, genomic data or the details of a laboratory experiment. Once the templates are defined, data can be recorded either manually through the web interface or programmatically using an API that is specifically designed for this purpose. This allows for quick integrations with databases and other data sources, such as spreadsheets or tabbed files.

The pseudo-anonymized data –being primary data and data resulting from the project analyses– is stored in the platform and is made accessible only to project partners via controlled user access. The Genomcore platform provides a flexible database model to ensure segregation of information blocks -interoperable data structures- that allow defining granular permissions on different levels of data access avoiding unforeseen usage or disclosure to non-authorized partners.

We are certified with ISO/IEC standards 27001:2013, 27017:2020 and 27018:2020, ensuring compliance with applicable data protection laws and regulations including GDPR and HIPAA. Genomcore’s Biomed platform guarantees ethical surveillance to ensure that the required international and national regulatory approvals are obtained to coordinate the data acquisition, storage and sharing.

Also, we provided support to delineate and monitor the project’s Data Management Plan (DMP), which describes  the management of data life cycle during its collection, processing and generation. By creating and periodically updating the DMP, we help coordinate data and metadata acquisition, as well as data comparability and development of systems to cross-validate results, including clinical-molecular correlative analysis from the different cohorts from several countries, and the bioinformatic criteria to generate and store genetic data.

Overall, implementing a federated data governance framework under FAIR principles can be a complex and challenging process, requiring close collaboration and communication between different research institutions, as well as a strong commitment to ethical and transparent data management practices. However, the benefits of such an approach – including increased data sharing, collaboration, and the potential for new discoveries – make it a worthwhile endeavor. 

Genomcore can provide the necessary technological framework to enable a cost-effective federated approach for transferring information between the clinic and research, with the ultimate goal of advancing personalized medicine and biomedical research. By implementing innovative data management and analysis tools, such as cloud-based data storage and no code workflows, Genomcore can support the integration and harmonization of data across different sources, while also ensuring that data is accessible, interoperable, and reusable in compliance with FAIR principles. This can facilitate cross-institutional collaboration and data sharing, leading to the generation of new insights and discoveries in the field of personalized medicine and beyond.


The test developed by Dr. Serra’s team appeared to be more precise than the current ones to identify and stratify tumours that present homologous recombination deficiency (HRD). The biomarker has been shown to be accurate in predicting response to PARP inhibitors and platinum-based chemotherapy.

According to Serra, the RAD51 test manages to predict the response to the PARP inhibitors in up to 95% of the cases in comparison to the 67% of HRR gene mutations and 71% of HRD genomics analyses. The RAD51 test not only helps to identify patients with BRCA1/2 alterations that would make them more sensitive to PARP inhibitors, but also those with epigenetic alterations. In this way, assures Serra, “it becomes a valuable aid in decision-making and makes it possible to extend the population of patients who can benefit from these drugs.”

Together with CRO SOLTI and AstraZeneca, the researchers will now carry out a clinical trial, the RADIOLA, to evaluate the effectiveness of the test on patients with HER2-negative metastatic breast cancer. The study participants will receive Olaparib treatment, a PARP inhibitor that has received approval from the European Medicines Agency (EMA) for breast cancer treatment, but is not yet available for this purpose in Spain.

"Participating in this trial will be a great opportunity for patients with germline BRCA1/2 alterations and will allow us to demonstrate in clinical practice the efficacy of using the RAD51 test to identify patients without genetic alterations who may benefit from these drugs".

At Genomcore we are very proud to have been part of the RAD51predict project providing our innovative multimodal unified datastore. As a result, a ground-breaking functional research assay has been transformed into a clinical-grade predictive test, offering personalized treatment options for cancer patients.

Discover the features used in this project