To ensure good coverage of medical priorities in Europe, both the coordinators of transnational funding programmes (ERA-Nets and Joint Programming Initiatives) and representatives of main pan-European initiatives and medical research communities were invited to be part of the Medical Infrastructure/Users Forum.

Several face-to-face meetings are planned during the whole project, in order to gather input from various stakeholders (infrastructures, scientific communities, funding bodies) and to identify solutions for a long-lasting collaboration strategy. Based on MIUF recommendations, a survey was conducted to collect feedback from RI users and medical researchers to identify missing tools and services, as well as potential missing infrastructures. A report summarizing the main outcomes and recommendations is available here.

Mice are widely used in biomedical research to gain insight into the gene function in human health and diseases, to act as disease models to elucidate the involved pathways and the effects of treatments, and to support the development of treatments for human diseases. Task 3.2 aims to develop convergent sets of mouse phenotyping assays and clinical outcomes for specific diseases, in order to contribute to validating mouse models as a predictive system. A group composed of experts from INFRAFRONTIER (mouse phenotyping techniques), COMET (Core Outcome Measures in Effectiveness Trials) and COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) has defined the strategy based on two different approaches:

1. Identify a disease where Core Outcome Set Measures are currently not established

   • Develop Core Outcome Set (COS) for type II diabetes through a Delphi process, followed by a consensus meeting (driven by U Liverpool)
   • Define instruments to measure the developed COS in humans (driven by VU/VUmc)
   • Identify matching instruments in animals (led by INFRAFRONTIER)

2. Identify animal tests that may mimic the subjective rating scales used to assess fatigue, quality of life or wellbeing in humans

Transparency and access to research data is a key feature for research policies, impacting data quality and robustness of results, and leading to optimal use of data generated by research projects. This is of particular importance in clinical research, where access to individual patient-level data would ensure data quality and robustness of analyses, improve the accuracy of estimates of benefits from a treatment, and optimize the use of clinical trial data for re-analyses, secondary analyses and meta-analyses. Various organisations have endorsed the principle of providing the scientific community with access to patient-level data and several medical journals have made similar requests for the papers they publish. In spite of multiple discussions and a few pilot initiatives, a global consensus on principles and a framework for the practical implementation of such a data sharing policy are still needed.

A taskforce composed of experts covering different fields in data sharing was set-up to provide recommendations to make data sharing a reality. Three meetings were planned and held and multiple issues were discussed following a Group Nominal Process (a group process involving problem identification, solution generation, and decision making):

• Informed consent for re-use vs. broad consent or no consent (data protection regulation)

• Pseudoymisation vs. anonymization of data, and assessment of the risk of re-identification
• Open access vs. restricted/controlled access to data, scientific assessment of the request
• Format of patient-level data for storage and processing (e.g. CDISC standards)
• Data security and storage
• Type of repositories
• Possible IT solutions for data extraction (with or without download of data)

According to the outcomes of the discussion, a consensus document will be prepared listing principles, recommendations and possible solutions for a secure data sharing policy.

Biomarker research projects have to deal with a broad diversity of data, each requiring different tools and methods. Hospitals lack the number of patients and the spectrum of techniques required for efficient biomarker research; moreover, biomarker research projects face difficulties in timely managing their datasets, for clinical as well as pre-clinical biomarker research, due to the lack of multimodal data infrastructure for standardized storage, management and processing.
The aim of this task is to develop a common IT framework to support data handling and analysis for clinical and pre-clinical biomarker research by integrating different existing tools and providing multimodal services.
The implementation is driven by two use cases:

1. oncology (pre-clinical use case): In collaboration with Cancer Core Europe, genomic and biological marker data will be integrated to imaging data using tranSMART and cBioPortal tools; the final aim is to harmonize data for an optimal re-use for secondary analyses
2. osteoarthritis (clinical use case): In collaboration with the IMI-funded APPROACH project, genomic and imaging data will be integrated and processed through tranSMART and xNAT tools to obtain a multidimensional stratification profile

Access to prospective longitudinal population or patient cohorts and registries with reliable and interoperable clinical, molecular and imaging data is critical for the development of robust prognostic biomarker data. Harmonized and enriched data sets will improve epidemiological insights and disease sub-classification, as well as early diagnosis, rational therapy development and/or improved prevention.
In support of this, task 3.5 performs a use case on pancreatic cancer, as an example of lethal and essentially untreatable disease. Population cohorts were used to search for early predictive biomarkers, detectable 1 to 3 years before the onset of pancreatic cancer, as treatment at a very early stage may eradicate the condition.
The four biobanks composing the consortium (THL, UTARTU, NTNU and ErasmusMC) started with the definition and harmonization of molecular and clinical parameters, treatment outcomes and other data types. Then an inventory was made of the number and status of the samples in each biobank, which fulfilled the criteria of cases or controls; at the end of the process, 491 pancreatic cancer cases and 982 controls were selected. The next step will be the metabolomics data generation.

View a presentation on Integrating population cohorts to derive prognostic biomarkers as part of this task (by PI Gertjan van Ommen, Leiden University, BBMRI-NL/ERIC).