Genomic Data for Personalised Medicine
Personalised medicine is one of the main approaches to address the issues of increasing demand for healthcare and the economic burden on health, driven by demographic evolution, the aging population, the chronicisation of diseases.
Personalised medicine uses unique environmental, genetic, and medical information to individualise the prevention, diagnosis, monitoring, and treatment of disease. By doing so, the quality of care is improved, quality of life enhanced, and the societal burden of disease reduced. To develop personalised medicine, many different types of information (multimodal) need to be integrated and used together in a meaningful way, which is challenging, and many obstacles stand in the way. In particular, ensuring the effective use of information from our genes and how they function (multiomic) is essential and is an area with many unsolved challenges.
In NextGen, we are building novel and synergistic tools to enable portable multimodal, multiomic and clinically oriented research in high-impact areas of cardiovascular medicine. NextGen tools will benefit researchers, innovators and healthcare professionals by identifying and overcoming health data linkage barriers in exemplar use cases which are complex or intractable with existing technology. Consequently, it will benefit patients, providing faster diagnosis, and better treatments (including personal medicine).
A comprehensive gap analysis of the existing landscape, factoring in ongoing initiatives will ensure NextGen deliverables are forward-looking and complementary.
In particular, the NextGen embedded governance framework and robust regulatory processes will ensure secure multi-jurisdictional phenotype and genomic data access aligned with initiatives including “1+ Million Genomes” and European Health Data Space.
The NextGen tools approach
NextGen tools focuses its genomics-founded approach on the data integration of a wide range of cardiovascular use cases and using the relevant datasets to construct a thematic dataspace and its operational and technological management functions, working out solutions for data integration. These will overcome the hurdles of privacy & governance requirements, the presence of multiple standards, distinct data formats, and underlying data complexity and volume of multimodal data.
The research action of NextGen starts with an initial comprehensive gap analysis of the existing landscape, and, based on this developing specific solutions, with the factoring of ongoing initiatives and embedding a governance framework and robust regulatory processes, which will not only act as an enabler for the dataspace, but also ensure forward-looking and complementary solutions.
The practical outcomes of NextGen Tools will include
- tooling for multimodal data integration and research portability, extension of secure federated analytics to genomic computation, more effective federated learning over distributed infrastructures, more effective and accessible tools for genomic data analysis;
- approaches providing improved clinical efficiency of variant prioritisation;
- scalable genomic data curation;
- and improved data discoverability and data management.
Specific Objectives
1
Develop tools for the prediction, prevention, diagnosis, monitoring, and treatment of cardiovascular disease using multimodal data.
Develop tools for Personalised Medicine. NextGen will develop tools for the prediction, prevention, diagnosis, monitoring, and treatment of cardiovascular disease using multiomic, multimodal data. Health-economic and ethics-parallel research will assess the qualitative benefits and ethical perspectives for stakeholders.
Deliverables:
- Artificial intelligence (AI) models in cardiovascular medicine
- Published healthcare research demonstrating effective use of real-world data in several use cases
- Assessment of the health economic and ethical benefits and considerations
2
Improve the effective use of genomic data through advanced integration and workflow tooling.
Improve the effective use of genomic data through advanced integration and workflow tooling. NextGen will develop tools to overcome barriers in health data integration and enhance the effective use and incorporation of genomic data.
Deliverables:
- Deployed multimodal integration tools enabling cross-site portability of research & development
- Open-source software allowing acceleration of secondary and tertiary genomic data processing
- Innovative efficiency-enhancing AI-guided genomic data curation/interpretation tools.
3
Develop data analytics platform & infrastructure.
Develop data analytics platform & infrastructure. The NextGen data analytics platform will allow more effective federated machine learning and genomics calculations. It will support advanced data catalogue and query functionality including cross-site cohort matching for multi-site extension and cross-validation of research.
Deliverables:
- Deployed platform with advanced secure federated data catalogues, machine learning and genomic analytics
4
Integrate best practices through Pathfinder and pilots.
Integrate best practices through Pathfinder and pilots. Demonstrate of advanced integration and workflow tools in piloted use cases showing removal of technical and operational barriers. Pilot integrated into the “NextGen Pathfinder”: a multi-site “mini-EHDS” network showcasing NextGen innovations in data management, data governance, cataloguing, compute, advanced data integration, genomic and interoperability capacities. The Pathfinder will integrate best practise from evolving EU-wide initiatives such as the EHDS and 1+MG
Deliverables:
- Pilot implementations of project tools extending scope and quality of research outcomes
- Pathfinder network developed with five demonstration biobank sites demonstrating project tools