European journal of cancer (Oxford, England : 1990) · 2025
Overcoming data management challenges in oncology research: Lessons from an NHS, industry, technology start-up and academic collaboration.
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Conway Alicia-Marie, Concannon Matthew, Brown Steven, Hussain Humera, Marland April, Griffiths Matthew, Plummer Barney, Warrington Kelly, Bridges Claire, Lightning-Jones Karen, Cook Natalie
Аннотация
BACKGROUND: The integration of large-scale genomic and multimodal data is critical to advancing oncology research. However, challenges related to data storage, sharing, and governance hinder its effective use. We share experience from conducting a multi-site, cross-industry UK project utilising large-scale genomic data obtained from tissue and liquid biopsies from patients with cancer, to produce recommendations for enabling and optimising the use of multimodal data in oncology research.
METHODS: A collaborative approach involving NHS Trusts, industry, start-ups, and academic partners was adopted to develop a robust data management strategy. A data lake architecture was selected as the centralised repository to store and share diverse datasets securely. Key factors influencing the selection and implementation of this solution included data storage requirements, access control, ownership, and information governance. Processes for planning, deploying, and maintaining the data lake infrastructure were documented and evaluated.
RESULTS: The data lake enabled secure, compliant, and federated storage of large-scale genomic and clinical data. Successful implementation required early engagement of stakeholders and the establishment of clear data governance frameworks. Lessons learned highlighted the importance of aligning technical solutions with governance, security, and accessibility requirements across diverse partners.
CONCLUSIONS: Effective management of multimodal data in oncology requires early planning, multi-stakeholder engagement (among National Health Service [NHS] Trusts, industry, start-up collaborators, and academic institutions), and robust governance. The data lake model demonstrated a scalable and compliant approach to enabling secure, collaborative research using genomic data, providing a template for future initiatives in precision oncology.
METHODS: A collaborative approach involving NHS Trusts, industry, start-ups, and academic partners was adopted to develop a robust data management strategy. A data lake architecture was selected as the centralised repository to store and share diverse datasets securely. Key factors influencing the selection and implementation of this solution included data storage requirements, access control, ownership, and information governance. Processes for planning, deploying, and maintaining the data lake infrastructure were documented and evaluated.
RESULTS: The data lake enabled secure, compliant, and federated storage of large-scale genomic and clinical data. Successful implementation required early engagement of stakeholders and the establishment of clear data governance frameworks. Lessons learned highlighted the importance of aligning technical solutions with governance, security, and accessibility requirements across diverse partners.
CONCLUSIONS: Effective management of multimodal data in oncology requires early planning, multi-stakeholder engagement (among National Health Service [NHS] Trusts, industry, start-up collaborators, and academic institutions), and robust governance. The data lake model demonstrated a scalable and compliant approach to enabling secure, collaborative research using genomic data, providing a template for future initiatives in precision oncology.