Data Governance and Distribution of Biobank: A Case from a Chinese Cancer Hospital

Objectives: To facilitate the regionalization, specialization, and digitization of biobanks, three issues regarding data collection and application must be addressed (1) integration and distribution of data governance, (2) efficiency and efficacy of data governance, and (3) sustainability of data governance. Methods: We collaborated with stakeholders to identify priorities and assess infrastructure needs through the continuous evaluation and analysis of projects. We developed data management solutions, catalogs, and data models to optimize and support data collection, distribution, and application. Furthermore, ontologies were used to facilitate data integration from multiple sources, and Minimum Information About BIobank Data Sharing (MIABIS) was defined as accessible to all patients. To enhance data integrity, we conducted retrospective and prospective follow-up studies. Results: We completed infrastructure upgrades to match technical solutions and research demands. An information management software with six primary functional divisions was developed for data governance. We optimized the database structure and changed the biospecimen accumulation model from biospecimen-based to patient-centered and service-oriented. Subsequently, we specified 85 attributes of MIABIS to describe the biobank contents. A dual-pillar approach was adopted to expand the biobank's data in collaboration with other institutions, and MIABIS served as a bridge for both vertical and horizontal networks. From 2003 to 2021, we collected a total of 156,997 patient biospecimens/data from 20 cancer types, matching 53,113 cases from follow-up surveys. In addition, we supplied more than 40,000 biospecimens/data points for above 300 scientific research projects. Conclusions: An appropriate information platform for a biobank is fundamental to data collection, distribution, and application, particularly in the context of data-intensive research. We implemented a standardized scientific data structure to fulfill the research requirements. The sustainable development of a biobank depends on a scientific, standardized, and service-oriented data governance approach, along with the efficient utilization of emerging technologies.