AstroID resource: a scalable, relational database structure for longitudinal biomarker discovery

The biological sciences are producing increasingly larger datasets for biomarker discovery. While common data models have been developed for medical terms as they relate to patient health outcomes, a data model that supports longitudinal tracking of biospecimens and relating them against an individual patient experience is a large, unmet need.

A structure and associated taxonomy were achieved through a six-tier build in Research Electronic Data CAPture (REDCap), which organizes the complexity of the therapeutic decisions, biospecimens, and outcomes that characterize a longitudinal patient experience. Modules were developed to support export of REDCap data into a Structured Query Language (SQL) format for merging with extended biomarker data, also housed in SQL.

The resultant AstroID resource is a relational structure for clinical and biospecimen data that meets several desired goals: searchable, flexible, generic, Health Insurance Portability and Accountability Act-compliant, auditable, and easy-to-use. The essential elements forming the core of the six-tiered build are provided, so others can readily adopt this schema, as well as an example of an extended, customized build to support biomarker discovery for patients with melanoma. Two examples where this data structure was used to support biomarker discovery and development are described, and example queries of the database are also presented. To the extent possible, the data dictionary was aligned with large data models, such as those for the National Institutes of Health's Human Tumor Atlas Network. The structure can readily scale to accommodate thousands of patients, multimodality data, and spatial characterization of billions of cells. Radiologic imagery can also be included along with pathology imagery to support spatial studies, including artificial intelligence-driven analyses.

This effort provides a database model for investigators conducting research on large volumes of biospecimens with clinical annotation. We have now deployed this structure in our laboratories and have over 1B cells spatially mapped, each effectively tagged with the clinical information from longitudinal patient experiences. While the description uses the example of cancer biomarkers, this data structure could be used to characterize longitudinal biospecimens from any disease process. In the near future, automatic synchronization between the electronic medical record and one or more AstroID databases is anticipated.