Development of an open-source, flexible framework for complex inter-institutional disparate data sharing and...

Area of Science:

• Biomedical Informatics
• Data Science in Healthcare
• Oncology Research

Background:

• Harmonizing diverse clinical research data, including omics and tissue samples, presents significant challenges for data mining.
• Existing data management systems often struggle to integrate disparate information stores effectively.
• Efficient data accessibility is crucial for advancing research in complex diseases like neuroblastoma.

Purpose of the Study:

• To develop and implement a unified platform for storing and accessing harmonized clinical research data.
• To facilitate data mining by integrating clinical information, omics datasets, and tissue inventory data.
• To enable researchers to efficiently query patient cohorts with available tissue samples.

Main Methods:

• Development of a data platform using the Django web framework for rapid tool and view creation.
• Instantiation of clinical data for 11,000 neuroblastoma patients into a database.
• Integration of Nationwide Children's Hospital's tissue inventory data via an API.

Main Results:

• Successful creation of a database integrating 34 metrics from 11,000 neuroblastoma patients.
• Implementation of a front-end interface allowing complex queries and real-time tissue availability checks.
• Established API for consuming external tissue inventory data, linking patient cohorts with available samples.

Conclusions:

• The developed platform effectively addresses the challenge of harmonizing and managing diverse research data.
• The system enhances data accessibility for researchers, facilitating complex queries and sample retrieval.
• Future work includes expanding the patient cohort and integrating genomic data for comprehensive research studies.