Case Studies for Overcoming Challenges in Using Big Data in Cancer.

Area of Science:

• Oncology
• Bioinformatics
• Health Informatics

Background:

• Big healthcare data offers significant potential for advancing cancer research and precision medicine.
• Challenges exist in organizing, sharing, and integrating large datasets for research accessibility.
• Effective data management is crucial for unlocking insights in oncology drug development and patient care.

Approach:

• This review examines five successful initiatives addressing big healthcare data challenges in oncology.
• Case studies include CancerLinQ, AACR Project GENIE, Project Data Sphere, NCI Genomic Data Commons, and VHA Clinical Data Initiative.
• The review highlights critical success factors for developing data-sharing systems.

Key Points:

• Robust data aggregation pipelines and common data models are essential.
• Data deidentification, workflow integration, and terminology standardization facilitate data utility.
• Interoperability, quality control, and multi-type data incorporation are vital for comprehensive analysis.
• Understanding optimal application of data resources is key to maximizing impact.

Conclusions:

• Emerging data resources demonstrate successful strategies for managing big healthcare data in oncology.
• Early consideration of secondary data use and proper sharing practices are encouraged.
• These efforts aim to generate insights that advance cancer understanding and treatment through data sharing.