Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience

Author:

Eschrich Steven A.1ORCID,Teer Jamie K.1ORCID,Reisman Phillip2,Siegel Erin3ORCID,Challa Chandan4,Lewis Patricia5,Fellows Katherine5,Malpica Everin4,Carvajal Rodrigo6ORCID,Gonzalez Guillermo6ORCID,Cukras Scott6,Betin-Montes Miguel6,Aden-Buie Garrick7ORCID,Avedon Melissa8ORCID,Manning Daniel9,Tan Aik Choon1ORCID,Fridley Brooke L.1ORCID,Gerke Travis2ORCID,Van Looveren Mattias4,Blake Amilcar4,Greenman Jennifer9,E. Rollison Dana10

Affiliation:

1. Department of Biostatistics & Bioinformatics, Moffitt Cancer Center, Tampa, FL

2. Health Informatics, Moffitt Cancer Center, Tampa, FL

3. Total Cancer Care, Moffitt Cancer Center, Tampa, FL

4. Data Engineering, Moffitt Cancer Center, Tampa, FL

5. Data Quality and Business Intelligence, Moffitt Cancer Center, Tampa, FL

6. Biostatistics and Bioinformatics Shared Resource, Moffitt Cancer Center, Tampa, FL

7. Collaborative Data Services Core, Moffitt Cancer Center, Tampa, FL

8. Basic, Population, and Quantitative Science Shared Resource Administration, Moffitt Cancer Center, Tampa, FL

9. Information Technology, Moffitt Cancer Center, Tampa, FL

10. Department of Epidemiology, Moffitt Cancer Center, Tampa, FL

Abstract

PURPOSE The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research. METHODS Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing. RESULTS The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case–focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse. CONCLUSION The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

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