Abstract The vast wealth of medical data collected over the last decade holds great promise for accelerating novel research, discovery, and clinical translation. Specifically, the rapid expansion of genomic testing provides new opportunities for the clinical management of cancer patients, influencing diagnosis, risk stratification, and treatment planning. Moffitt Cancer Center's Personalized Medicine Clinical Service integrates next-generation sequencing test results into patient care, using the data to guide individualized treatment plans. To maximize the efficiency and efficacy of this service, creative solutions for data harmonization, storage, and management are required. We implemented a commercial molecular data warehouse (MDW), directly linked to our existing clinical data warehouse, to store and manage molecular data ranging from genotypic alterations to annotations from public resources (HUGO, COSMIC, Ensembl) and clinically actionable targets (4,256 records currently loaded). A centralized, cloud-based data and analytics platform is also being implemented at Moffitt that will integrate a broad range of multi-modal data. In the cloud environment, the data from the MDW will be linked to typically siloed data streams from the electronic health record, cancer registry management system, biospecimen management system, billing and scheduling systems, patient-reported information and outcomes, and patient-generated health data, creating a unique and customized Personalized Medicine Curated Data Mart (CDM). In addition to describing the features of the MDW and the challenges faced during its implementation, we will provide an overview of the extensive data cleaning and curation required to facilitate such a CDM. This includes the extraction of disease characteristics from unstructured clinical text via natural language processing, creation of new derived data fields, approaches to extracting and managing complex treatment data, and the inclusion of detailed, manually-abstracted recurrence and outcomes data for historical patients from existing institutional datasets such as the Clinical Genomics Action Committee (CGAC) database. Finally, we will present prototypes of analytics dashboards that will interface directly with our CDM, facilitating intuitive data exploration for all members of our personalized medicine teams. Citation Format: Rachel Howard, Kevin Hicks, Jamie Teer, Phillip Reisman, Mandy O'Leary, Steven Eschrich, Ross Mitchell, Howard McLeod, Dana Rollison. Facilitating personalized medicine with cloud-based storage and analytics [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3226.
Read full abstract