Big Data Clinical Realities and the Human Dimensions of Interoperable Data

Saturday, February 13, 2016: 1:00 PM-2:30 PM
Marshall Ballroom North (Marriott Wardman Park)
John N. Aucott, Johns Hopkins University School of Medicine, Lutherville, MD
Lyme disease is the most common vector- born infection in the United States.  It is a complex disease that may involve multiple organ systems and results in complicated interactions between the human host and the bacterial pathogen. The progression of the disease over time is also complex, with the potential for untreated infection to lead to chronic neurological, immune, and musculoskeletal dysfunction. The complexity of Lyme disease, combined with lack of biomarkers to measure infection, has slowed progress in Lyme disease research. 

The SLICE study at Johns Hopkins has taken a new approach to unravelling the mysteries of Lyme disease using advances in “Big Data” and personalized medicine. Patients are followed over 2 years, gathering large amounts of clinical and biologic information.  The “Big Data” approach to understanding disease mechanisms requires meticulous clinical description of the patients that the SLICE study provides. . When this patient data is combined with molecular data from blood samples there is great potential for understanding disease mechanisms and new approaches for treatment. 

Recent advances in technology now allows us to perform multiple blood assays simultaneously from small amounts of blood to measure specific molecular biomarkers of disease.  This creates enormous amounts of data. The tiniest concentration of blood molecules can now be detected, molecules that were previously “invisible” to scientists. This data includes DNA sequencing, gene expression, blood protein and cell analysis that offers the ability to measure the complex biology in Lyme disease.

Recent advances in data science and statistical analysis now offer new opportunities to realize the potential of “Big Data” that combine these  large amounts of blood test analysis with the clinical profiles of individual patients. These computational approaches can find molecular patterns and significant biomarkers in the large data sets that have been created.  

This approach has also enabled "Precision Medicine" methods in Lyme disease to identify important patient subgroups and their defining biomarkers. The SLICE study has already shown the value of this approach in identifying unique subgroups of patients with Lyme disease that develop different immune responses to the infection, opening windows into the understanding of differences in disease outcomes. The SLICE project is enabled by the emerging confluence of these technologies, which provide new opportunities to accelerate understanding, therapies, and improved patient outcomes in Lyme disease.