Ask the Expert: Big Data

Garry CuttingGarry Cutting is a pediatrics professor in the McKusick-Nathans Institute of Genetic Medicine and is director of the CFTR2 project, a searchable database of information from people with cystic fibrosis. He is using big data to develop individualized treatments for people with cystic fibrosis, a debilitating disease caused by mutations in the CFTR gene.  


1. How is big data transforming treatments of cystic fibrosis? What sorts of information are being used to determine these treatments?

Clinical and genetic data collected from almost 40,000 individuals with cystic fibrosis is informing the development of a new generation of treatments that directly target the defective protein in cystic fibrosis. As there are hundreds of different defects to treat, we are assembling detailed profiles of the clinical features associated with each defect. These profiles will serve as a baseline for assessing the efficacy of molecular-based treatments.

2. How do you think big data will be used to treat cystic fibrosis in five years? Ten years?

The goal over the next five years is to have a molecular therapy for all individuals with cystic fibrosis. To achieve this, we are in the process of collecting data on all individuals with cystic fibrosis worldwide. While the new therapies are promising, individuals vary in their response and course of disease. Over the next 10 years, we propose to optimize outcomes by gathering genome-wide variation and specific environmental exposures from a fraction of individuals with cystic fibrosis. These data will be used to determine which combinations produce better outcomes and to create treatment profiles that can be applied to all individuals with cystic fibrosis.

3. How is big data transforming medicine in general? 

Big data is an appealing concept in medicine, as it promises new approaches to patient management and cost containment. By assembling and analyzing data from large groups of patients, it is believed that patterns will emerge that enable prospective rather than reactive management of health and disease.   

4. What are the current limitations of big data in medicine in terms of gathering data and computing power?  

To analyze data gathered as part of medical care, we must be able to draw information from electronic patient record systems. Unfortunately, there are many different electronic patient record systems, and they do not facilitate data exchange. A second challenge is the computing capacity required to handle very large datasets. A High Performance Research Computing Center at Johns Hopkins Bayview Medical Center is coming online and should provide the necessary infrastructure. A third issue is the skilled personnel needed to assemble, verify and analyze the data. Programs that train data professionals are proliferating, and their graduates should fill the current gap. The final challenge is to present these data in formats that medical professionals will embrace and use to maintain the health of their patients in a cost-effective manner.

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