Experts from The Ohio State University and University of Kentucky are collaborating to create a first of its kind biomedical informatics based approach to help improve survival rates in people with lung cancer, which is the leading cancer killer among men and women in the United States.
The approach is combines two different areas of biomedical informatics -- digital imaging and genomic sequencing -- in order to match patients with treatments most likely to extend their survival. According to the American Lung Association, over half of people with lung cancer die within one year of being diagnosed.
"Lung cancer treatment choice hangs critically on how a pathologist classifies certain cancer cell traits -- the phenotype. But this interpretation can be highly subjective," said Kun Huang, PhD, associate professor of Biomedical Informatics at Ohio State's College of Medicine. "We have genomic data that tells us what treatments might work best for a specific person, but that doesn't tell us how aggressive the cancer type may be. So clinicians today are making decisions on the best available data, but it's an incomplete set of information."
The data from one individual's genome and lung cancer phenotype equals a little more than four gigabytes- about as much data contained on a feature length movie DVD. Huang and his research partner, Lin Yang, PhD, assistant professor with the Division of Biomedical Informatics at University of Kentucky, will be working with computers and programs that are able to crunch and compare over 15 terabytes of data, the equivalent of about 3,000 DVDs.
"A pathology report gives you one set of insights, a person's genome another. This model will bring the power of both together to help clinicians select the right treatment for the best outcome," said Yang who also leads the Biomedical Image Computing and Imaging Informatics (BICI2) Lab at UK.
Both Huang and Yang have spent their careers refining biomedical informatics technology and approaches to help turn digital medical images into useable data and to create programs that uncover the clues to disease buried deep within the genetic code. Friends from graduate school in University of Illinois at Urbana-Champaign and Rutgers University, when the researchers relocated to Ohio and Kentucky, they reignited a conversation that had been started years earlier about their mutual passion for creating way to integrate imaging and genomic data.
The team's passion paid off, and in the fall of 2012, they received a $50,000 Community Engagement Joint Pilot Award Collaboration Grant from the Ohio State Center for Clinical and Translational Science (CCTS) and the University of Kentucky CCTS. Both the Ohio State and UK translational science centers created the Appalachian Translational Research Network (ATRN) in 2011 to help address health disparities in the Appalachian region, a 205,000-square-mile area that spans from southern New York to northern Mississippi. The estimated 24.8 million people living in Appalachia are more likely to die from diabetes, lung cancer and heart disease than people living in other areas of the country.
Huang and Yang will initially create their model using a repository of tissue from more than 4,000 lung cancer patients from Appalachia, and genomic data from a National Institutes of Health database, The Cancer Genome Atlas (TCGA).
"We're hoping that the model will not only allow clinicians to personalize treatment, but also allow us to track trends within Appalachia itself in order to really start addressing the disparities in a targeted way," said Huang, who is also a Technical Director of the Bioinformatics Shared Resource facility in the Comprehensive Cancer Center at Ohio State.
While the hills of Appalachia may separate the two researchers, they communicate frequently using Skype, and have planned visits to each other's labs in the coming months.
"You really have to trust the person you're working with, and communication has been smooth so far. Every time we talk we are coming up with new ideas for how the data might be used -- new biomarkers, new image markers -- the possibilities are endless and exciting," said Yang.
The team hopes to start sharing results from their work in one year.
The above post is reprinted from materials provided by Ohio State University Center for Clinical and Translational Science. Note: Materials may be edited for content and length.
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