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Stephen D. Hursting, PhD, MPH
Professor, Department of Nutrition
Nutrition Research Institute and
Lineberger Comprehensive Cancer Center
University of North Carolina
Chapel Hill, North Carolina
Seeking to understand the drivers of obesity-related breast cancer and develop effective interventions to prevent breast cancer.
Laboratory studies are conducted to test an intervention of weight loss and omega-3 fatty acid supplementation in preventing tumor growth.
These studies may inform the development of effective intervention strategies to reduce weight and the risk of breast cancer.
Obesity has emerged as an important risk and prognostic factor for several types of breast cancer. Dr. Hursting's BCRF-supported studies suggest that moderate weight loss may be insufficient to reverse the cancer-associated metabolic and inflammatory perturbations that occur with chronic obesity. However, the more severe weight loss that follows bariatric surgery does correct these perturbations and prevents the development and growth of mammary tumors in an experimental model of basal-like breast cancer.
Because bariatric surgery can be expensive and cause problems for the patient, Dr. Hursting is looking for ways to mimic the anti-cancer effects of bariatric surgery using dietary changes. Initial results showed that a low-fat diet supplemented with omega-3 fatty acids from fish partially reverses the harmful effects of obesity.
The team is currently working to mimic the anticancer effects of surgery by combining an intermittent energy restricted diet (shown to be more effective and sustainable than a low-fat diet or a constant energy restricted diet) with an anti-inflammatory intervention (omega-3 fatty acids).
In preliminary studies, both the energy restriction diet and omega-3 supplementation alone partially reversed the effects of obesity on inflammation, metabolic dysregulation, and mammary tumors.
In the upcoming year, the group will test whether combining the energy restriction with omega-3 fatty acids will more effectively mimic the anticancer effects of the bariatric surgery. These studies continue to build on the collaborative efforts of Dr. Hursting and his BCRF colleague, Dr. Carol Fabian, in linking preclinical and clinical research on breast cancer prevention.
Dr. Stephen Hursting is Professor in the Department of Nutrition and the Lineberger Comprehensive Cancer Center at UNC-Chapel Hill and Professor at the UNC Nutrition Research Institute in Kannapolis, NC. He earned his PhD in nutritional biochemistry and MPH in nutritional epidemiology from the UNC-Chapel Hill, and he completed postdoctoral training in molecular carcinogenesis and cancer prevention at the National Cancer Institute (NCI). Prior to joining the UNC faculty in 2014, Dr. Hursting was Professor and Chair of the Department of Nutritional Sciences at the University of Texas at Austin and Professor of Molecular Carcinogenesis at the UT-MD Anderson Cancer Center (2005-14). He also served as Deputy Director of the NCI’s Office of Preventive Oncology and Chief of the NCI’s Nutrition and Molecular Carcinogenesis Laboratory Section (1999-2005). His research interests center on diet-gene interactions relevant to cancer prevention, particularly the molecular and metabolic mechanisms underlying obesity-breast cancer associations, and the interplay between obesity, diabetes and breast cancer risk and response to therapy. Primarily using specially engineered laboratory models of breast cancer in parallel with breast cancer prevention trials (in collaboration with Dr. Carol Fabian at the Kansas Cancer Center), he is currently focusing on the molecular and metabolic changes occurring in response to lifestyle-based (dietary and physical activity), or pharmacologic manipulation of energy metabolism and cell signaling pathways, with emphasis on the IGF-1/Akt/mTOR and Wnt signaling pathways as well as inflammation. He also has expertise in assessing diet-related serum and tissue biomarkers, including hormones/growth factors, cytokines and chemokines, and microRNA’s in mouse and human samples.