Table 3.

Projects in the UCSD TREC Center titled Energetics & Breast Cancer: Obesity, Inflammation, Insulin Resistance & Risk (Principal Investigator: Ruth E. Patterson PhD)

Project 1. Role of Inflammation and Insulin Resistance in Mouse Models of Breast Cancer. Project Leader: Jerrold Olefsky, MD Diets rich in saturated and omega-6 (ω6) fatty acids (FAs) are pro-inflammatory and increase breast cancer risk, but diets rich in omega-3 (ω3) fatty acids are anti-inflammatory and decrease cancer risk. We hypothesize that the receptor, GPR120, is the critical mediator of the protective effects of ω3 FAs in breast cancer. This will be tested in 4 specific aims that combine (1) studies using orthotopic tumor cell transplants, (2) spontaneous tumors in obese wild type (WT) and GPR120 knockout (KO) mice, with and without ω3 FA supplementation; (3) studies using orthotopic mouse and human tumor cell transplants into RAG2 knockout mice, and (4) studies of metastasis using genetically marked tumor cells in obese WT and GPR120 KO mice.

Project 2: Diet Composition and Genetics: Effects on Weight, Inflammation and Biomarkers. Project Leader: Cheryl L Rock, PhD, RD This project will examine whether there is a differential response to dietary macronutrient composition in a weight loss intervention in healthy obese women, depending on insulin resistance status. Outcomes include weight loss; hormonal factors and markers of inflammation that may link obesity to breast cancer mortality; and nutrient-gene interactions that contribute to differential response of cytokines to weight loss and diet composition associated with polymorphisms in IL-6 and TNF-α genes. The study will address these aims in a randomized controlled trial involving 234 obese women randomly assigned to a high-carbohydrate (65% energy) low-fat (20% energy); a low-carbohydrate (45% energy) high-monounsaturated fat (35% energy) diet; or a walnut-rich, lower carbohydrate (45%), higher fat (35% of energy) diet in a 12-month behavioral weight loss program.

Project 3: Weight Loss, Mechanisms, and Mortality Among Breast Cancer Survivors. Project leader: Ruth E. Patterson, PhD Biomarkers will be assayed in archived blood samples from a cohort of overweight/obese, postmenopausal cancer survivors with long-term follow-up for mortality. Biomarkers will represent the major proposed mechanisms by which obesity is associated with postmenopausal breast cancer: (1) insulin-IGF axis, (2) endogenous sex hormones, and (3) inflammation. The joint role of these biomarkers will be examined to develop a Biomarker Risk Score that predicts breast cancer mortality. These Risk Score biomarkers will then be assayed in blood samples collected in a randomized controlled trial of metformin and/or weight loss. This 6-month weight loss and exercise intervention trial will randomize 340 overweight/obese postmenopausal breast cancer survivors into four groups: weight loss and metformin, weight loss and placebo, metformin, or placebo. Change in the Biomarker Risk Score from baseline will be used to predict change in the risk of breast cancer mortality in response to the interventions.

Project 4: Advancing Assessment of Energy Expenditure in Women with Increased Cancer Risk. Project Leader: Jacqueline Kerr, PhD Participants in the UCSD randomized trials described above will participate in Project 4, by wearing an accelerometer, GPS device, and heart rate monitor for 7 days at study entry and follow-up. Machine Learning algorithms will be applied to the data collected by these devices. Machine Learning is a process whereby computer algorithms are developed and validated in comparison to a training data set (i.e., a gold standard). The training data set will be developed in a separate but parallel study where obese women will wear the physical activity assessment devices and a SenseCam: a small device worn like a necklace that passively collects ~3000 images per day. These images will be manually coded to create the annotated truth file (gold standard) for comparison to the data collected via accelerometers, GPS, and heart rate monitors. The algorithms will be used to assess behavior types as well as activity intensity and their relation to insulin resistance.