Abstract
Targeting a tumor’s metabolic dependencies is a clinically actionable therapeutic approach, but identifying subtypes of tumors likely to respond remains difficult. The use of lipids as a nutrient source is of particular importance, especially in breast cancer. Imaging techniques offer the opportunity to quantify nutrient use in preclinical models to aid in the development of new drugs to restrict uptake or utilization of these nutrients. We describe a fast and dynamic approach to image fatty acid uptake in vivo, a tool relevant to study tumor metabolic reprogramming or for studying the effectiveness of drugs targeting lipid metabolism spanning beyond breast cancer and optical imaging alone. Specifically, we developed a quantitative optical approach to spatially and longitudinally map the kinetics of long-chain fatty acid uptake in in vivo murine models of breast cancer using a fluorescently labeled palmitate molecule, Bodipy FL c16. We chose intra-vital microscopy of mammary tumor windows to validate our approach in two orthotopic breast cancer models: a MYC-overexpressing transgenic triple-negative breast cancer (TNBC) model and a murine model of the 4T1 family. Following injection, Bodipy FL c16 fluorescence increased and reached its maximum after approximately 30 minutes, with the signal remaining stable during the 30-80-minute post-injection period. We used the fluorescence at 60 minutes (Bodipy60), the mid-point in the plateau region, as a summary parameter to quantify Bodipy FL c16 fluorescence in subsequent experiments. Using our imaging platform, we observed a two- to four-fold decrease in fatty acid uptake in response to the downregulation of the MYC oncogene consistent with findings from in vitro metabolic assays. In contrast, our imaging studies report an increase in fatty acid uptake with tumor aggressiveness (6NR, 4T07, and 4T1), and uptake was significantly decreased after treatment with a fatty acid transport inhibitor, perphenazine, in both normal mammary pads and in the most aggressive 4T1 tumor model. Our approach fills an important gap between in vitro assays, which provide rich metabolic information but at static time points, and imaging approaches that can visualize metabolism in whole organs, but which suffer from poor resolution.
Competing Interest Statement
The authors have declared no competing interest.
