Abstract 5239: Unraveling the complex regulatory relationship between PI3K signaling and metabolic transformation in breast cancer.

Abstract

Abstract Cancer cells exhibit a metabolic phenotype characterized by high rates of glucose uptake and lactate production, known as the Warburg effect. While the Warburg effect and normal proliferative metabolism appear similar, important molecular differences exist. We hypothesize that molecular and metabolic drivers of the Warburg effect can be modulated to impede cancer proliferation without substantial effects on normal tissue growth. Intracellular networks exhibit a variety of emergent non-linear behaviors and, as a result, the use of experimental intuition alone will not be enough to identify these drivers. Using a combination of experimental and theoretical methods, we developed a model of breast cancer progression that includes metabolism and the phosphatidylinositol-3 kinase (PI3K) signaling pathway, an important regulator of carbon metabolism. A key component of our model is a detailed logic network of molecular interactions associated with PI3K signaling as well as regulatory connections to central carbon metabolism, including the ATP/AMP ratio, GLUT receptor activation, hexokinase activation, and changes in the catalytic activity of pyruvate kinase. To validate our model, a series of phospho Western blot analyses were performed using a normal-like breast cell line and a diverse set of breast cancer cell lines exposed to PI3K pathway inhibitors. From these data, a series of predictive network models were constructed representing distinct stages of breast cancer progression. We also generated detailed metabolic flux maps for each cell line using metabolic flux analysis (MFA), a method that relies on carbon-13 tracers, mass-spectrometry, and measurements of extracellular flux to infer intracellular flux. In agreement with recent studies, we found an increase in the reductive carboxylation of glutamine derived alpha-ketoglutarate in cells constitutively adapted to hypoxia. We also identified a potentially important metabolic vulnerability in aggressive breast cancers. Moreover, we found important PI3K network differences at the RNA and protein levels, some of which were isoform specific. Together our data indicate that very different system-level properties are associated with distinct stages of breast cancer progression and metabolic transformation. Our model is suitable for performing in silico molecular perturbations to predict a normal as well as tumor level response to a targeted therapy or combination of therapies. Our approach also serves as a prototype for the use of systems biology methods in personalized medicine where molecular and metabolic data collected from a patient's biopsied tumor is input into a predictive model designed to develop a strategic treatment plan for the patient. The use of predictive models to integrate data from an individual patient will have a profound impact on cancer care decisions and patient outcomes in the future. Citation Format: Michelle L. Wynn, Megan Egbert, Lauren D. Van Wassenhove, Zhi Fen Wu, Firas Midani, Charles Evans, Charles F. Burant, Santiago Schnell, Sofia D. Merajver. Unraveling the complex regulatory relationship between PI3K signaling and metabolic transformation in breast cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5239. doi:10.1158/1538-7445.AM2013-5239