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Systems biology takes an interdisciplinary approach to the systematic study of complex interactions in biological systems. This approach seeks to decipher the emergent behaviors of complex systems rather than focusing only on their constituent properties. As an increasing number of examples illustrate the value of systems biology approaches to understand the initiation, progression, and treatment of cancer, systems biologists from across Europe and the United States hope for changes in the way their field is currently perceived among cancer researchers. In a recent EU-US workshop, supported by the European Commission, the German Federal Ministry for Education and Research, and the National Cancer Institute of the NIH, the participants discussed the strengths, weaknesses, hurdles, and opportunities in cancer systems biology. Cancer Res; 70(1); 12–3

Metabolic reprogramming is increasingly recognized as a fundamental hallmark of cancer. While the Warburg effect and normal proliferative metabolism are similar, they are not equivalent. We hypothesize that there are key drivers of malignant metabolism that can be modulated to impede cancer proliferation without substantial effects on normal tissue growth. Using 13C-labeled glucose and glutamine tracers in combination with mass spectrometry and measurements of extracellular glucose, lactate, and glutamine flux, we have characterized system level differences in a series of breast cancer cell lines as well as normal-like breast epithelial cells. We observed an increase in the reductive carboxylation of glutamine-derived citrate and alpha-ketoglutarate in the triple-negative inflammatory breast cancer cell line SUM149. We also observed that the SUM149 exhibit high levels of HIF-1α and low levels of oxygen consumption under normoxia, suggesting that the cell line is highly adapted to hypoxia. Surprisingly, the stable depletion of HIF-1α via shRNA had no significant effect on the metabolic profile of these cells. Previous work by our lab and others has demonstrated that the GTPase RhoC is a driver of the metastatic phenotype exhibited by inflammatory breast cancer. Activation of RhoC is known to induce cytoskeletal rearrangements and increase invasive potential. The Rho GTPase family of proteins has also recently been linked to metabolism, specifically regulation of glutaminase activity. Here we show that stable knockdown of RhoC in SUM149 cells results in a marked decrease in the rate of both glutamine uptake and intracellular reductive carboxylation. This work reinforces the role of RhoC as an important driver of inflammatory breast cancer metastatic potential. We conclude that RhoC remains an important clinical target with the potential to alter patient outcomes. Citation Format: Joel A. Yates, Michelle L. Wynn, ZhiFen Wu, Charles R. Evans, Charles Burant, Santiago D. Schnell, Sofia D. Merajver. Metastasis-associated oncogene RhoC as a regulator of glutamine metabolism in the inflammatory breast cancer cell line SUM149. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1203. doi:10.1158/1538-7445.AM2015-1203

It is increasingly likely that the high incidence of off-target effects associated with targeted inhibitors is due, in part, to the highly complex and dysregulated intracellular molecular networks associated with cancer. Ignoring this complexity can lead to suboptimal results and subsequent loss of life through ineffective therapies. The phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) pathways are two of the most dysregulated pathways across all cancers. Several regulatory mechanisms have been proposed to explain the apparent cross-talk between them. For example, RAS to ERK signaling in the MAPK pathway has been proposed as an important metastases “escape mechanism” when PI3K is inhibited. In order to rationally develop precise therapeutic avenues to target these oncogenic pathways, it is critical to understand how the pathways are wired as an integrated network, in both normal and tumor cells. We have developed a systems biology approach that integrates measurements of protein activation under diverse experimental conditions, including inhibition of MEK and PI3K, with a novel network inference computational model that predicts the causal connectivity of a network from experimental data. The network inference model utilizes a genetic algorithm to search for the “optimal” network configuration that most closely matches the experimental data used as input. We validated the approach using in silico data generated from a set of randomized test networks. Next, we applied this approach to breast epithelial cell lines (MFC10A, MCF7, MDA-MB-231, and SUM149) by performing a set of experiments using a series of pathway specific inhibitors with and without growth factor stimulation. From phospho western blot readouts of several proteins in the PI3K and MAPK pathways, we have predicted network configurations most likely responsible for the distinct experimental output of each of the four cell lines. In some cases, our network inference model predicted multiple optimal networks for a given cell line. Our method predicted the next experiments needed to optimally distinguish between the set of possible candidate networks. Our results suggest that some proposed interactions and feedback mechanisms attributed to MAPK and PI3K cross-talk in the literature may not be valid. This approach has important implications for the prospect of effective personalized cancer treatments and targeted molecular inhibition. Elucidating the mechanisms of cross-talk between the MAPK and PI3K pathways in cells collected from patient tumors will permit rational discovery of the optimal combination of targeted therapies needed to treat a specific cancer, based on the actual network that is operational in the tumor. Moreover, our methodology and predictive network model can be applied to any set of signaling pathways. Citation Format: Megan E. Egbert, Michelle L. Wynn, Zhi Fen Wu, Rabia A. Gilani, Santiago Schnell, Sofia D. Merajver. A systems biology approach for rational molecular network inference. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 357. doi:10.1158/1538-7445.AM2014-357

Targeted molecular inhibitors have emerged as a leading anti-cancer strategy; however, despite promising pre-clinical data, many targeted inhibitors induce undesirable off-target effects in the clinic. The large number of off-target effects associated with molecular inhibitors was recently termed the ‘‘whack a mole problem’’ because inhibiting one molecular target often unintentionally activates another molecule. It is increasingly clear that the high incidence of off-target effects associated with targeted inhibitors is related to the complex interactions and emergent behaviors inherent to the highly complex and dysregulated intracellular networks of cancer. Both the mitogen activated protein kinase (MAPK) and phosphatidylinositol-3 kinase (PI3K) pathways are known to be dysregulated in cancer. In previous work, we and others have demonstrated that the MAPK pathway promotes motility, invasion, and angiogenic factors while the PI3K pathway plays an important role in controlling anchorage independent growth. In addition, the PI3K pathway plays an essential role in stimulating glucose metabolism and the Warburg effect. We hypothesize that robust interactions exist between these two pathways that influence efficacy and potentially also acquired resistance to targeted therapies. Using a combination of experimental and theoretical techniques, we developed a predictive network model linking growth factor signaling to the MAPK and PI3K pathways as well as to glucose metabolism. Specifically, we constructed a logic-based network of the cross-talk between MAPK and PI3K signaling that relied on a detailed literature survey to identify known molecular interactions as well as proposed interactions and regulatory feedback connections in the literature. We next performed a set of experiments using a normal-like breast epithelial cell line and a series of pathway specific inhibitors with and without growth factor stimulation to validate our model. Finally, we repeated these experiments using a diverse set of breast cancer cell lines and integrated this data to produce a series of cancer networks representative of different stages of breast cancer progression. Our model was able to recapitulate both our own experimental data and published data in the literature using a smaller subset of regulatory feedback mechanisms than we started with. Together, our results suggest that some proposed interactions and feedback mechanisms attributed to MAPK and PI3K cross-talk in the literature may not be valid. Citation Format: Megan E. Egbert, Michelle L. Wynn, Zhi Fen Wu, Santiago Schnell, Sofia D. Merajver. Elucidating the complex cross-talk between the MAPK and PIK3 pathways. [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 5223. doi:10.1158/1538-7445.AM2013-5223

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