(172c) Dynamic Modeling of Pancreatic Cancer Metabolism to Investigate Optimal Therapeutic Strategies

Pancreatic cancer is an aggressive disease with an overall 5-year survival rate of ~5%. Cancer cells in pancreatic tumors rely on glucose and glutamine to generate energy and building blocks for proliferation. Therapies that target tumor metabolism aim to impede the unique metabolic phenotype of the cancer cells; however, in order to develop effective treatment strategies, quantitative information about how specific reactions contribute to and control tumor metabolism is needed.

We have built a computational model that predicts the dynamics of glucose and glutamine metabolism in pancreatic cancer cells over time. The model predicts relevant metabolic information that is difficult to measure experimentally, including the time courses of the metabolite concentrations and the fluxes of each reaction [1]. The model also links metabolism to cancer cell growth and predicts the number of pancreatic cancer cells over time. We fit the model to published data obtained from experimental studies of pancreatic cancer cells and validated the model using a distinct set of measurements not included in the fitting. As a result of this work, we constructed a validated model of pancreatic cancer metabolism that predicts tumor growth in response to nutrient availability and utilization. To our knowledge, this is the first model of cellular metabolism that specifically predicts the dynamics of pancreatic cancer cells.

The model provides mechanistic insight into novel targets for inhibiting pancreatic cancer cell metabolism. We applied the model to predict the effects of inhibiting various enzymes in the metabolic network, identifying effective therapeutic targets to inhibit tumor metabolism impede cell growth. We specifically investigate strategies that target core metabolic enzymes individually and in combination. Thus, the model can be used to design novel therapies for impeding cancer cell proliferation, complementing in vitro and in vivo pre-clinical studies. We are building on this work to predict the dynamics of carbon utilization and labeling using isotope-labeled metabolomics experiments. Altogether, we demonstrate the importance of kinetic modeling to investigate novel therapeutic strategies for pancreatic cancer.

[1] Roy, M. and Finley, S.D., (2017) Computational model predicts the effects of targeting cellular metabolism in pancreatic cancer. Front. Physiol. 8:217. doi: 10.3389/fphys.2017.00217