RAS Pathway in Cancer and Disease
Mutant forms of the RAS genes, KRAS, NRAS, and HRAS, are major drivers of cancers. Pancreatic cancer, colon cancer, melanoma, lung cancer, and leukemia are just a few of the malignancies that are commonly driven by a RAS mutation. Mutant forms of the RAS genes are also associated with several of the genetic syndromes collectively referred to as the RASopathies.
Treatments that target RAS to benefit patients are not yet available. We believe that new approaches are needed to break new ground on RAS and the treatment of RAS-driven diseases. We have had success applying mathematical and computational techniques to the study of the RAS pathway. We believe that this "systems biology" approach provides our lab with a fresh perspective on fundamental problems pertaining to RAS, its role in disease development, and how it can be targeted.
Our previous work on the RAS pathway includes the use of our models to formulate hypotheses about RAS in cancer that we tested and confirmed experimentally (Stites EC et al, Science, 2007; Stites EC et al, Cell Reports, 2015). We have many other novel hypotheses about the RAS pathway in cancer and in genetic diseases that we developed from studying our mathematical models. We are actively testing these hypotheses experimentally in the laboratory.
Oncogenic Kinase Signaling
There are currently drugs that target several proteins in the Ras pathway. These proteins are all kinases. We are developing new approaches to computationally and mathematically model oncogenic kinases. We will use these models to investigate the relationship between mutations, disease, and the response to treatment. We are focusing on kinases in the RAS signaling network, such as RAF (Hu J, et al, Cell, 2013) and EGFR (Stites EC et al, Biophysical Journal, 2015). We are also experimentally testing new hypotheses about kinase signaling in disease (Stites EC, Science Signaling 2012), including ideas for how to better target these kinases therapeutically.
Systems Biology of Disease
Exciting potential applications for computational models of disease-associated networks include roles in drug development (systems pharmacology), in matching patients with the right drugs for their genetic background (personalized medicine), and for interpreting patient-specific genomic and molecular characterization (computational pathology).
The RAS model has been successfully applied to problems in each of these areas; we aim to do this for more proteins, more networks, and more diseases. The computational methods will necessarily vary for reasons ranging from what information is available to what specific questions will be asked of the model. We anticipate that analyses of such models will provide deeper insights into mechanisms of disease, and will lead to new ideas for how to prevent and to treat disease.