Stites Lab

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, and lung cancer 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.

 

We believe that new approaches are needed to break new ground on RAS and the treatment of RAS-driven diseases.  The laboratory's recent work has uncovered clinically significant relationships between KRAS mutations and the response to treatment (McFall T et al, Science Signaling, 2019; McFall T et al, bioRxiv, 2019).

 

These studies have been heavily influenced by our systems biology approach to studying oncogenes and their response to treatment (Stites EC et al, Science, 2007; Stites EC et al, Cell Reports, 2015).  We believe that this 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.

 

Mathematical and computational models of RAS and RAS pathway proteins play a prominent role in our work.  These models that are based upon the high-quality data that is available to characterize the biochemistry of wild-type and mutant RAS proteins.

 

We have additional 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

 

We are also applying our integrated experimental and computational biology approach to the study oncogenic kinases.  We are invetigating the relationships 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, Stites EC, Science Signaling, 2012) and EGFR (Stites EC et al, Biophysical Journal, 2015).  Our first studies focus on incompletely explained behaviors of cancers treated with RAF kinase inhibitors (Mendiratta G, et al, bioRxiv, 2019).

 

 

 

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 both how to prevent and how to treat disease.