Project advisor: Dr. Dana Paquin
Project description: Chronic myelogenous leukemia (CML) results from the uncontrolled growth of white blood cells due to increased and unregulated growth of predominantly myeloid cells in the bone marrow and the accumulation of these cells in the blood. The standard treatment for CML is the tyrosine kinase inhibitor imatinib mesylate. Although imatinib is an effective treatment for CML and most patients attain some form of remission, imatinib does not completely eliminate all leukemia cells, and if the imatinib treatment is stopped, all patients eventually relapse. In 2010, I worked with researchers at the University of Utah and the University of Maryland to develop and analyze a delay differential equations mathematical model for strategic treatment interruptions as a potential therapeutic strategy for CML patients. In such treatment programs, imatinib treatment is temporarily stopped to stimulate and leverage the anti-leukemia immune response to combat CML. The simulations presented in our previous work demonstrate that such treatment interruption programs may prevent leukemia from relapsing for significantly longer than continuous imatinib treatments. In this project, students will expand and improve this mathematical model by incorporating the possibility of acquired imatinib resistance. Drug resistance has been an ongoing obstacle to successful treatment of many diseases, and there are numerous existing mathematical models for acquired drug resistance. This project involves adding the possibility of drug resistance into the existing model for strategic treatment interruptions of CML, simulating the model using Matlab or a similar program, and analyzing the simulation results. This project will be an excellent fit for students interested in applied mathematics and/or mathematical biology. While no specific Matlab experience is required, it will be useful to have some experience with C, Mathematica, etc. The project may also involve some data-fitting, so experience with or interest in statistics is also a plus.