Denise Dengi L&S Math & Physical Sciences
Predictive mathematical models of combination immunotherapy for cancer
The use of immune checkpoint inhibitors (ICIs), which reduce the ability of tumor cells to escape immune detection, has become more common in clinical trials for cancer treatment and increases survival rates across many types of cancer. Although monotherapy with ICIs induces tumor regression in some cases, combined treatment using an ICI and another treatment has been shown to be much more effective. Mathematical modeling has proven to be a very useful tool in providing insights and predictions in cancer research, and more specifically in understanding and improving treatments. Our research asks how we can improve upon existing mathematical tumor-immune models consisting of ordinary differential equations (ODEs) to investigate combined treatment of cancers. We will examine treatment using the ICI avelumab and the immunostimulant NHS-mulL 12 with a simple continuous ODE tumor-immune model and validate this model using available data. Formulating a simple mathematical model which better captures the biological dynamics of tumor-immune response in the presence of combined treatments will ultimately allow for the development of more effective cancer treatments.
Message To Sponsor
I am incredibly grateful to my sponsor for giving me the opportunity to pursue my passion for mathematical oncology through the SURF L&S program. I am eager to expand my mathematical modeling skills and to hopefully make a modest contribution to the development of combined immunotherapy treatments for cancer. Thanks to this sponsorship, I will be one step closer to achieving my career goals and my pursuit of a PhD in applied mathematics with a focus in biomedical science and oncology.