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Mathematical Model of a Personalized Cancer Vaccine and the Human Immune System: Evaluation of Efficacy

Quantitative Biology Colloquium

Mathematical Model of a Personalized Cancer Vaccine and the Human Immune System: Evaluation of Efficacy
Series: Quantitative Biology Colloquium
Location: ONLINE
Presenter: Marisabel Rodriguez, Office of Biostatistics and Epidemiology, FDA

Cancer vaccines are a novel immunotherapy, enhancing the immune response to malignant cells by activating CD4 + and CD8 + T-cells. In this work, we developed a mathematical model of nonlinear ordinary differential equations to describe key interactions of a personalized neoantigen cancer vaccine with the immune system of an individual patient. We quantify the effect of a personalized, peptide-based neoantigen cancer vaccine on the CD4 + and CD8 + T-cell species and tumor size. This model was calibrated using patient-specific data from a neoantigen peptide vaccine for anti-melanoma clinical trial. Model parameters estimated through model fitting describe the activation of naïve T-cells, and the killing and proliferation interactions between activated T-cells and tumor cells. The model predicts the clinical outcome of patients from a clinical trial and simulate their observed CD4 + and CD8 + T-cells response over time. Based on sampled initial tumor burden of a patient, the model predicts the ‘best’ clinical outcome of a personalized neoantigen peptide vaccine. Some model parameters were identified to be important through global sensitivity analysis such as proliferation rate of activated T-cells, which has been shown to be a favorable prognostic sign and may help determine efficacy of the immunotherapy. Our model has the potential to lay the foundation for generating in silico clinical trial data and aid in the development and efficacy assessment of personalized cancer vaccines.

Bio: I obtained my Ph.D. in Applied Mathematics from Arizona State University in 2018. Upon completing my Ph.D., I took a one year visiting lecturer position in the Department of Mathematics at Dartmouth College in Hanover, NH. After that, I joined the Data Science Initiative center at Brown University, where I started a postdoctoral position. In December 2019, I joined the Analytics and Benefit-Risk Assessment Team under the Office of Biostatistics and Epidemiology in the Center of Biologics Evaluation and Research at FDA as an ORISE Research Fellow, where I have been mainly working on developing a computational tool that can aid the development and efficacy assessment of personalized cancer vaccines. 

Place: Zoom:  https://asu.zoom.us/j/85049043960