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High School Student with a Traditionally Underrepresented Background

HS Poster

Poster 140 - Sensitivity Analysis of Adoptive Immunotherapy Model for Leukemia

Saturday, October 26, 2024
10:00 AM – 12:00 PM EST
Location: Exhibit Hall E, F & G

    Presenting Author(s)

  • Lillian Houle

    High School Student, Intern at UMN BME
    University of Minnesota Biomedical Engineering High School Internship Program
    Hugo, Minnesota, United States

    • Primary Investigator(s)

  • DO

    David J. Odde

    Professor
    University of Minnesota
    Minneapolis, Minnesota, United States


Introduction: Adoptive immunotherapy has been successful at treating acute lymphocytic leukemias. Specifically, T-cells that have been modified to express synthetic chimeric antigen receptors (CARS), are able to reprogram themselves to recognize the CD19 cell surface marker on the cancer cells.
This leukemia is a result of proliferation of lymphoblasts that crowd out the healthy white blood cells.

The purpose of this study is to identify a procedure that uses ordinary differential equations (ODEs) to model the behavior of this therapy and identify the consequences of manipulated parameters.

Clinical implications and importance: Use of a mathematical model can reduce the need to undergo extensive testing. Parameters can be manipulated easily, and outcomes received instantly. Mathematical modeling can reduce the risk factor involved in clinical trials.

Focus Questions: How can the model describe the effect of adoptive immunotherapy? What happens when the parameters of the model are manipulated?

Materials and

Methods: Software used:
- MatlabR2024A
- Julia version 10.0
- BioRender

Ordinary Differential Equations:
1. 𝑑𝑇/𝑑𝑑=π‘Žπ‘‡ (1 βˆ’π‘π‘‡)βˆ’ 𝛾cET
2. 𝑑𝐸/𝑑𝑑=𝑠𝑒 βˆ’π‘‘π‘›π‘ βˆ’π‘˜π‘›π‘( 𝑇/(𝑇 + 𝑔) )βˆ’π›ΎeET
3. 𝑑𝑁/𝑑𝑑=𝑠𝑛 βˆ’π‘‘π‘›π‘ βˆ’π‘˜π‘›π‘(𝑇/(𝑇+𝑔))

Adjustments from the original model:
- Uses a logistic model instead of Gompertzian
-Logistic: Inflection point at 50%, linear growth rate that decreases with tumor size
-Gompertzian: Inflection point at 37%, exponential growth rate
- Assumes a saturating response, where tumor cells stimulate the saturating response which causes effector cell production

Nominal parameter values:
- Adapted from the original study to be more biologically accurate
- Initial values are based on the original paper [1]

Morris Method – Sensitivity Analysis:
- The output is the absolute value of the mean and the variance
- We used the area under the curve to calculate the influence of parameters

Results, Conclusions, and Discussions: - Sensitivity analyses demonstrate the carrying capacity of a body to tumor cells (b) and the rate of effector cell loss due to tumor cells (𝛾𝑒) as having the greatest impact on the model.

- When the carrying capacity of a body to tumor cells (b) is increased by a factor of ten, the area under the tumor cell curve decreases, but the time it takes to reduce tumor size is the same as the original model. When we increase the carrying capacity, the body can handle more tumor cells.
Although we might initially expect the tumor to be reduced slower because we assume a saturating response and the system is saturated at a slower rate, the effector and naΓ―ve T-cell rates of production are the same. This means there is no change in the rate of tumor cell death.

- When the rate of effector cell loss due to tumor cells (𝛾𝑒) is increased by a factor of ten, the area under the tumor cell curve increases. This is because as the body loses effector cells, its ability to fight the tumor cells decreases. Unchecked, the amount of tumor cells will continue to increase over the 30-day period.

Future Directions:
1. Manipulate multiple parameters at once and see what combination is most influential.
2. Adopt the model to function most efficiently in specific areas/organs of the body.
3. Find the set of parameters that will optimize the adoptive immunotherapy and the immune response of the body.
4. Experiment with different initial cell quantities using clinical data of leukemia patients and analyze the results.

Acknowledgements (Optional): Thank you to the Department of Biomedical Engineering at the University of Minnesota and the lab of David J. Odde for this opportunity. Special thanks to Nikolaos Memmos, Ali Mohammad, and Gabbi Horsford for your guidance throughout the program.

References (Optional): [1] Moore and Li, Model for CML and T Cell Interaction. 2004
[2] Talkington, Anne, et al., ODE Models Adopt. Imm. therapy. 2017
[3] Mayo Clinic. Acute Lymphocytic Leukemia. 2022
[4] Salter, Alexander I, et al., CAR Modified T Cells: CD19. 2018
[5] Moore, James R, et al. CD8 T Cell Antigen Load, Y. Fever. 2019