1. Modeling the Effect of Melanoma Tumor Cell Growth in the Presence of Natural Killer Cells
Ann Wells
University of Tennessee-Knoxville
Kara Kruse, M.S.E.
Richard C. Ward, Ph.D.
Computational Sciences and Engineering Division
Sharon Bewick, Ph.D.
August 2009
Cray Confidential Cray Proprietary

2. Overview
Introduction
Significance
Methodology
The System
Results
Conclusion
Future work
Acknowledgments

3. Introduction Cancer 2nd leading cause of death
Tumor cells often evade the immune system and immune cells may evade tumor cells
Many immune cells have tumor cell lysis potential
Natural Killer (NK) cells show tumor cell lysis potential
Create a mathematical model showing the interactions between NK cells and tumor cells
Create a theoretical model that simulates the effects of natural killer cells on tumor cells
Tumor cells often evade the immune system and continue to proliferate. Once this happens metastases becomes a problem
It is not understood why tumors are able to evade the immune system
People are now trying to understand why the immune system can’t handle tumor cells
Natural killer cells show potential to kill tumors

4. Significance Predict behavior of the NK cell
Predict behavior of the tumor cell
Predict interaction between immune cells and tumor cells
Create new ways to treat cancer
Immunotherapy
Less invasive
This is important research because it tells us more about the wat tumor cells evade the immune system as well as why immune cells are at a disadvantage. Immunotherapies may also be developed from this research.

5. Methodology Searched for experimental data in previous literature
Biochemical reactions
Important receptors
Created a series of continuous PDEs
Simulate interaction between NK cells and tumor cells
Show effects of matrix metalloproteinases (MMPs) on receptors, Extracellular Matrix (ECM) and NKG2D receptor
Used MATLAB and Systems Biology Workbench

6. Important players in the system
NK cells
Tumor Cells
Matrix Metalloproteinases (MMPs)
MICA
Cytokines
Extracellular matrix

7. Natural Killer Cells Large granular lymphocytes
Part of the innate immune system
Contains receptors and ligands that bind to foreign substances
NKp30, NKp44, NKp46 and C-type lectin receptor, NKG2D
Does not require prior sensitization
Shows tumor cell lysis potential

8. Other Players ECM Cytokines Provides structural support to cells
Usually made from collagen and/or fibronectin
Blocks tumor from NK cells
Cytokines
Signaling molecules
Proteins, peptides or glycoproteins
Tumor cells secrete cytokines which act as chemoattractants for NK cells

9. Other Players Matrix Metalloproteinases (MMP) Ligand
Degrades extracellular matrix proteins
Known to cleave cell surface receptors
Secreted by NK cells
Allows NK cells to migrate
Ligand
Binds substances together to create complexes
Causes conformational changes
MICA is a ligand and binds to NKG2D receptor when soluble

10. Biological interactions
NK cells that express NKG2D recognize and kill tumor cells
Tumor cells that come in contact with MMPs shed MICA making them invisible
Soluble MICA can bind to NKG2D receptor, down-regulating surface density of NKG2D receptors
NK cell

11. Biological interactions cont.
NK cells follow a tumor secreted cytokine gradient
The ECM surrounds tumor cells creating a barrier preventing NK cells from invading the tumor
NK cells can breakdown ECM through the secretion of MMPs

12. Steps to creating model
1. Enter biochemical reactions into Systems Biology Workbench (SBW)
2. Check ordinary differential equations (ODEs) created from SBW
3. Add spatial component to ODEs to create partial differential equations (PDEs)
4. Write Matlab code for solving PDEs
5. Verify model with experimental data

13. Biochemical Reactions
Figure 1: Output from Systems Biology Workbench

14. Partial Differential Equations

15. Results A
Figure 2: Matlab graph depicting results for high concentration of MMP input

16. Results B
Figure 3: Matlab graph depicting results for normal concentration of MMP input

17. Results C
Figure 4: Matlab graph depicting results for low concentration of MMP input

18. Conclusion Numerical solution gives expected behavior for this model
Change in MMP concentration effects behavior of system

19. Future Work Design experiments to run based on mathematical model
Revise and expand mathematical model based on experimental results

20. References
Zwirner, N. W., M. B. Fuertes, M. V. Girart, C. I. Domaica, L. E. Rossi. Cytokine-driven regulation of NK cell functions in tumor immunity: Role of the MICA-NKG2D system. Cytokine and Growth Factors Reviews. 18 (2007)
Kuppen, P. J. K., M. M. van der Eb, L. E. Jonges, M. Hagenaars, M. E. Hokland, U. Nannmark, R. H. Goldfarb, P. H. Basse, G. Jan Fleuren, R. C. Hoeben, C. J.H. van de Velde. Tumor structure and extracellular matrix as a possible barrier for therapeutic approaches using immune cells or adenoviruses in colorectal cancer. Histochemistry Cell Biology (2001)

21. Acknowledgements Thanks to: Kara Kruse, M.S.E. Richard Ward, Ph.D.
Sharon Bewick, Ph.D.
John Biggerstaff, Ph.D.
Debbie McCoy
ORNL
UT-Batelle
Department of Energy

22. Questions

23. Photo Citations
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