modeling the effect of melanoma tumor cell growth in the presence of natural killer cells
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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. University of Tennessee-Knoxville - PowerPoint PPT PresentationTRANSCRIPT
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.University of Tennessee-Knoxville
August 2009
2 Managed by UT-Battellefor the U.S. Department of Energy
Overview
• Introduction
• Significance
• Methodology
• The System
• Results
• Conclusion
• Future work
• Acknowledgments
3 Managed by UT-Battellefor the U.S. Department of Energy
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
4 Managed by UT-Battellefor the U.S. Department of Energy
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
5 Managed by UT-Battellefor the U.S. Department of Energy
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
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Important players in the system
• NK cells
• Tumor Cells
• Matrix Metalloproteinases (MMPs)
• MICA
• Cytokines
• Extracellular matrix
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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
http://www.iayork.com/Images/2008/2-25-08/NKTumorCell.jpg
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Other Players
• ECM– 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
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Other Players
• Matrix Metalloproteinases (MMP)– 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
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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
http://www.immunesupportonline.com/Images/lymphocyte%20and%20nutrophil.jpg
http://myhealth.ucsd.edu/library/healthguide/en-us/images/media/medical/hw/n5550299.jpg
NK cell
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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
http://sciencecodex.com/files/sciencecodex-oZsXUwc08Fbda46Z.jpg
12 Managed by UT-Battellefor the U.S. Department of Energy
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
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Biochemical Reactions
Figure 1: Output from Systems Biology Workbench
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Partial Differential Equations
MMPDNKpNKRpTMICAMMPkTMICAMMPkt
MMPmmpBR
233
TumorGTumorECMDTMICAMMPkt
TumorTT
2
6 )(
)()(2)(5
2211
TCNKRECMNKRNKRECMNKRDTMICANKRk
TMICANKRkTMICANKRkSMICANKRkSMICANKRkt
NKR
sMICADTMICAMMPkSMICANKRkSMICANKRkt
SMICAsMICA
2611
)()()( 24 TNKNK CNKECMNKECMDSMICANKRk
t
NK
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Results
AA
Figure 2: Matlab graph depicting results for high concentration of MMP input
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Results
BB
Figure 3: Matlab graph depicting results for normal concentration of MMP input
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Results
CC
Figure 4: Matlab graph depicting results for low concentration of MMP input
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Conclusion
• Numerical solution gives expected behavior for this model
• Change in MMP concentration effects behavior of system
http://www.drugabuse.gov/NIDA_notes/NNvol21N6/killercell.jpg
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Future Work
• Design experiments to run based on mathematical model
• Revise and expand mathematical model based on experimental results
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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. 115 (2001)
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Acknowledgements
• 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
Thanks to: