Background 1 Jose Perez 1 , Meghan Bloom 2 , Marcelo Behar 2 1 The University of Texas at El Paso 2 Cellular Sensing and Communication Dynamics Research Group, Biomedical Engineering, The University of Texas at Austin Multi-Scale Modeling of T Cell and Antigen Presenting Cell Interaction in the Tumor Microenvironment Conclusions • Model recapitulates basic interactions between T Cells and APCs • Ligand competition between CTLA-4 and CD28 receptors • CTLA-4 recycling intracellular process • Cell movement and T Cell co-activation extracellular processes • Model sets a basis for development • More complex intracellular and extracellular processes required for immunotherapy design Abstract The impact cancer has on the world today is very significant and costly. Out of the current treatments for cancer one of particular promise is immunotherapy. However, a large fraction of cancer patients is still unresponsive to immunotherapies. This is partly due to the fact that every patient is different and tumor microenvironments are very diverse. There is therefore a need for predictive tools suitable for adjusting treatments to individual patient’s microenvironments. To this end we implement a computational model of immune cell interactions including cell types and molecular processes relevant for cancer immunotherapy. Ultimately, the model will enable clinicians to test therapies and dosages to define optimal treatment plans for individual patients. Results (Continued) Multi-Scale Model Processes Selection Methodology Results Acknowledgments Research reported in this poster was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under linked Award Numbers RL5GM118969, TL4GM118971, and UL1GM118970. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Doctor gathers data from patient Model is adjusted for individual patient Treatment plans are selected Model simulates treatment s Treatment s are compared Optimal treatment is proposed • Cancer is a systemic disease that influences and is influenced by the immune system. • Immunotherapy is a type of cancer treatment that helps the immune system fight cancer. (National Cancer Institute). • One form of T Cell immunotherapy is checkpoint-blocking. 1 • Immune checkpoint molecules are used by tumors to suppress and evade attacks from the immune system. 1 • Checkpoint blocking therapies seek to prevent this suppression of immune activity. 1 • Interactions between T Cells and Antigen Presenting Cells in the tumor microenvironment are relevant for this therapy. • Model begins by implementing some of these interactions. 1 15 29 43 57 71 85 99 113127141155169183197211225239 0 1000 2000 3000 4000 CD28 and CTLA-4 Receptor Engagement CD28 CTLA-4 Time (Monte Carlo Steps) Ligand Engagements (Total) Figure 2. Movement of cells from initial arrangement after 7 MCS. Cells have scattered around their origin and began interacting. Figure 3. State of cells after a usual simulation of 200 MCS. Activated T Cells from the co- activation process are present. Legend APC Naïve Treg Active Treg Anergic Treg Naïve Tconv Active Tconv Anergic Tconv Active Treg APC Active Tconv Naive Treg APC T Cell T Cell co-activation process (Intercellular) * CTLA-4 recycling process (Intracellular) CD80 Ligand s Peptide-MHC CD86 CD28 TCR CTLA-4 CTLA-4 Receptor s Figure 1. Multi-scale interaction comprising intercellular and intracellular processes. CTLA-4 is a key player of immune checkpoint- blocking therapy. *T cell activation involves at least two signals: one via engagement of the T cell receptor (TCR) and another through a co-receptor. CTLA-4 is internalized CTLA-4 * CTLA-4 * CTLA-4 is recycled CTLA-4 * Inside of T cell Figure 4. CTLA-4 recycling can be observed by the inverse relationship between internal and external CTLA-4 over time. 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 0 1000 2000 T Cell Receptors Regulation Internal CTLA-4 External CTLA-4 Time (Monte Carlo Step) Amount (AU) Figure 5. Simulation data which recapitulates CTLA-4 has a higher affinity to bind compared to CD28 and competes for ligand engagement 2. References Extracellular • Movement • T Cell Activation Intracellular • CTLA-4 Recycling Multi-Scale Model Processes Selection Extracellular • Agent-based approach • CompuCell3D modeling environment Intracellular • Biochemical system simulated as systems of Ordinary Differential Equations • BioNetGen rule-based environment Modeling Approach Movement • T Cells move at a rate of ~0.75um/min while APCs move at ~0.1um/min • Scale by 1 pixel = 1 um • Move cells pseudorandomly (APCs secrete chemical to attract T Cells) CTLA-4 Recycling • Mass-action kinetics equations Model Implementation T Cell Activation • T Cell activated by co-activation process and by CD28 engagement passing a threshold • Regulatory T Cells (Tregs) and Conventional T Cells (Tconvs) simulated • Tregs have both CD28 and CTLA-4 surface expression • Tconvs only express surface CTLA-4 when activated Model Integration Run inter- and intra- cellular models sequentially Biochemical model updated 10 times every Monte Carlo step Simulations typically run for 200 model hours : − 4 1 → − 4 ∗ : − 4 ∗ 2 → −4 1. Postow, M. A., Callahan, M. K. & Wolchok, J. D. Immune Checkpoint Blockade in Cancer Therapy. J. Clin. Oncol. JCO.2014.59.4358 (2015). doi:10.1200/JCO.2014.59.4358 2. Walker, L. S. K. & Sansom, D. M. Confusing signals: Recent progress in CTLA-4 biology. Trends Immunol. 36, 63–70 (2015).
Multi-Scale Modeling of T Cell and Antigen Presenting Cell Interaction in the Tumor Microenvironment
Apr 12, 2017
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Background
1
Jose Perez1, Meghan Bloom2, Marcelo Behar2
1 The University of Texas at El Paso2 Cellular Sensing and Communication Dynamics Research Group, Biomedical Engineering, The University of Texas at Austin
Multi-Scale Modeling of T Cell and Antigen Presenting Cell Interaction in the Tumor Microenvironment
Conclusions• Model recapitulates basic interactions between T Cells and APCs
• Ligand competition between CTLA-4 and CD28 receptors
• CTLA-4 recycling intracellular process
• Cell movement and T Cell co-activation extracellular processes
• Model sets a basis for development
• More complex intracellular and extracellular processes required for immunotherapy design
AbstractThe impact cancer has on the world today is very significant and costly. Out of the current treatments for cancer one of particular promise is immunotherapy. However, a large fraction of cancer patients is still unresponsive to immunotherapies. This is partly due to the fact that every patient is different and tumor microenvironments are very diverse. There is therefore a need for predictive tools suitable for adjusting treatments to individual patient’s microenvironments.
To this end we implement a computational model of immune cell interactions including cell types and molecular processes relevant for cancer immunotherapy. Ultimately, the model will enable clinicians to test therapies and dosages to define optimal treatment plans for individual patients.
Results (Continued)Multi-Scale Model Processes Selection
Methodology
Results
AcknowledgmentsResearch reported in this poster was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under linked Award Numbers RL5GM118969, TL4GM118971, and UL1GM118970. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Doctor gathers data from patient
Model is adjusted for
individual patient
Treatment plans are selected
Model simulates
treatments
Treatments are compared
Optimal treatment is
proposed
• Cancer is a systemic disease that influences and is influenced by the immune system.
• Immunotherapy is a type of cancer treatment that helps the immune system fight cancer. (National Cancer Institute).
• One form of T Cell immunotherapy is checkpoint-blocking.1 • Immune checkpoint molecules are used by tumors to suppress and
evade attacks from the immune system.1 • Checkpoint blocking therapies seek to prevent this suppression of
immune activity.1
• Interactions between T Cells and Antigen Presenting Cells in the tumor microenvironment are relevant for this therapy.
• Model begins by implementing some of these interactions.
1 12 23 34 45 56 67 78 89 1001111221331441551661771881992102212322430
1000
2000
3000
4000CD28 and CTLA-4 Receptor Engagement
CD28 CTLA-4
Time (Monte Carlo Steps)
Liga
nd E
ngag
emen
ts (T
otal
)
Figure 2. Movement of cells from initial arrangement after 7 MCS. Cells have scattered around their origin and began interacting.
Figure 3. State of cells after a usual simulation of 200 MCS. Activated T Cells from the co-activation process are present.
Legend
APCNaïve Treg
Active Treg
Anergic Treg
Naïve Tconv
Active Tconv
Anergic Tconv
Active TregAPC
Active Tconv
Naive Treg
APC T Cell
T Cell co-activation process (Intercellular)* CTLA-4 recycling process (Intracellular)
CD80Ligands
Peptide-MHC
CD86 CD28TCR
CTLA-4
CTLA-4
Receptors
Figure 1. Multi-scale interaction comprising intercellular and intracellular processes. CTLA-4 is a key player of immune checkpoint-blocking therapy.*T cell activation involves at least two signals: one via engagement of the T cell receptor (TCR) and another through a co-receptor.
CTLA-4 is internalized CTLA-4*
CTLA-4*
CTLA-4 is recycled CTLA-4*
Inside of T cell
Figure 4. CTLA-4 recycling can be observed by the inverse relationship between internal and external CTLA-4 over time.
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 1060500
100015002000
T Cell Receptors Regulation
Internal CTLA-4 External CTLA-4
Time (Monte Carlo Step)
Amou
nt (A
U)
Figure 5. Simulation data which recapitulates CTLA-4 has a higher affinity to bind compared to CD28 and competes for ligand engagement2.
References
Extracellular• Movement• T Cell Activation
Intracellular• CTLA-4 Recycling
Multi-Scale Model Processes Selection
Extracellular• Agent-based approach• CompuCell3D modeling environment
Intracellular• Biochemical system simulated as systems of Ordinary
Differential Equations• BioNetGen rule-based environment
Modeling Approach
Movement•T Cells move at a rate of ~0.75um/min while APCs move at ~0.1um/min
•Scale by 1 pixel = 1 um•Move cells pseudorandomly (APCs secrete chemical to attract T Cells)
CTLA-4 Recycling• Mass-action kinetics equations
Model Implementation
T Cell Activation•T Cell activated by co-activation process and by CD28 engagement passing a threshold•Regulatory T Cells (Tregs) and Conventional T Cells (Tconvs) simulated•Tregs have both CD28 and CTLA-4 surface expression•Tconvs only express surface CTLA-4 when activated
Model IntegrationRun inter- and intra-cellular
models sequentially
Biochemical model updated 10 times every Monte Carlo
step
Simulations typically run for 200 model hours
𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 : 𝑅𝐶𝑇𝐿𝐴− 4𝑘1→𝑅𝐶𝑇𝐿𝐴 −4
∗
𝑅𝑒𝑐𝑦𝑐𝑙𝑖𝑛𝑔 :𝑅𝐶𝑇𝐿𝐴−4∗ 𝑘2
→𝑅𝐶𝑇𝐿𝐴− 4
1. Postow, M. A., Callahan, M. K. & Wolchok, J. D. Immune Checkpoint Blockade in Cancer Therapy. J. Clin. Oncol. JCO.2014.59.4358 (2015). doi:10.1200/JCO.2014.59.4358
2. Walker, L. S. K. & Sansom, D. M. Confusing signals: Recent progress in CTLA-4 biology. Trends Immunol. 36, 63–70 (2015).
Related Documents
