Ganguli2018-immuno regulatory mechanisms in tumor microenvironment

Model Identifier
BIOMD0000000810
Short description
This model describes the concept of Cancer Stem Cells(CSC) differentiation and tumor-immune interaction into a generic model that has been validated with known experimental data. Created by COPASI 4.24(Build197)
Abstract:
The tumor microenvironment comprising of the immune cells and cytokines acts as the 'soil' that nourishes a developing tumor. Lack of a comprehensive study of the interactions of this tumor microenvironment with the heterogeneous sub-population of tumor cells that arise from the differentiation of Cancer Stem Cells (CSC), i.e. the 'seed', has limited our understanding of the development of drug resistance and treatment failures in Cancer. Based on this seed and soil hypothesis, for the very first time, we have captured the concept of CSC differentiation and tumor-immune interaction into a generic model that has been validated with known experimental data. Using this model we report that as the CSC differentiation shifts from symmetric to asymmetric pattern, resistant cancer cells start accumulating in the tumor that makes it refractory to therapeutic interventions. Model analyses unveiled the presence of feedback loops that establish the dual role of M2 macrophages in regulating tumor proliferation. The study further revealed oscillations in the tumor sub-populations in the presence of TH1 derived IFN-γ that eliminates CSC; and the role of IL10 feedback in the regulation of TH1/TH2 ratio. These analyses expose important observations that are indicative of Cancer prognosis. Further, the model has been used for testing known treatment protocols to explore the reasons of failure of conventional treatment strategies and propose an improvised protocol that shows promising results in suppressing the proliferation of all the cellular sub-populations of the tumor and restoring a healthy TH1/TH2 ratio that assures better Cancer remission.
Format
SBML
(L2V4)
Related Publication
-
Exploring immuno-regulatory mechanisms in the tumor microenvironment: Model and design of protocols for cancer remission.
- Ganguli P, Sarkar RR
- PloS one , 1/ 2018 , Volume 13 , Issue 9 , pages: e0203030 , PubMed ID: 30183728
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, Maharashtra, India.
- The tumor microenvironment comprising of the immune cells and cytokines acts as the 'soil' that nourishes a developing tumor. Lack of a comprehensive study of the interactions of this tumor microenvironment with the heterogeneous sub-population of tumor cells that arise from the differentiation of Cancer Stem Cells (CSC), i.e. the 'seed', has limited our understanding of the development of drug resistance and treatment failures in Cancer. Based on this seed and soil hypothesis, for the very first time, we have captured the concept of CSC differentiation and tumor-immune interaction into a generic model that has been validated with known experimental data. Using this model we report that as the CSC differentiation shifts from symmetric to asymmetric pattern, resistant cancer cells start accumulating in the tumor that makes it refractory to therapeutic interventions. Model analyses unveiled the presence of feedback loops that establish the dual role of M2 macrophages in regulating tumor proliferation. The study further revealed oscillations in the tumor sub-populations in the presence of TH1 derived IFN-γ that eliminates CSC; and the role of IL10 feedback in the regulation of TH1/TH2 ratio. These analyses expose important observations that are indicative of Cancer prognosis. Further, the model has been used for testing known treatment protocols to explore the reasons of failure of conventional treatment strategies and propose an improvised protocol that shows promising results in suppressing the proliferation of all the cellular sub-populations of the tumor and restoring a healthy TH1/TH2 ratio that assures better Cancer remission.
Contributors
Submitter of the first revision: Szeyi Ng
Submitter of this revision: Szeyi Ng
Modellers: Szeyi Ng
Submitter of this revision: Szeyi Ng
Modellers: Szeyi Ng
Metadata information
is (2 statements)
isDescribedBy (1 statement)
hasProperty (5 statements)
isPropertyOf (1 statement)
hasTaxon (1 statement)
isDescribedBy (1 statement)
hasProperty (5 statements)
Gene Ontology
regulation of immune response to tumor cell
Human Disease Ontology cancer
Experimental Factor Ontology cancer
Mathematical Modelling Ontology Ordinary differential equation model
NCIt Immunotherapy
Human Disease Ontology cancer
Experimental Factor Ontology cancer
Mathematical Modelling Ontology Ordinary differential equation model
NCIt Immunotherapy
isPropertyOf (1 statement)
hasTaxon (1 statement)
Curation status
Curated
Modelling approach(es)
Tags
Connected external resources
Name | Description | Size | Actions |
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Model files |
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Ganguli2018-immuno regulatory mechanisms in tumor microenvironment.xml | SBML L2V4 file for the model | 273.52 KB | Preview | Download |
Additional files |
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Ganguli2018-immuno regulatory mechanisms in tumor microenvironment.cps | COPASI 4.24 (Build 197) file for the model | 295.94 KB | Preview | Download |
Ganguli2018-immuno regulatory mechanisms in tumor microenvironment.sedml | Sedml L1V2 file producing figure 2 | 10.29 KB | Preview | Download |
reproduced figure.PNG | PNG plot of the model simulation | 490.53 KB | Preview | Download |
- Model originally submitted by : Szeyi Ng
- Submitted: Sep 11, 2019 2:22:41 PM
- Last Modified: Sep 11, 2019 2:46:08 PM
Revisions
-
Version: 5
- Submitted on: Sep 11, 2019 2:46:08 PM
- Submitted by: Szeyi Ng
- With comment: Edited model metadata online.
-
Version: 3
- Submitted on: Sep 11, 2019 2:22:41 PM
- Submitted by: Szeyi Ng
- With comment: Automatically added model identifier BIOMD0000000810
(*) You might be seeing discontinuous
revisions as only public revisions are displayed here. Any private revisions
of this model will only be shown to the submitter and their collaborators.
Legends
: Variable used inside SBML models
: Variable used inside SBML models
Species
Species | Initial Concentration/Amount |
---|---|
M1 Tumor Associated Macrophages Tumor-Associated Macrophage ; M1 Macrophage |
85000.0 mmol |
Resistant Stem Cells S R Drug Resistance Status ; Cancer Stem Cell |
0.0 mmol |
Cancer Cells C Malignant Cell |
0.0 mmol |
Resistant Cancer Cells C R Malignant Cell ; Drug Resistance Status |
0.0 mmol |
Type I T helper Cell T H1 T-helper cell type 1 ; T-helper 1 cell differentiation |
71000.0 mmol |
Cytokine IL10 Cytokine ; Interleukin-10 |
0.0085 mmol |
Cytokine IL2 Cytokine ; Interleukin-2 |
0.0094 mmol |
Cancer Stem Cells S Cancer Stem Cell |
1.0 mmol |
Cytotoxic T Cells T C Cytotoxic and Regulatory T-Cell Molecule |
56000.0 mmol |
Reactions
Reactions | Rate | Parameters |
---|---|---|
M1_Tumor_Associated_Macrophages => | compartment*delta_M1*M1_Tumor_Associated_Macrophages | delta_M1 = 1.02 1/d |
Resistant_Stem_Cells_S_R => | compartment*delta_S*Resistant_Stem_Cells_S_R | delta_S = 2.0E-7 1/d |
Resistant_Stem_Cells_S_R => Resistant_Cancer_Cells_C_R | compartment*p_2*gamma_S*Resistant_Stem_Cells_S_R | p_2 = 0.05; gamma_S = 0.15 1/d |
Cancer_Stem_Cells_S => Cancer_Cells_C | compartment*p_2*gamma_S*Cancer_Stem_Cells_S | p_2 = 0.05; gamma_S = 0.15 1/d |
Resistant_Stem_Cells_S_R => Resistant_Stem_Cells_S_R + Resistant_Cancer_Cells_C_R | compartment*p_1*gamma_S*Resistant_Stem_Cells_S_R | p_1 = 0.2; gamma_S = 0.15 1/d |
Type_I_T_helper_Cell_T_H1 => | compartment*delta_Th1*Type_I_T_helper_Cell_T_H1 | delta_Th1 = 2.0 1/d |
=> Cytokine_IL10; M2_Tumor_Associated_Macrophages | compartment*beta_M2*M2_Tumor_Associated_Macrophages | beta_M2 = 1.0E-15 1/d |
Cytokine_IL2 => | compartment*delta_Ck3*Cytokine_IL2 | delta_Ck3 = 8.664339 1/d |
Cancer_Stem_Cells_S => | compartment*delta_S*Cancer_Stem_Cells_S | delta_S = 2.0E-7 1/d |
Cytotoxic_T_Cells_T_C => ; Cancer_Stem_Cells_S, Resistant_Stem_Cells_S_R | compartment*myu_TcS*Cytotoxic_T_Cells_T_C*(Cancer_Stem_Cells_S+Resistant_Stem_Cells_S_R)/(Cytotoxic_T_Cells_T_C+lambda_Tc2) | myu_TcS = 1.0E-10 1/d; lambda_Tc2 = 500000.0 1/ml |
Curator's comment:
(added: 11 Sep 2019, 14:21:14, updated: 11 Sep 2019, 14:21:14)
(added: 11 Sep 2019, 14:21:14, updated: 11 Sep 2019, 14:21:14)
Fig 2(a) and 2(c) are reproduced and shown here.
To reproduce Fig 2(A), I used COPASI and any file attached would work, but I need to change gamma_S=2 1/day, delta_S=0.2 1/day, as the author described in the paper. Set the duration to 5 days.
To reproduce Fig 2(C), I used COPASI and any file attached would work, there is no need to change any parameters. Set the duration to 50 or 800 to reproduce middle left, or bottom pictures.