Ganguli2018-immuno regulatory mechanisms in tumor microenvironment

  public model
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

Metadata information

is (2 statements)
BioModels Database MODEL1909110001
BioModels Database BIOMD0000000810

isDescribedBy (1 statement)
PubMed 30183728

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

isPropertyOf (1 statement)
hasTaxon (1 statement)
Human Disease Ontology breast cancer

Curation status
Curated
Modelling approach(es)
ordinary differential equation model
Tags
Immuno-oncology

Connected external resources

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Name Description Size Actions

Model files
Ganguli2018-immuno regulatory mechanisms in tumor microenvironment.xml SBML L2V4 file for the model 273.52 KB Preview | Download

Additional files
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 public model Download this version
    • Submitted on: Sep 11, 2019 2:46:08 PM
    • Submitted by: Szeyi Ng
    • With comment: Edited model metadata online.
  • Version: 3 public model Download this version
    • 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 unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: 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)
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.