Tsur2019 - Response of patients with melanoma to immune checkpoint blockade

  public model
Model Identifier
BIOMD0000000838
Short description
This is a simple mathematical population model for pembrolizumab-treated advanced melanoma patients, used to predict the response of melanoma patients to immune checkpoint inhibitors.
Format
SBML (L2V4)
Related Publication
  • Response of patients with melanoma to immune checkpoint blockade¬†- insights gleaned from analysis of a new mathematical mechanistic model.
  • Tsur N, Kogan Y, Rehm M, Agur Z
  • Journal of theoretical biology , 9/ 2019 , Volume 485 , pages: 110033 , PubMed ID: 31580835
  • Optimata Ltd., Zichron Ya'akov St., 20, Tel Aviv 6299920, Israel.
  • Immune checkpoint inhibitors (ICI) are becoming widely used in the treatment of metastatic melanoma. However, the ability to predict the patient's benefit from these therapeutics remains an unmet clinical need. Mathematical models that predict melanoma patients' response to ICI can contribute to better informed clinical decisions. Here, we developed a simple mathematical population model for pembrolizumab-treated advanced melanoma patients, and analyzed the local and global dynamics of the system. Our results show that zero, one, or two steady states of the mathematical system exist in the phase plane, depending on the parameter values of individual patients. Without treatment, the simulated tumors grew uncontrollably. At increased efficacy of the immune system, e.g., due to immunotherapy, two steady states were found, one leading to uncontrollable tumor growth, and the other resulting in tumor size stabilization. Model analysis indicates that a sufficient increase in the activation of CD8+ T cells results in stable disease, whereas a significant reduction in T-cell exhaustion, another process contributing CD8+ T cell activity, temporarily reduces the tumor mass, but fails to control disease progression in the long run. Importantly, the initial tumor burden influences the response to treatment: small tumors respond better to treatment than larger tumors. In conclusion, our model suggests that disease progression and response to ICI depend on the ratio between activation and exhaustion rates of CD8+ T cells. The analysis of the model provides a foundation for the use of computational methods to personalize immunotherapy.
Contributors
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Rahuman Sheriff
Modellers: Rahuman Sheriff, Johannes Meyer

Metadata information

is (2 statements)
BioModels Database MODEL1910250002
BioModels Database BIOMD0000000838

isDescribedBy (1 statement)
PubMed 31580835

hasProperty (3 statements)
Mathematical Modelling Ontology Ordinary differential equation model
Experimental Factor Ontology melanoma
NCIt Immune Checkpoint Modulator


Curation status
Curated



Connected external resources

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Name Description Size Actions

Model files

Tsur2019.xml SBML L2V4 Representation of Tsur2019 - Response of patients with melanoma to immune checkpoint blockade 32.14 KB Preview | Download

Additional files

Tsur2019.cps COPASI file of Tsur2019 - Response of patients with melanoma to immune checkpoint blockade 58.07 KB Preview | Download
Tsur2019.sedml SED-ML file of Tsur2019 - Response of patients with melanoma to immune checkpoint blockade 2.67 KB Preview | Download

  • Model originally submitted by : Johannes Meyer
  • Submitted: Oct 25, 2019 2:35:27 PM
  • Last Modified: Oct 5, 2021 7:56:38 PM
Revisions
  • Version: 5 public model Download this version
    • Submitted on: Oct 5, 2021 7:56:38 PM
    • Submitted by: Rahuman Sheriff
    • With comment: Automatically added model identifier BIOMD0000000838
  • Version: 2 public model Download this version
    • Submitted on: Oct 25, 2019 2:35:27 PM
    • Submitted by: Johannes Meyer
    • With comment: Automatically added model identifier BIOMD0000000838

(*) 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
M

C36873
1000000.0 item
T

CD8-Positive T-Lymphocyte
6.60941730355407E7 item
A

trans-3-Hydroxycinnamate
14119.9020779221 item
Reactions
Reactions Rate Parameters
M => ; T compartment*nu_mel*T*M/(M+g) nu_mel = 0.1245; g = 6.01E7
T => compartment*mu_e*T mu_e = 0.1777
A => compartment*mu_a*A mu_a = 0.231
=> M compartment*gamma_mel*M gamma_mel = 0.04496
=> T; A compartment*alpha_e*A alpha_e = 831.8
=> A; M compartment*alpha_A*M/(M+b) b = 92330.0; alpha_A = 2986.0
Curator's comment:
(added: 25 Oct 2019, 14:35:16, updated: 25 Oct 2019, 14:35:16)
Reproduced plot of Figure 7A in the original publication. Model simulated and plot produced using COPASI 4.24 (Build 197)