Ribba2012 - Low-grade gliomas, tumour growth inhibition model

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Short description
Ribba2012 - Low-grade gliomas, tumour growth inhibition model

Using longitudinal mean tumour diameter (MTD) data, this model describe the size evolution of low-grade glioma (LGG) in patients treated with chemotherapy or radiotherapy.

This model is described in the article:

Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F.
Clin. Cancer Res. 2012 Sep; 18(18): 5071-5080

Abstract:

PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy.

EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy.

RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data.

CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Format
SBML (L2V4)
Related Publication
  • A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy.
  • Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F
  • Clinical cancer research : an official journal of the American Association for Cancer Research , 9/ 2012 , Volume 18 , pages: 5071-5080
  • Ribba, INRIA, Project-team NUMED, Ecole Normale Superieure de Lyon, 46 allee d0Italie, 69007 Lyon Cedex 07, France. benjamin.ribba@inria.fr
  • PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy. EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy. RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data. CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules.
Contributors
Vijayalakshmi Chelliah

Metadata information

is
BioModels Database MODEL1402250000
BioModels Database BIOMD0000000521
isDescribedBy
PubMed 22761472
hasTaxon
Taxonomy Homo sapiens
isVersionOf
Gene Ontology defense response to tumor cell
Human Disease Ontology benign glioma
hasProperty
Mathematical Modelling Ontology Ordinary differential equation model
Curation status
Curated
  • Model originally submitted by : Vijayalakshmi Chelliah
  • Submitted: Feb 25, 2014 11:43:46 AM
  • Last Modified: Oct 9, 2014 6:37:21 PM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Oct 9, 2014 6:37:21 PM
    • Submitted by: Vijayalakshmi Chelliah
    • With comment: Current version of Ribba2012 - Low-grade gliomas, tumour growth inhibition model
  • Version: 1 public model Download this version
    • Submitted on: Feb 25, 2014 11:43:46 AM
    • Submitted by: Vijayalakshmi Chelliah
    • With comment: Original import of Ribba2012 - Low-grade gliomas, tumour growth inhibition model
Curator's comment:
(added: 03 Mar 2014, 11:31:48, updated: 03 Mar 2014, 11:31:48)
Population prediction (Figure 4 - top right - dashed line) based on the mean parameter values for PCV (Table 1) treatment is reproduced here. The model was simulated using Copasi v4.11 (Build 64). The plot was generated using Gnuplot. Note: Parameter values corresponding to individual subjects can be obtained on request (to biomodels-cura@ebi.ac.uk).