Dataset Information

Babbs2012 - immunotherapy

ABSTRACT: The paper describes a simple model of tumor immunotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model Charles F Babbs Am J Cancer Res 2012;2(2):204-213 Abstract: The objective of this study was to create a clinically applicable mathematical model of immunotherapy for cancer and use it to explore differences between successful and unsuccessful treatment scenarios. The simplified predator-prey model includes four lumped parameters: tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling factor, λ; and immune cell half-life decay, μ. The predator-prey equations as functions of time, t, for nor- malized tumor cell numbers, y, (the prey) and immunocyte numbers, x, (the predators) are: dy/dt = gy – kx and dx/dt = λxy – μx. A parameter estimation procedure that capitalizes on available clinical data and the timing of clinically observable phenomena gives mid-range benchmarks for parameters representing the unstable equilibrium case in which the tumor neither grows nor shrinks. Departure from this equilibrium results in oscillations in tumor cell num- bers and in many cases complete elimination of the tumor. Several paradoxical phenomena are predicted, including increasing tumor cell numbers prior to a population crash, apparent cure with late recurrence, one or more cycles of tumor growth prior to eventual tumor elimination, and improved tumor killing with initially weaker immune parame- ters or smaller initial populations of immune cells. The model and the parameter estimation techniques are easily adapted to various human cancers that evoke an immune response. They may help clinicians understand and predict certain strange and unexpected effects in the world of tumor immunity and lead to the design of clinical trials to test improved treatment protocols for patients. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . 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.

SUBMITTER: Jinghao Men

PROVIDER: BIOMD0000000758 | BioModels | 2019-07-24

REPOSITORIES: BioModels

ACCESS DATA

Publications

Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model.

Babbs Charles F CF

American journal of cancer research 20120215 2

The objective of this study was to create a clinically applicable mathematical model of immunotherapy for cancer and use it to explore differences between successful and unsuccessful treatment scenarios. The simplified predator-prey model includes four lumped parameters: tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling factor, λ; and immune cell half-life decay, μ. The predator-prey equations as functions of time, t, for normalized tumor cell numbers, y, (the prey) ...[more]

PMID: 22432059

Similar Datasets

Project description:Immunotherapeutics represent highly promising agents with the potential to improve patient outcomes in a variety of cancer types. Unfortunately, single-agent immunotherapy has achieved limited clinical benefit to date in patients suffering from pancreatic ductal adenocarcinoma (PDAC). This may be due to the presence of a uniquely immunosuppressive tumor microenvironment (TME) present in PDACs, which creates a barrier to effective immune surveillance. Critical obstacles to immunotherapy in PDAC tumors include the dense desmoplastic stroma that acts as a barrier to T-cell infiltration and the high numbers of tumor-associated immunosuppressive cells. We have identified hyperactivated focal adhesion kinase (FAK) activity in neoplastic PDAC cells as a significant regulator of the fibrotic and immunosuppressive TME. We found that FAK activity was elevated in human PDAC tissues and correlates with high levels of fibrosis and poor CD8+ cytotoxic T-cell infiltration. Single-agent FAK inhibition (VS-4718) dramatically limited tumor progression, resulting in a doubling of survival in the p48-Cre/LSL-KrasG12D/p53Flox/+ (KPC) mouse model of human PDAC. This alteration in tumor progression was associated with dramatically reduced tumor fibrosis, decreased numbers of tumor-infiltrating immature myeloid cells and immunosuppressive macrophages. We postulated that these desirable effects of FAK inhibition on the TME might render PDAC tumors more sensitive to immunotherapy. Accordingly, we found that VS-4718 rendered the previously unresponsive KPC mouse model responsive to anti-PD1 and anti-CTLA4 antagonists leading to a nearly tripling of survival times. These data suggest that FAK inhibition increases immune surveillance by overcoming the fibrotic and immunosuppressive PDAC TME thus rendering tumors more responsive to immunotherapy. We treated KP orthotopic tumor-bearing mice with vehicle and FAK inhibitor (FAKi) for 14 days, then extracted total RNA from tumor tissues.

Project description:This is the reduced model described in the article: A synthetic Escherichia coli predator–prey ecosystem Balagaddé FK, Song H, Ozaki J, Collins CH, Barnet M, Arnold FH, Quake SR, You L.Mol Syst Biol. 2008;4:187. Epub 2008 Apr 15. PMID: 18414488; DOI:10.1038/msb.2008.24 Abstract: We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator–prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner. In the article the cell density is given in per 103 cells per microlitre. To evade a conversion factor in the SBML implementation, the unit for the cell densities was just left the same as for the AHLs A and A2 (nM). This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team. For more information see the terms of use. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Dataset Information

Babbs2012 - immunotherapy

Publications

Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure