Calibrating models of cancer invasion: parameter estimation using approximate Bayesian computation and gradient matching

Xiao, Yunchen; Thomas, Len; Chaplain, M. A. J.

doi:10.1098/rsos.202237

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…These distributions provide more detailed information should additional data be made available for a more precise data fitting. In recent years, the use of the ABC rejection method in the study of complex biological systems has been increasing (Browning et al (2017), Stepien et al (2019), Xiao et al (2021), da Costa et al (2018)).…”

Section: Parameter Analysismentioning

confidence: 99%

Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

Anderson

Takacs

Harris

et al. 2023

Preprint

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Glioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD- 1 and MDSCs, and the T cell death rate. We use the Approximate Bayesian Computation (ABC) rejection method to construct probability density distributions that estimate model parameters with a set of preclinical experimental data. These distributions inform an appropriate search curve for global sensitivity analysis using the extended Fourier Amplitude Sensitivity Test (eFAST). Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD- 1 immune checkpoint and MDSC suppression of T cells. Additionally, numerical simulations along with ABC results suggest that the activated T cell population could be maximized by targeting the immune suppression by the PD-L1-PD1 complex and MDSCs. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting MDSCs, such as a CCR2 antagonist, should be explored.

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Section: Parameter Analysismentioning

confidence: 99%

Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

Anderson

Takacs

Harris

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A comparison between the methods of MCMC and profile likelihoods was carried out in [ 28 ] for ODE models of varying complexity. Other methods for inference include gradient matching [ 29 , 30 ] and approximate Bayesian computation [ 31 ]. The application of profile likelihoods in model selection was discussed by Simpson et al [ 3 ], who used three different ODE models to describe coral growth, and discussed the importance of parameter identifiability in model selection.…”

Section: Introductionmentioning

confidence: 99%

Parameter identifiability and model selection for partial differential equation models of cell invasion

Liu,

Suh,

Maini

et al. 2024

J. R. Soc. Interface.

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When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide ranges of unseen scenarios, as well as for understanding the underlying mechanisms. In this work, we use a profile-likelihood approach to investigate parameter identifiability for four extensions of the Fisher–Kolmogorov–Petrovsky–Piskunov (Fisher–KPP) model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and that they require more data to be practically identifiable. As a result, we suggest that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection.

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Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

Anderson,

Takacs,

Harris

et al. 2023

J. Math. Biol.

View full text Add to dashboard Cite

Glioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD-1 and MDSCs, and the T cell death rate. Bifurcation analysis suggests that a treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and MDSCs results in the system tending to the tumor-free equilibrium. Using a set of preclinical experimental data, we implement the approximate Bayesian computation (ABC) rejection method to construct probability density distributions that estimate model parameters. These distributions inform an appropriate search curve for global sensitivity analysis using the extended fourier amplitude sensitivity test. Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD-1 immune checkpoint and MDSC suppression of T cells. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting the inhibitory mechanisms of MDSCs should be explored.

show abstract

Calibrating models of cancer invasion: parameter estimation using approximate Bayesian computation and gradient matching

Cited by 5 publications

References 36 publications

Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

Parameter identifiability and model selection for partial differential equation models of cell invasion

Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma

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