Hanson2016 - Toxicity Management in CAR T cell therapy for B-ALL
Ontology highlight
ABSTRACT:
SUBMITTER: 
Johannes Meyer
PROVIDER: BIOMD0000000837 | BioModels | 2024-09-02
REPOSITORIES: BioModels
Dataset's files
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| BIOMD0000000837?filename=Hanson2016.cps | Other | |||
| BIOMD0000000837?filename=Hanson2016.sedml | Other | |||
| BIOMD0000000837?filename=Hanson2016.xml | Xml |
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Project description:This mathematical model of T cell-tumour interactions considering the roles of T cell competition and stochastic extinction events in CAR T cell therapy is described by the publication:
Kimmel GJ, Locke FL, Altrock PM. "The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy." Proc Biol Sci. 2021 Mar 31;288(1947):20210229.
doi: 10.1098/rspb.2021.0229
Comment:
Reproduction of Fig. 2(a) and (b) was simulated by using the fitted model parameter set given in Table 1 of the manuscript's Supplementary Material, however substituting the values of r_N and rho_C for those stated in Table 1 of the publication manuscript, i.e. r_N = 0.17 and rho_C = 0.0251.
Abstract:
Chimeric antigen receptor (CAR) T cell therapy is a remarkably effective immunotherapy that relies on in vivo expansion of engineered CAR T cells, after lymphodepletion (LD) by chemotherapy. The quantitative laws underlying this expansion and subsequent tumour eradication remain unknown. We develop a mathematical model of T cell–tumour cell interactions and demonstrate that expansion can be explained by immune reconstitution dynamics after LD and competition among T cells. CAR T cells rapidly grow and engage tumour cells but experience an emerging growth rate disadvantage compared to normal T cells. Since tumour eradication is deterministically unstable in our model, we define cure as a stochastic event, which, even when likely, can occur at variable times. However, we show that variability in timing is largely determined by patient variability. While cure events impacted by these fluctuations occur early and are narrowly distributed, progression events occur late and are more widely distributed in time. We parameterized our model using population-level CAR T cell and tumour data over time and compare our predictions with progression-free survival rates. We find that therapy could be improved by optimizing the tumour-killing rate and the CAR T cells' ability to adapt, as quantified by their carrying capacity. Our tumour extinction model can be leveraged to examine why therapy works in some patients but not others, and to better understand the interplay of deterministic and stochastic effects on outcomes. For example, our model implies that LD before a second CAR T injection is necessary.
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