Dataset Information

Leon-Triana2021 - Competition between tumour cells and dual-target CAR T-cells

ABSTRACT: This model of the use of chimeric antigen receptor (CAR)-T cell therapy in the treatment of solid tumours is described in the article: "Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept" Odelaisy León-Triana, Antonio Pérez-Martínez, Manuel Ramírez-Orellana and Víctor M. Pérez-García Cancers 2021, 13, 703.; doi: 10.3390/cancers13040703 Comment: This is the second mathematical model, derived from equations 3 to 6, used in the paper. Reproduction of Figure 5b was achieved by setting alpha_1 = 0.183, in substitution for alpha_1 = 0.2 as quoted in the article. Abstract: Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.

DISEASE(S): Glioblastoma Multiforme

SUBMITTER: Emilia Chen

PROVIDER: BIOMD0000001014 | BioModels | 2024-09-02

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	BIOMD0000001014?filename=Leon-Triana2021%20(eqs%203-6)-biopax2.owl	Owl
	BIOMD0000001014?filename=Leon-Triana2021%20(eqs%203-6)-biopax3.owl	Owl
	BIOMD0000001014?filename=Leon-Triana2021%20(eqs%203-6)-matlab.m	Other
	BIOMD0000001014?filename=Leon-Triana2021%20(eqs%203-6)-octave.m	Other
	BIOMD0000001014?filename=Leon-Triana2021%20(eqs%203-6).cps	Other

Items per page:

1 - 5 of 12

Publications

Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept.

León-Triana Odelaisy O Pérez-Martínez Antonio A Ramírez-Orellana Manuel M Pérez-García Víctor M VM

Cancers 20210209 4

Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taki ...[more]

PMID: 33572301

Similar Datasets

Project description:This model of the use of chimeric antigen receptor (CAR)-T cell therapy in the treatment of solid tumours is described in the article: "Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept" Odelaisy León-Triana, Antonio Pérez-Martínez, Manuel Ramírez-Orellana and Víctor M. Pérez-García Cancers 2021, 13, 703.; doi: 10.3390/cancers13040703 Comment: This is the first mathematical model, derived from equations 1 and 2, used in the paper. Reproduction of Fig. 5a was achieved by setting alpha_1 = 0.04, different to the value quoted in the article caption for Fig. 5. Abstract: Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.

Project description:This model is based on the publication: "CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models". Odelaisy León-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chulián, Álvaro Martínez-Rubio, María Rosa, Antonio Pérez-Martínez, Manuel Ramirez-Orellana, Víctor M. Pérez-García doi: 10.1016/j.cnsns.2020.105570 Comment: This model is based on equations (4a)-(4c). Abstract: Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies –leukaemias and lymphomas–is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient’s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.

Project description:This model is based on the publication: "CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models". Odelaisy León-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chulián, Álvaro Martínez-Rubio, María Rosa, Antonio Pérez-Martínez, Manuel Ramirez-Orellana, Víctor M. Pérez-García doi: 10.1016/j.cnsns.2020.105570 Comment: This model is based on equations (3a)-(3c) from the paper. Abstract: Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies –leukaemias and lymphomas–is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient’s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.

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.