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Prior Chemotherapy Deteriorates T Cell Quality for CAR T Cell Therapy in B-Cell Non-Hodgkin’s Lymphoma

ABSTRACT: Chimeric antigen receptor (CAR) T cell therapy depends on T cells that are genetically modified to recognize and attack cancer cells. Their effectiveness thus hinges on the functionality of a patient’s own T cells. However, CAR T cell therapy is currently only approved for advanced cancers after at least one line of chemotherapy. To evaluate the potential negative effects of prior exposure to chemotherapy on T cell functionality we studied T cells of two B cell Non-Hodgkin’s lymphoma (B-NHL) patient cohorts, one collected before treatment (pre-therapy) and the other after one or more (median 3) lines of chemotherapy (post-therapy). Leveraging advanced multi-parameter flow cytometry and single-cell RNA sequencing (scRNAseq), we elucidated significant modifications in T cell subsets and their transcriptional profiles post-therapy. Our analysis revealed a discernible shift towards phenotypically more differentiated T cells and an upregulation of markers indicative of T cell exhaustion. Additionally, scRNAseq and DNA methylation analyses revealed gene expression and epigenetic changes associated with diminished functionality in post-therapy T cells. Cytotoxicity assays demonstrated superior killing efficacy of CAR T cells derived from treatment-naïve patients compared to those with chemotherapy history. These findings corroborate that employing T cells collected prior to frontline chemotherapy could enhance the effectiveness of CAR T cell therapy and improve patient outcomes.

ORGANISM(S): Homo sapiens

PROVIDER: GSE275947 | GEO | 2025/04/10

REPOSITORIES: GEO

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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.

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.

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Prior Chemotherapy Deteriorates T Cell Quality for CAR T Cell Therapy in B-Cell Non-Hodgkin’s Lymphoma

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