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. 2023 Apr 14:14:1152498.
doi: 10.3389/fimmu.2023.1152498. eCollection 2023.

Monitoring of kinetics and exhaustion markers of circulating CAR-T cells as early predictive factors in patients with B-cell malignancies

Clara Beatriz García-Calderón  1 , Belén Sierro-Martínez  1 , Estefanía García-Guerrero  1 , Luzalba Sanoja-Flores  1 , Raquel Muñoz-García  2 , Victoria Ruiz-Maldonado  1 , María Reyes Jimenez-Leon  1 , Javier Delgado-Serrano  1 , Águeda Molinos-Quintana  1 , Beatriz Guijarro-Albaladejo  1 , Inmaculada Carrasco-Brocal  1 , José-Manuel Lucena  2 , José-Raúl García-Lozano  2 , Cristina Blázquez-Goñi  1 , Juan Luis Reguera-Ortega  1 , María-Francisca González-Escribano  2 , Marta Reinoso-Segura  1 , Javier Briones  3 , José Antonio Pérez-Simón  1 , Teresa Caballero-Velázquez  1
Affiliations

Monitoring of kinetics and exhaustion markers of circulating CAR-T cells as early predictive factors in patients with B-cell malignancies

Clara Beatriz García-Calderón et al. Front Immunol. .

Abstract

Purpose: CAR-T cell therapy has proven to be a disruptive treatment in the hematology field, however, less than 50% of patients maintain long-term response and early predictors of outcome are still inconsistently defined. Here, we aimed to optimize the detection of CD19 CAR-T cells in blood and to identify phenotypic features as early biomarkers associated with toxicity and outcomes.

Experimental design: In this study, monitoring by flow cytometry and digital PCR (dPCR), and immunophenotypic characterization of circulating CAR-T cells from 48 patients treated with Tisa-cel or Axi-cel was performed.

Results: Validation of the flow cytometry reagent for the detection of CAR-T cells in blood revealed CD19 protein conjugated with streptavidin as the optimal detection method. Kinetics of CAR-T cell expansion in blood confirmed median day of peak expansion at seven days post-infusion by both flow cytometry and digital PCR. Circulating CAR-T cells showed an activated, proliferative, and exhausted phenotype at the time of peak expansion. Patients with increased expansion showed more severe CRS and ICANs. Immunophenotypic characterization of CAR-T cells at the peak expansion identified the increased expression of co-inhibitory molecules PD1 and LAG3 and reduced levels of the cytotoxicity marker CD107a as predictors of a better long-term disease control.

Conclusions: These data show the importance of CAR-T cells in vivo monitoring and identify the expression of PD1LAG3 and CD107a as early biomarkers of long-term disease control after CAR-T cell therapy.

Keywords: B-ALL; CAR-T; Lymphoma; biomarkers; dPCR (digital PCR); flow cytometry; monitoring.

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Conflict of interest statement

JP-S is an advisor or consultant for Novartis, Janssen, Roche, Jazz Pharmaceuticals, Amgen and Gilead Sciences; reports research support from Novartis, Janssen, Pfizer, Roche and Takeda; reports travel support from Roche, Gilead Sciences and Janssen; and reports patents, royalties or other intellectual property from Entourage Bioscience on cannabinoid derivatives. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Validation of the detection reagent for academic and commercial CD19 CAR-T cells by flow cytometry. (A) Illustration of the different detection methods available for the identification of CAR-T cells by flow cytometry. (B) Percentage of detection of academic CD19 CAR-T cells in CD4 and CD8 compartments with different reagents. (C) Stain index of detection of academic CD19 CAR-T cells in CD4 and CD8 compartments with different reagents. (D) Example of flow cytometry plot of the identification of commercial CD19 CAR-T cells with two recombinant CD19 proteins. (E) Percentage (left) and stain index (right) of the detection of commercial CD19 CAR-T cells with two different reagents. Depicted are median and individual values of four independent experiments. P-values between the indicated groups were calculated using unpaired Mann-Whitney U-t tests. ns: non-significant, *p<0.05.
Figure 2
Figure 2
Commercial CD19 CAR-T cell expansion in the blood of patients post-infusion. (A) Flow cytometry plot of the expansion of CAR-T cells in the blood of patient 32 at different time points post-infusion. (B) Absolute count of CAR-T cells/uL by flow cytometry in the blood of patients with B-ALL and lymphoma. (C) CAR copies/cell measured by digital PCR in the blood of patients with B-ALL and lymphoma. (D) Spearman correlation in peripheral CAR-T cell expansion between flow cytometry and dPCR assays (n=48 patients and 185 observations). (E) Normalized peripheral CAR-T cell expansion by flow cytometry and dPCR assays (n=48 patients). Correlation was calculated using non-parametric Spearmans rank correlation coefficient.
Figure 3
Figure 3
Immunophenotype characterization of non-modified T cells and CAR-T cells at the time of peak expansion in blood. (A) CD4/CD8 ratio of non-modified T cells and CAR-T cells at the time of peak expansion. (B) Memory subsets of CD4+ and CD8+ compartments comparing non-modified and CAR-T cells. (C) Levels of CD69 (i), Ki67 (ii), CD107a (iii), FasL (iv), PD1 and TIM3 (v) and PD1 and LAG3 (vi) in the CD4 and CD8 non-modified and CAR-T cells at the time of peak expansion. Depicted are median and individual values of the nine-teen samples. P-values between the indicated groups were calculated using unpaired Mann-Whitney U-t tests. ns: non-significant, **p<0.01, ***p<0.001.
Figure 4
Figure 4
Swimmer plots of 48 patients with B-ALL or lymphoma after CD19 CAR-T cell infusion. BCA (B-cell aplasia). CRS and ICANs grades are depicted from 1-4 according to the intensity of the color.
Figure 5
Figure 5
Correlation between CAR-T cell expansion in blood and toxicity or response. (A) Correlation between peak expansion by flow cytometry (left) or dPCR (right) and severity of CRS from patients with B-ALL and lymphoma. (B) Correlation between peak expansion by flow cytometry (left) or dPCR (right) and incidence of ICANs from patients with B-ALL and lymphoma. (C) Correlation between peak expansion by flow cytometry (left) or dPCR (right) and severity of CRS from patients with lymphoma excluding patients with B-ALL. (D) Correlation between peak expansion by flow cytometry (left) or dPCR (right) and incidence of ICANs from patients and lymphoma excluding patients with B-ALL. Correlation between absolute CAR-T cell count (E) or CAR copies/cell (F) and event free survival of all patients or (F) excluding patients with B-ALL (G, H). Depicted are median and individual values of the forty-eight samples. P-values between the indicated groups were calculated using unpaired Mann-Whitney U-t tests. ns: non-significant, *p<0.05. Correlation studies were performed using Kaplan-Meier survival analysis.
Figure 6
Figure 6
Immunophenotypic characteristics of CAR-T cells at the time of peak expansion correlate with increased event free-survival. Expression of PD1 and LAG3 >5.2% (red) within CD4 CAR-T cells at the time of peak expansion correlates with increased EFS in patients with B-ALL and lymphoma (A) or excluding patients with B-ALL (B). Expression of CD107a <6% (red) within CD8+ CAR-T cells at the time of peak expansion correlates with increased EFS in patients with B-ALL and lymphoma (C) or excluding patients with B-ALL (D). The median expression levels of these biomarkers were used to calculate these thresholds. Correlation studies were performed using Kaplan-Meier survival analysis.
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