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. 2022 Aug 5;103(1):16–26. doi: 10.1002/cyto.a.24676

Monitoring anti‐CD19 chimeric antigen receptor T cell population by flow cytometry and its consistency with digital droplet polymerase chain reaction

Jiali Cheng 1, Xia Mao 1, Caixia Chen 1, Xiaolu Long 1, Liting Chen 1, Jianfeng Zhou 1, Li Zhu 1,✉
PMCID: PMC10087589  PMID: 35875964

Abstract

Anti‐CD19 chimeric antigen receptor (CAR19) T cell therapy has produced impressive clinical efficacy in patients with relapsed or refractory B‐cell malignancies. As a living drug, monitoring the pharmacokinetics of CAR T cells in vivo is an important part of clinical work, which provides valuable information for assessing therapeutic response and related side effects. However, no guidelines are available regarding the detection and quantification of CAR T cells. Flow cytometry is a convenient and commonly used method in monitoring CAR T cell kinetics, but its performance remains to be validated. By using a commercial anti‐idiotype antibody that detects unique epitopes on the most popular CAR19 construct, we evaluated important performance parameters, including specificity, lower limit of detection, lower limit of quantification, and precision of flow cytometry in the detection and quantification of CAR19 T cells. Consistency between the results generated by flow cytometry and droplet digital PCR was then investigated in 188 pairs of clinical data and in cell line experiments. Rabbit anti‐mouse FMC63 monoclonal antibody possesses high specificity in the detection of CAR19 positive cells by FCM with a cut‐off value of 0.05%. The results produced by flow cytometry and ddPCR were well correlated in the clinical samples and in cell lines, but the correlation deteriorated as the abundance of CAR19 positive cells decreased. This was especially evident with less than 0.5% of lymphocytes in clinical data, possibly due to reduced precision (indicated by intra‐ and inter‐assay coefficients of variability) of both droplet digital PCR and flow cytometry. We demonstrated that flow cytometry using anti‐idiotype antibody is a reliable and robust approach in the detection and quantification of CAR19 T cells in vivo and has good consistency with droplet digital PCR in monitoring CAR19 T cell kinetics.

Keywords: CAR T cell therapy, CAR19 T cell, droplet digital PCR, flow cytometry, pharmacokinetics, rabbit anti‐mouse FMC63 antibody

  • Droplet Digital PCR measures CAR vector copy number at the genomic level while flow cytometry can quantify the CAR protein at the proteomic level.

  • The monoclonal antibody R19M possesses high specificity and specificity in the detection of CAR19 positive cells by FCM.

  • Flow cytometry using anti‐idiotype antibody is a reliable approach to detecting and quantifying CAR19 T cells in vivo and has good consistency with ddPCR in monitoring CAR19 T cell kinetics.

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1. BACKGROUND

Chimeric antigen receptor (CAR) T cell therapy has achieved impressive efficacy in the treatment of B cell malignancies [1, 2, 3], and is being widely investigated for its potential to improve the prognosis of a broad range of tumors, as well as autoimmune and infectious diseases [4, 5, 6]. As a living drug, the in vivo pharmacokinetics of CAR T cells offer important information about the therapeutic response and related side effects. Peak expansion value and persistence of anti‐CD19 CAR (CAR19) T cells in vivo are important factors impacting the best and most durable response to B cell leukemia [7]. Moreover, cytokine release syndrome occurred during CAR T cell therapy was suggested to correlate with the expansion of CAR T cells, highlighting the importance of timely detection of CAR T cells in vivo [8, 9].

There are three main analytical approaches commonly used to monitor the kinetics of CAR T cells in vivo, including digital droplet polymerase chain reaction (ddPCR), quantitative real‐time PCR (qPCR), and flow cytometry (FCM). ddPCR and qPCR quantitatively detect the CAR transgene of the input DNA to reflect the concentration of CAR T cells in vivo. ddPCR quantitates the copy number of CAR transgenes independent of an external reference by partitioning the PCR system into numerous nanoliter‐sized droplets and detecting the fluorescence signal of each droplet after PCR amplification [10, 11]. Many studies have shown that ddPCR has greater precision and sensitivity than qPCR in the detection of rare events with a small sample size [12, 13], making it one of the best choices for detecting CAR T cell in vivo. However, ddPCR and qPCR do not allow retrieval of CAR T cells from patients for multi‐parameter analysis. Moreover, the single‐chain variable fragment (scFv) transgene sequences of some commercialized CAR T cells products, like Yescarta (Axi‐cel) and CARTEYVA (Relma‐cel) are unavailable in all hospital laboratories, and thus ddPCR or qPCR cannot be performed due to a lack of primers and probes. In those conditions, only FCM can be used to evaluate pharmacokinetics of CAR T cells in vivo.

FCM reports the proportion of CAR T cells and is quasi‐quantitative. It takes less time to carry out an FCM assay compared with ddPCR. Moreover, high‐throughput FCM allows simultaneous detection of the kinetics, phenotypes, and functions of CAR T cells [14, 15]. An obstacle to detecting CAR T cells by FCM is availability of antibodies specific to CAR. There are three main kinds of commercial reagents available to detect CAR expression, including Protein L [16], target protein, and anti‐scFv region monoclonal antibody. Protein L, a bacterial surface protein, can recognize single‐chain antibody fragments of the CAR, as well as the kappa light chain and Fab fragments of immunoglobulin, resulting in nonspecific detection of CAR T cells [16, 17, 18]. As antigens, fluorescent‐tagged target proteins that take advantage of CAR's binding affinity for its target antigen can be recognized followed by specific binding by CAR T cells [18, 19, 20, 21]. The disadvantages of fluorescent‐tagged target proteins were described in the product instructions including difficulty to store, easy to inactivate, and surface adsorption loss. Murine‐derived anti‐human CD19 protein, FMC63, is the basis of scFv of most CAR19, and anti‐FMC63 antibody is an anti‐idiotype antibody for this kind of CAR19 [22]. In this study, we used a commercial rabbit anti‐mouse FMC63 monoclonal antibody (R19M) in detecting CAR19 T cells after validating the specificity and practicality of the reagents.

The performance of ddPCR in the detection of CAR19 T cells was demonstrated in detail in our previous work [23], but similar studies on FCM are limited [21]. As far as we know, no data illustrates the performance of FCM in the detection and quantification of CAR19 T cells using R19M and compares it with ddPCR. In this study, we determined important performance parameters of FCM using R19M to quantify CAR19 T cells including specificity, limit of detection (LOD), lower limit of quantification (LLOQ), and precision; investigated the corresponding and consistency of ddPCR and FCM in the quantification of CAR19 T cells; and discussed the precision of ddPCR and FCM in the detection of CAR19 positive cells, aiming to provide a reference for how to interpret the data of CAR T cell kinetics obtained by the two methods, especially FCM.

2. MATERIALS AND METHODS

2.1. Validation of anti‐idiotype antibody for CAR19

Before the study was conducted, a staining regimen involving anti‐CD45, anti‐CD3, and anti‐CAR19 was designed and verified. Three commonly used anti‐CAR19 reagents, custom FITC‐conjugated protein L‐Cys (PrimeGene, Cat: 1004‐03), FITC‐conjugated CD19 protein (ACRO, Cat: CD9‐HF2H2), and AF647‐conjugated R19M antibody (BioSwan Laboratories, Cat: 200102) were tested to determine the specificity using specimens from negative controls (PB without CAR19 T cells).

2.2. Study design

Four important parameters, including specificity, sensitivity (LOD and LLOQ), accuracy (consistency with ddPCR), and precision (intra‐ and inter‐assay), were evaluated for FCM in the detection and quantification of CAR19 T cells. The validation design and statistical evaluation are described in Table 1. We conducted the research mainly through two sets of experiments, including cell line experiments and clinical sample analysis (Figure 1). In the cell line experiments, samples with different expected concentrations of CAR19 positive cells were prepared through serial dilution of monoclonal CAR19 positive 293FT cells with wild‐type 293FT cells. The prepared cell line samples were then detected as indicated in Figure 1. The correlation between the results of ddPCR and FCM was calculated and the intra‐CV and inter‐CV for ddPCR and FCM were also determined as shown in Figure 1. In the clinical sample study, we included 188 pairs of data (by ddPCR and FCM) quantifying CAR19 T cells in peripheral blood (PB) collected at the same time, and analyzed the consistency between the results of the two methods. Healthy donor‐derived PB samples were detected as blank or negative control to investigate the specificity and LOD.

TABLE 1.

FCM validation for CAR T cell detection

Parameters Validation design Calculation Definition
Specificity PB without CAR T cell (7 < S < 10) / The ability to generate a negative result for negative samples

Sensitivity

LOD

LLOQ

LOD: PB without CAR T cells (S = 15);

LLOQ: 293 FT cell line samples with different concentration of CAR19 positive 293FT cells (7 dilutions, performed in triplicates).

LOD: mean + 3 SD

LLOQ: above the LOD, the lowest concentration where the CV is acceptable (CV < 30% is acceptable)

The ability to correctly identify positive populations among a sample.

LOD: the lowest amount of an analyte that can reliably be detected.

LLOQ: the lowest amount of an analyte that can reliably be quantitated.
Accuracy a Detect a sample with or without CAR19 T cells by both FCM and ddPCR (S = 188, 7 b ) Described by the Spearman correlation coefficient r (r > 0.8 is acceptable). How close the detected value to the real true value

Precision

Intra‐assay

Inter‐assay

Intra‐assay: triplicates for each sample (S = 7);

Inter‐assay: three independent experiments, triplicates for each sample per experiment (S = 7).

Intra‐assay: calculated intra‐CV for each sample and the precision is indicated by the mean of intra‐CV and the range.

Inter‐assay: mark the mean for each sample in an experiment as M. For each sample, calculate the inter‐CV from the three Ms. The precision is indicated by the mean of inter‐CV and the range.

The reproducibility of a test

Intra‐assay: how close the results are when the same sample is repeated tested

Inter‐assay: the variation among different assay
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Abbreviations: PB, peripheral blood; S, sample size.

a

Due to the lack of acknowledged reference standards, true accuracy cannot be exactly determined. We intended to choose ddPCR as a “gold standard” method, and compare the consistency of the results between FCM and ddPCR. Of particular importance is that the results of ddPCR do not absolutely represent the true value.

b

One hundred and eighty‐eight pairs of clinical samples and seven pairs of 293FT cell line samples.

FIGURE 1.

FIGURE 1

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Workflow of the study. The study included two sets of experiments, clinical data analysis and cell line experiments. Correlation analysis was conducted in 188 pairs of FCM and ddPCR data obtained from clinical samples from 96 patients in trial ChiCTR‐OPN‐16008526. In cell line experiments, established monoclonal CAR19 positive 293FT cells, marked as A, and wild type 293FT cells, marked as B, were used to generate serial mixtures with different proportions of CAR19+ 293FT cells according to the description in the serial dilution diagram. Then, the prepared samples were divided in two parts according to the cell number for ddPCR assay and FCM assay. By repeated experiments following the illustration, intra‐ and inter‐CV were calculated. Exp. stands for experiment. An experiment refers to all steps from sample processing to acquisition. Each concentration of samples was assayed in triplicate in an independent experiment to generate intra‐CV. Inter‐CV was calculated from the means of three batches of experiments performed with an interval of 20 min for FCM and in another day for ddPCR [Color figure can be viewed at wileyonlinelibrary.com]

2.3. Flow cytometry

For CAR19 T assessments, PB samples from patients before or after CAR T cell infusion were processed within 24 h. Antibodies CD45‐FITC, CD3‐PE (BD Biosciences), and R19M‐AF647 (BioSwan Laboratories, Cat: 200102) were incubated with fresh whole PB containing about 2 × 106 white blood cells (WBCs) at room temperature, while CD3‐PE, CD45‐FITC were used in the parallel fluorescence minus one (FMO) control tube. After a 20‐min incubation, all samples were processed after red blood cells lysis (BD Biosciences, San Jose, CA, Cat No. 349202) and FBS buffer (phosphate‐buffered saline with 1% fetal bovine serum) washing before analysis. In order to enrich enough T cells for analysis, samples were lysed (BD Biosciences, Cat No. 555899) and washed before staining if 2 × 106 WBCs were not available in 200 μl of whole blood. The staining procedure for Protein L‐FITC and CD19‐FITC protein was nearly the same as R19M, except for fluorescence of the gating antibody (CD3‐APC and CD45‐PercP). In the cell line experiments, prepared samples containing 1 × 106 293FT cells were washed by FBS buffer before (once) and after (twice) incubation with R19M‐AF647 antibodies. Within the lymphocyte gate in clinical samples or the living cell gate in cell line samples, at least 20,000 events were acquired by Novocyte flow cytometry (Agilent) with the model of D3000. Voltage is the factory setting, and the engineer recommends not to adjust the voltage in daily testing. Quality control using QC particles was conducted every day, and the experiments were performed after the QC test was passed. Data were analyzed by FlowJo software version 10 and NovoExpress version 1.3.0.

2.4. Droplet digital PCR

DNA extracted from whole blood cells or cell lines was prepared for ddPCR. A 20 μl PCR amplification system was prepared by 10 μl 2X ddPCR Supermix (Bio‐Rad), 2 μl primers, 2 μl probe, 2 μl DNA template, and 4 μl H2O. Droplet generation, PCR, and results readout were performed in a Quantalife QX200 Droplet Digital PCR system (Bio‐Rad). The sequence of CAR19 transgene specific primers and VIC fluorophores coupled probe were described our previous work, as well as the detailed procedure [23].

2.5. Establishment of monoclonal CAR19 positive 293FT cells, serial dilution, and DNA extraction

Lentivirus encoding CAR19 was used to transfect 293FT cells. The 293FT cell line was obtained from the ATCC (Manassas, VA), and the CAR19 used in this research was mainly composed of a FMC63‐based scFv, two co‐stimulatory domains of CD28 and 4‐1BB, and the activation domain of CD3ζ. CAR19 positive 293FT cells identified by R19M antibody were sorted into 96‐well plates containing 100 μl culture medium with penicillin and streptomycin at single‐cell resolution by fluorescence‐activated cell sorting (FACS). The sorted cells were incubated at 37°C with 5% CO2. The expanded monoclonal population with CAR19 positive rate > 99.5% (determined by FCM using R19M antibody) was selected and cultured for the following experiment.

Serial dilution was conducted according to the cell number, which was counted using a Countstar Rigel S2 Smart Cell Analyzer (ALIT Life Science, China). The workflow, summarized in Figure 1, shows that CAR19 positive 293FT cells were diluted with wild‐type 293FT cells. At each concentration, 3 × 106 cells were assigned for FCM assay and the remaining 2 × 106 cells were used for DNA extraction implemented with a DNA blood mini kit (Qiagen) (Figure 1). A NanoDrop spectrophotometer (Thermo Scientific) was used to detect the concentration and quality of the extracted DNA. The DNA was stored at −20°C.

2.6. Inclusion of clinical data

The CAR T cell FCM detection data from November 13, 2019 to July 1, 2020 generated from patients in the clinical trial of CAR19/22 T cell therapy for relapsed/refractory non‐Hodgkin B‐cell lymphoma (B‐NHL) or acute B‐cell leukemia (B‐ALL) were screened (Trial No: ChiCTR‐OPN‐16008526). Not all FCM data had a corresponding ddPCR, and finally, 188 pairs of data from 96 patients were included in the consistency analysis. The detection of CAR19 T cells or CAR19 transgene copy number was performed at different days post CAR T cell infusion (ranging from days 1 to 1210), and Figure S1 depicts the relationship between the value detected by FCM or ddPCR and the days post CAR19 T cell infusion. The details of the clinical study were introduced in our previous publication [24]. The clinical trial was carried out with the consent of the patients, approved by the Ethics Committee of Tongji Medical College, and complied with the Helsinki Declaration.

2.7. Data processing

Data were analyzed with GraphPad Prism 8.2.1 (GraphPad Software Inc.). All tests were two‐tailed, and statistical significance was defined as p < 0.05. Before statistical analysis, a normality test was done to examine whether the data had a normal distribution. Mann–Whitney U test was used for two groups of unpaired non‐normally distributed data. Spearman test was used to examine the correlation of paired non‐normally distributed data. Generalized least squares were used for linear regression and R squares were calculated. The coefficient of variance (CV) was determined as standard deviation (SD) divided by the corresponding mean. The intra‐assay CV and inter‐assay CV were obtained from cell line experiments and calculated as indicated in Figure 1.

3. RESULTS

3.1. Validation of anti‐idiotype antibody for CAR19

As mentioned above, before the study was conducted, the staining regimen and three commonly used reagents, protein L, CD19 protein, and R19M antibody, were tested. Figure 2A shows the gating strategy of the clinical sample assay. Specificity validations of the three commonly used reagents were performed in negative samples (healthy donor‐derived PB without CAR19 T cells). Protein L displayed the worst specificity with an average proportion of CAR T cells to lymphocytes (false‐positive) of 5.15% (Figure 2B,C). In contrast, CAR positive cells were nearly undetected when using the CD19 protein and R19M antibody. The average proportion of false CAR positive cells to lymphocytes were 0.027% and 0.016%, respectively. The result indicated that CD19 protein and R19M antibody possess higher specificity than Protein L in the detection of CAR19 T cells (Figure 2B–E). Considering the instability of the CD19 protein after reconstitution, R19M antibody was chosen to detect CAR19 T cells. We then conducted a titration assay to obtain the suitable staining concentration of R19M, and finally a 1:200 dilution was chosen (Figure S2). In the examination of PB sample from patient receiving CAR19 T cell infusion, the R19M antibody clearly identified CAR19 T cells among CD3+ T cells, compared with the FMO group (Figure 2F).

FIGURE 2.

FIGURE 2

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Validation of the staining regimen and the anti‐CAR19 antibody. In order to define CAR‐T events more strictly, CD3+ T cells was firstly gated in CD45+ lymphocytes, and then CAR‐T events was gated in CD3+ T cells, and the proportion of CAR‐T in lymphocytes was calculated. (A) The gating strategy to distinguish T‐cells from whole blood cells. De‐adhesion was performed after the lymphocyte gate, which is not shown here. The data was from a patient with CAR19 T cells infusion. (B) The percentage of CAR T‐cell to lymphocyte detected by protein L, R19M, and CD19 protein, together with anti‐CD45 antibody and anti‐CD3 antibody, in healthy donor‐derived peripheral blood (PB) that reflects the specificity of the corresponding reagents. (C, D, E) Representative dot plots of protein L, CD19 protein, and R19M in the detection of CAR T‐cells using healthy donor‐derived PB. (F) Representative histogram of CAR T‐cells gated from CD3+ T‐cells detected by R19M in the PB of patients that received CAR19 T‐cell infusion. The results were obtained by deducting the proportion of CAR‐T cells to lymphocytes in FMO group from that of R19M group. The data were from the same patient in (A) [Color figure can be viewed at wileyonlinelibrary.com]

3.2. Defining the LOD and LLOQ for flow cytometric assay

To better understand the reported value of FCM in the quantification of CAR19 T cells using R19M antibody, the LOD, defined as mean plus three times SD, was calculated from the results of the FCM assay of 15 PB samples without CAR19 T cells [25]. The mean percentages of CAR19 T cells to lymphocytes were 0.016% and SD was 0.00696% (Figure 2B). Therefore, LOD was 0.03678%. Based on LOD, serial dilution was conducted using the established CAR19 positive 293FT cell line and wild‐type 293FT cell line to produce seven different proportions of CAR19 positive 293FT cells with the lowest proportion expected of 0.05% (Figure 1). The dilution was accurate as the mean detected value almost equals the expected value, with R 2 for linear regression of 0.9999 (Figure 3A). Moreover, the CAR19 positive population was clearly exhibited in the dot plot of FCM assay in all levels of CAR19 positive proportion (Figure 3B). In the 0.05% group, the quantitative performance of FCM was accurate and precise with a mean proportion of 0.048% (n = 9), a mean intra‐CV of 10.8% (n = 3), and an inter‐CV of 2.0%. These results suggested that it is safe to set the LLOQ of FCM for the detection of CAR19 positive cells using R19M antibody at 0.05% when there are enough acquired cells. FCM using R19M antibody can accurately and precisely quantify the CAR19 positive cells when the proportion is greater than or equal to 0.05%.

FIGURE 3.

FIGURE 3

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Results of serial dilution of 293FT‐CAR19. CAR19 expressing 293FT monoclonal cell lines were firstly established and wild type 293FT cells were mixed with 293FT‐CAR19 cells to generate samples with different proportions of CAR19 positive 293FT cells. (A) Correlation of the detected percentage of CAR19 positive 293FT and the expected percentage. In each group, the detection was repeated nine times and the mean value was adopted in the correlation analysis. (B) Representative dot plots showing CAR19 positive populations of each concentration group [Color figure can be viewed at wileyonlinelibrary.com]

3.3. Correlation between results of flow cytometry and ddPCR in quantifying CAR19 positive cells

To investigate the correlation between FCM and ddPCR in monitoring of CAR19 positive cells, samples generated in the serial dilution of CAR19 positive 293FT cell lines were detected by the two techniques simultaneously. In the range of concentrations between 0.05% and 100%, the mean value of nine repetitions of CAR19 positive 293FT cell proportion determined by FCM had a significant correlation with that of CAR19 copy number determined by ddPCR, with a Spearman correlation coefficient r of 1, and a p‐value of 0.0004. These data indicated that in cell lines, FCM and ddPCR have good consistency in the quantification of CAR19 positive cells with a concentration above 0.05%.

3.4. Correlation between results of ddPCR and flow cytometry in clinical sample

A total of 188 pairs of data reflecting the abundance of CAR19 T cells in vivo reported by ddPCR and FCM during November 13, 2019 to July 1, 2020 were collected from relapsed or refractory B‐NHL or B‐ALL patients who received CAR19 T cell therapy. Figure 4B depicts the distribution of CAR19 T cells to lymphocytes percentage (CAR‐T/Lym) according to the range of CAR19 copy number, generally indicating the corresponding between CAR T/Lym and copies/μg DNA. Spearman correlation analysis revealed that results of FCM correlated with those of ddPCR (p < 0.0001, r = 0.6875) (Figure 4C). Since the LLOQ of FCM determined in the cell line study was 0.05%, we re‐calculated the correlation coefficient of the data set when CAR‐T/Lym was greater than or equal to 0.05%. Spearman correlation coefficient r was slightly enhanced to 0.7001 (n = 134). Further, we divided the data into groups by CAR‐T/Lym with an interval of 0.1% and found that when CAR‐T/Lym measured by FCM was less than 0.5%, there was no statistical difference in the copy number of CAR19 measured by ddPCR among the groups (Figure S3A). The correlation between the data generated by ddPCR and by FCM was poor, with r equal to 0.3577 (Figure S3B). By excluding the data with CAR19 T cell proportion less than 0.5% of lymphocytes, Spearman correlation coefficient r increased to 0.814 (Figure 4D). At the individual level, the copy number of CAR19 detected by ddPCR is also correlated with CAR‐T/Lym determined by FCM. Figure 4E depicted the pharmacokinetics of CAR19 T cells post infusion monitored by ddPCR and FCM in two patients. The Spearman r between the results of ddPCR and FCM for patient 1 and patient 2 are 0.93 and 0.71, respectively. The weaker correlation in patient 2 is possibly due to fewer CAR19 T cells in vivo. These data collectively suggested that ddPCR and FCM had relatively good consistency in monitoring CAR19 T cell kinetics in clinical practice, but when the abundance of CAR19 T cells becomes low, especially less than 0.5% of lymphocytes, the consistency deteriorated, showing a deviation in the quantification of CAR19 T cells by ddPCR or FCM.

FIGURE 4.

FIGURE 4

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Correlation of the results generated by FCM and ddPCR in clinical samples and in cell line samples. (A) Correlation (R 2 = 0.9942) of the results obtained by FCM and ddPCR in the detection of abundance of CAR19 positive 293FT cells using the standards generated in the serial dilution assay. Each standard was detected nine times and the mean value was adopted in the correlation analysis. (B) Distribution of the proportion of CAR T‐cells to lymphocytes obtained by FCM in the clinical examination according to the levels of CAR19 copy number reported by ddPCR. (C, D) Correlation of the results obtained by FCM and ddPCR in the detection of CAR T‐cell kinetics in patients received CD19 CAR T‐cell infusion (D shows when the proportion of CAR T‐cells to lymphocytes was greater than 0.5%). Each sample was detected once. (E) Two examples of the results generated by ddPCR and FCM in the monitoring of CAR T‐cell kinetics at an individual level [Color figure can be viewed at wileyonlinelibrary.com]

3.5. Precision of flow cytometry and ddPCR

Clinical data indicated that the consistency of FCM and ddPCR deteriorates when the abundance of CAR19 T cells becomes low. We further analyzed the precision of FCM and ddPCR in the detection of CAR19 positive cells in the laboratory to interpret the characteristics of the clinical data. Standard samples produced by serial dilution using the CAR19 positive 293FT cell line were prepared as mentioned above. Each sample was processed and detected in three independent experiments, with three replicates in each experiment, and the intra‐CV and inter‐CV of the two methodologies were determined (Figure 1). The results are shown in Figure 5A,B. The CVs of ddPCR and FCM in all groups were less than 18%. However, the intra‐CVs of the two methodologies increased as the abundance of CAR19 positive 293FT cells decreased (Figure 5C), consistent with the clinical data. The mean intra‐CV value of FCM and ddPCR in group with 0.05% CAR19 positive 293FT cells were 10.8% and 14.7%, respectively. No statistically significant differences existed between the intra‐CV of FCM and ddPCR in all groups (Figure 5C). The mean intra‐CV of FCM and ddPCR for all samples were 3.99% (with the range of 0.08%–17.6%) and 6.38% (with the range of 1.09%–17.7%). In the inter‐assay analysis, the variances of FCM and ddPCR were no more than 10% (Figure 5D), and the mean inter‐CV for all samples was 1.80% (with a range of 0.12%–3.57%) and 4.69% (with a range of 1.40%–8.88%). The inter‐CV of ddPCR and FCM were both less than the intra‐CV in groups with a proportion of CAR19 positive 293FT no more than 0.5%, which may result from the repeated measurements of one batch experiment. These data suggested that ddPCR and FCM are both precise in quantifying CAR19 positive cells. However, the precision decreased in the detection of the sample with a low abundance of CAR19 positive cells for both ddPCR and FCM.

FIGURE 5.

FIGURE 5

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The precision of ddPCR and FCM in the detection of cell line samples. (A, B) The original results of FCM (A) and ddPCR (B) in the detection of CAR19 positive 293FT proportions or CAR19 copy numbers in the prepared standards. (C, D) The intra‐CV and inter‐CV of FCM and ddPCR in the detection of abundance of CAR19 positive 293FT cells in the prepared standards. Three intra‐CV was obtained in each group. The error bar represents the mean % CV ± 95% confidence interval (C). The inter‐CV was calculated from three batches of independent experiments. Only one inter‐CV was obtained. No statistical differences existed between the intra‐CV or inter‐CV of FCM and ddPCR [Color figure can be viewed at wileyonlinelibrary.com]

4. DISCUSSION

CAR T cells usually experience an expansion phase within the first 2 weeks after infusion in a majority of patients, then enter a contraction phase, finally maintaining a relatively low level or even becoming undetectable in vivo, leading to difficulties in detecting and quantifying CAR T cells [26, 27]. This study showed that the LLOQ for FCM in the detection of CAR19 positive cells using R19M antibody is 0.05%. Our previous study suggested that the LOQ for ddPCR was 50 copies/μg DNA which is lower than 0.05% of CAR19 T to lymphocytes, given that: (i) a WBC contains 6.6 × 10−6 μg DNA; (ii) CAR19 transgene was represent as single copy in CAR T cells; (iii) the ratio of lymphocytes to WBC is no more than 1. The calculation process was described in Data S1. These data indicated that ddPCR is more sensitive than FCM in detecting samples with a very low abundance of CAR19 T cells. However, FCM can simultaneously monitor the host immune state, especially the presence of B cells. B cells aplasia (BCA) helps to imply the existence and well function of CAR19 T cells in vivo and researches suggested that prolonged BCA indicated superior long‐term remission [27]. Data collected from the clinical samples and cell line showed good consistency between FCM and ddPCR in monitoring CAR19 T cell kinetics. We exploited the most commonly used representations, proportion to lymphocyte by FCM and copy number per μg DNA by ddPCR, in clinical practice to perform the analysis. In addition, copies/μg DNA is also a kind of proportion because 1 μg DNA typically represents for 106/6.6 WBCs and a copy of CAR19 represents for one CAR T cell. Therefore, proportion of CAR T‐cells to Lymphocytes in theory positively correlates with the copy number of CAR19 transgene per μg DNA in PB samples. Nonetheless, CAR T/WBC is possibly a better form of representation in terms of the consistency analysis with ddPCR reported as copies/μg DNA. The intra‐ and inter‐assay precision of both techniques are acceptable with respective largest mean CV values of 10.8% and 14.7% for FCM and ddPCR at 0.05% abundance of CAR19 positive cells. These results supported FCM as also feasible for quantifying CAR19 T cells in vivo when the proportion of CAR19 T cells to lymphocytes is above 0.05%. However, the consistency of ddPCR and FCM in clinical samples decreased, with the Spearman correlation coefficient dropping from 0.8144 to 0.7001, as the lowest concentration of CAR‐T/Lym in the data set decreased from 0.5% to 0.05%. The precision of ddPCR and FCM determined in cell line samples also decreased. These results indicated that higher variation existed in the results generated by ddPCR and FCM for a lower abundance of CAR19 T cells, especially when the proportion is less than 0.5%.

The performance of these two techniques is dependent on the characteristics of probes and antibodies, the starting materials, instrument settings, and operator proficiency [28, 29]. In the detection of CAR19 positive cells by FCM, our data showed that AF647 conjugated R19M antibody had a better specificity than customized FITC conjugated Protein L. It was a pity that we did not use the same fluorescent dye that the stain index and sensitivity of AF647 is better than FITC. Besides, a brighter fluorescent dye like PE could be chosen when the expression of antigen is weak. Consequently, the staining panels had to be adjusted. To minimize the fluorescence spillover of the gated antibody CD3 or CD45 to the CAR19 detection channel, the fluorescent dyes for CD3 and CD45 in Protein L‐FITC or CD19 Protein‐FITC groups were APC and PerCP, respectively, but in R19M groups were PE and FITC, respectively. The gating on T cells or lymphocytes were not impaired much since the clone of the antibodies were the same and the expression of CD3 in T cells, CD45 in leukocytes is intensive. In the clinical detection, the staining panel was consistent and FMO control was performed on every sample to deduct the background signals. Setting of FMO group is necessary for accurately gating, especially when the concentration of CAR T cells in the sample was very low or very high. The number of cells acquired for analysis is also an important factor influencing the sensitivity and accuracy of FCM [30]. In this study, 20,000 lymphocytes or viable 293FT cells were acquired, which is practicable. Nevertheless, patients receiving CAR T cell therapy usually experience a period of leukopenia, leading to failed acquisition of the expected number of cells in a fixed volume of blood [31]. This may be one of the reasons that consistency between ddPCR and FCM of clinical data was not as good as that of the cell line.

It should be emphasized that the clinical data analyzed in the study were from a clinical trial of a cocktail infusion of CAR19 T and CAR22 T cell therapy. In this study, we only examined CAR19 T cells due to the unavailability of an anti‐idiotype antibody against CAR22 T. Theoretically, infusion of CAR22 T cells will decrease the proportion of CAR19 T cells to lymphocytes, since CAR22 T cells are included in the total number of lymphocytes. This reduction might be more significant in samples taken close to the infusion when the CAR T cells (including CAR19 T cells and CAR22 T cells) expand robustly. But the effect of CAR22 T cell infusion with FCM was the same as ddPCR. It will not affect consistency analysis of FCM and ddPCR, because the copy number of CAR19 reported by ddPCR is calculated from the input whole‐genome DNA extracted from PB, which also contains CAR22 T cells. In addition, the R19M used in the study was an anti‐FMC63 epitope anti‐idiotype antibody, therefore it is only applicable for murine‐derived CAR19 T cells, including the approved CAR19 T cell products in the market like Yescarta (Axi‐cel) and CARTEYVA (Relma‐cel). Fully human CD19 specific CAR T cell therapies are continuously being developed, and new detection reagents are required.

For ddPCR, high‐quality DNA, well‐designed primers, generation of enough droplets, and robust PCR amplification are crucial to producing accurate results [29]. A skillful operator is especially required. Setting an internal reference and a certain number of repeats may contribute to the reliability of the results of ddPCR [32]. In the clinical practice of ddPCR in this study, the DNA extracted from the blood sample during a period of time was usually stored at −20°C and all samples were detected together. However, the freeze–thaw process may lead to the breakage of DNA polymers and result in loss of genetic material in the sample, which may affect the detection and quantification of CAR19 transgene, and therefore impact the consistency between ddPCR and FCM. In conclusion, every laboratory should validate its own analytical method and set relevant parameters according to the real situation to better interpret its data.

Results reported in this study by ddPCR (copies/μg DNA) and FCM (CAR T/Lym) were both relative quantitation, as well as majority of the other research centers [24, 26, 27]. During the CAR T‐cell therapy, the abundance of endogenous immune cells, especially CD3+ T cells and CD19+ B cells changes dramatically, resulting in fluctuations of CAR T ratios. Absolute quantification of CAR T cells using counting beads can directly reflect the concentration of CAR T‐cell in circulation independent of endogenous immune cells. So far, no study investigated the difference between the CAR T‐cell kinetics of relative quantification and absolute quantification. As mentioned at the beginning, CAR T‐cell kinetics tightly associated with the short‐term and long‐term of prognosis and the severity of the CRS, where CAR T‐cells kinetics were mostly relative quantification, usually copies/μg DNA. The clinical significance of absolute CAR T‐cells quantification is worthy of further investigation. Recently, a clinical research of anti‐CD7 CAR T cell therapy measured absolute CAR T cells/μl PB and showed the similar trend as copies/μg DNA detected by qPCR, where no difference of peak number of CAR T‐cells/μl PB was identified between severe (>2 grade) and mild (≤2 grade) CRS groups [33]. However, absolute quantification using counting beads is difficult for leukopenia sample which commonly need to be enriched before testing. Regarding the detecting principles, FCM detects the expression of CAR antibodies on the cell membrane, while ddPCR detects the existence of CAR transgenes in the cell. There are many regulatory procedures from genes to proteins expressed on the membrane, which mainly include transcription, translation, and post‐translational modification. One study showed that the sequence of CAR transgene encoding a different structural form of scFv affects CAR expression on the cell membrane [34]. Ubiquitination of CAR controls its expression on the cell surface [35]. Regulation of CAR expression is a research hotspot [36]. Combining the results of ddPCR and FCM in the quantification of CAR T cells provides information about the expression of CAR transgenes. Under the premise of accurate detection, if the copy number of CAR19 is high, but the proportion of CAR19 T cells is very low, the expression of CAR19 transgene in cells is likely downregulated.

5. CONCLUSION

FCM using R19M antibody is a promising method in the detection and quantification of murine CAR19 T cells in vivo that has good consistency with ddPCR. The precision of both ddPCR and FCM decreased in detecting samples with a low abundance of CAR T cells, leading to reduced consistency between the two techniques. Simultaneous incorporation of ddPCR and FCM to detect CAR T cells provides a more reliable result for CAR T cell kinetics in vivo.

AUTHOR CONTRIBUTIONS

Jiali Cheng: Conceptualization (equal); formal analysis (lead); investigation (lead); project administration (lead); validation (lead); visualization (lead); writing – original draft (lead). Xia Mao: Conceptualization (supporting); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); resources (equal); validation (supporting); writing – review and editing (supporting). Caixia Chen: Investigation (supporting); methodology (supporting); project administration (supporting); validation (supporting); visualization (supporting); writing – review and editing (supporting). Xiaolu Long: Data curation (supporting); investigation (supporting); methodology (supporting); project administration (supporting); validation (supporting); visualization (supporting); writing – review and editing (supporting). Liting Chen: Conceptualization (supporting); data curation (supporting); investigation (supporting); resources (supporting); supervision (supporting); writing – review and editing (supporting). Jianfeng Zhou: Conceptualization (equal); resources (supporting); supervision (supporting); writing – review and editing (supporting). Li Zhu: Conceptualization (equal); funding acquisition (lead); resources (equal); supervision (equal); writing – review and editing (equal).

CONFLICT OF INTEREST

The authors declare that they have no competing interests.

6.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/cyto.a.24676.

Supporting information

Appendix S1 Supporting Information

Click here for additional data file. (1.1MB, docx)

MIFlowCyt: MIFlowCyt Item Checklist

Click here for additional data file. (120KB, doc)

ACKNOWLEDGMENT

The authors would like to thank all members of the study team, the patients, and their families.

Cheng J, Mao X, Chen C, Long X, Chen L, Zhou J, et al. Monitoring anti‐CD19 chimeric antigen receptor T cell population by flow cytometry and its consistency with digital droplet polymerase chain reaction. Cytometry. 2023;103(1):16–26. 10.1002/cyto.a.24676

Jiali Cheng and Xia Mao contributed equally to this work and share first authorship.

Funding information National Natural Science Foundation of China, Grant/Award Number: 81900187

REFERENCES

  • 1. Maude SL, Laetsch TW, Buechner J, Rives S, Boyer M, Bittencourt H, et al. Tisagenlecleucel in children and young adults with B‐cell lymphoblastic leukemia. N Engl J Med. 2018;378(5):439–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Enblad G, Karlsson H, Gammelgård G, Wenthe J, Lövgren T, Amini RM, et al. A phase I/IIa trial using CD19‐targeted third‐generation CAR T cells for lymphoma and leukemia. Clin Cancer Res. 2018;24(24):6185–94. [DOI] [PubMed] [Google Scholar]
  • 3. Chong EA, Ruella M, Schuster SJ. Five‐year outcomes for refractory B‐cell lymphomas with CAR T‐cell therapy. N Engl J Med. 2021;384(7):673–4. [DOI] [PubMed] [Google Scholar]
  • 4. Jin X, Xu Q, Pu C, Zhu K, Lu C, Jiang Y, et al. Therapeutic efficacy of anti‐CD19 CAR‐T cells in a mouse model of systemic lupus erythematosus. Cell Mol Immunol. 2020;18(8):1896–903. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Haas AR, Tanyi JL, O'Hara MH, Gladney WL, Lacey SF, Torigian DA, et al. Phase I study of Lentiviral‐transduced chimeric antigen receptor‐modified T cells recognizing Mesothelin in advanced solid cancers. Mol Ther. 2019;27(11):1919–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Seif M, Einsele H, Löffler J. CAR T cells beyond cancer: Hope for immunomodulatory therapy of infectious diseases. Front Immunol. 2019;10:2711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Hay KA, Gauthier J, Hirayama AV, Voutsinas JM, Wu Q, Li D, et al. Factors associated with durable EFS in adult B‐cell ALL patients achieving MRD‐negative CR after CD19 CAR T‐cell therapy. Blood. 2019;133(15):1652–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Cohen AD, Garfall AL, Stadtmauer EA, Melenhorst JJ, Lacey SF, Lancaster E, et al. B cell maturation antigen‐specific CAR T cells are clinically active in multiple myeloma. J Clin Invest. 2019;129(6):2210–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Frey N, Porter D. Cytokine release syndrome with chimeric antigen receptor T cell therapy. Biol Blood Marrow Transplant. 2019;25(4):e123–7. [DOI] [PubMed] [Google Scholar]
  • 10. Mehle N, Dreo T. Quantitative analysis with droplet digital PCR. Methods Mol Biol. 2019;1875:171–86. [DOI] [PubMed] [Google Scholar]
  • 11. Chen L, Wang G, Long X, Hou H, Wei J, Cao Y, et al. Dynamics of blood viral load is strongly associated with clinical outcomes in coronavirus disease 2019 (COVID‐19) patients: a prospective cohort study. J Mol Diagn. 2021;23(1):10–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Hindson CM, Chevillet JR, Briggs HA, Gallichotte EN, Ruf IK, Hindson BJ, et al. Absolute quantification by droplet digital PCR versus analog real‐time PCR. Nat Methods. 2013;10(10):1003–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Franke GN, Maier J, Wildenberger K, Cross M, Giles FJ, Müller MC, et al. Comparison of real‐time quantitative PCR and digital droplet PCR for BCR‐ABL1 monitoring in patients with chronic myeloid leukemia. J Mol Diagn. 2020;22(1):81–9. [DOI] [PubMed] [Google Scholar]
  • 14. Martinez EM, Klebanoff SD, Secrest S, Romain G, Haile ST, Emtage PCR, et al. High‐throughput flow Cytometric method for the simultaneous measurement of CAR‐T cell characterization and cytotoxicity against solid tumor cell lines. SLAS Discov. 2018;23(7):603–12. [DOI] [PubMed] [Google Scholar]
  • 15. de Azevedo JTC, Mizukami A, Moço PD, Malmegrim KCR. Immunophenotypic analysis of CAR‐T cells. Methods Mol Biol. 2020;2086:195–201. [DOI] [PubMed] [Google Scholar]
  • 16. Zheng Z, Chinnasamy N, Morgan RA. Protein L: a novel reagent for the detection of chimeric antigen receptor (CAR) expression by flow cytometry. J Transl Med. 2012;10:29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Nilson BH, Lögdberg L, Kastern W, Björck L, Akerström B. Purification of antibodies using protein L‐binding framework structures in the light chain variable domain. J Immunol Methods. 1993;164(1):33–40. [DOI] [PubMed] [Google Scholar]
  • 18. Hu Y, Huang J. The chimeric antigen receptor detection toolkit. Front Immunol. 2020;11:1770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. De Oliveira SN, Wang J, Ryan C, Morrison SL, Kohn DB, Hollis RP. A CD19/Fc fusion protein for detection of anti‐CD19 chimeric antigen receptors. J Transl Med. 2013;11:23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Laurent E, Sieber A, Salzer B, Wachernig A, Seigner J, Lehner M, et al. Directed evolution of stabilized monomeric CD19 for monovalent CAR interaction studies and monitoring of CAR‐T cell patients. ACS Synth Biol. 2021;10(5):1184–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Demaret J, Varlet P, Trauet J, Beauvais D, Grossemy A, Hego F, et al. Monitoring CAR T‐cells using flow cytometry. Cytometry B Clin Cytom. 2020;100(2):218–24. [DOI] [PubMed] [Google Scholar]
  • 22. Nicholson IC, Lenton KA, Little DJ, Decorso T, Lee FT, Scott AM, et al. Construction and characterisation of a functional CD19 specific single chain Fv fragment for immunotherapy of B lineage leukaemia and lymphoma. Mol Immunol. 1997;34(16–17):1157–65. [DOI] [PubMed] [Google Scholar]
  • 23. Lou Y, Chen C, Long X, Gu J, Xiao M, Wang D, et al. Detection and quantification of chimeric antigen receptor transgene copy number by droplet digital PCR versus real‐time PCR. J Mol Diagn. 2020;22(5):699–707. [DOI] [PubMed] [Google Scholar]
  • 24. Wang N, Hu X, Cao W, Li C, Xiao Y, Cao Y, et al. Efficacy and safety of CAR19/22 T‐cell cocktail therapy in patients with refractory/relapsed B‐cell malignancies. Blood. 2020;135(1):17–27. [DOI] [PubMed] [Google Scholar]
  • 25. Selliah N, Eck S, Green C, Oldaker T, Stewart J, Vitaliti A, et al. Flow cytometry method validation protocols. Curr Protoc Cytom. 2019;87(1):e53. [DOI] [PubMed] [Google Scholar]
  • 26. Mueller KT, Maude SL, Porter DL, Frey N, Wood P, Han X, et al. Cellular kinetics of CTL019 in relapsed/refractory B‐cell acute lymphoblastic leukemia and chronic lymphocytic leukemia. Blood. 2017;130(21):2317–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Mueller KT, Waldron E, Grupp SA, Levine JE, Laetsch TW, Pulsipher MA, et al. Clinical pharmacology of Tisagenlecleucel in B‐cell acute lymphoblastic leukemia. Clin Cancer Res. 2018;24(24):6175–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Cunliffe J, Derbyshire N, Keeler S, Coldwell R. An approach to the validation of flow cytometry methods. Pharm Res. 2009;26(12):2551–7. [DOI] [PubMed] [Google Scholar]
  • 29. Huggett JF. The digital MIQE guidelines update: minimum information for publication of quantitative digital PCR experiments for 2020. Clin Chem. 2020;66(8):1012–29. [DOI] [PubMed] [Google Scholar]
  • 30. Rawstron AC, Orfao A, Beksac M, Bezdickova L, Brooimans RA, Bumbea H, et al. Report of the European myeloma network on multiparametric flow cytometry in multiple myeloma and related disorders. Haematologica. 2008;93(3):431–8. [DOI] [PubMed] [Google Scholar]
  • 31. Abramson JS, Palomba ML, Gordon LI, Lunning MA, Wang M, Arnason J, et al. Lisocabtagene maraleucel for patients with relapsed or refractory large B‐cell lymphomas (TRANSCEND NHL 001): a multicentre seamless design study. Lancet. 2020;396(10254):839–52. [DOI] [PubMed] [Google Scholar]
  • 32. Demeke T, Eng M. Effect of endogenous reference genes on digital PCR assessment of genetically engineered canola events. Biomol Detect Quantif. 2018;15:24–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Pan J, Tan Y, Wang G, Deng B, Ling Z, Song W, et al. Donor‐derived CD7 chimeric antigen receptor T cells for T‐cell acute lymphoblastic leukemia: first‐in‐human, phase I trial. J Clin Oncol. 2021;39(30):3340–51. [DOI] [PubMed] [Google Scholar]
  • 34. Fujiwara K, Masutani M, Tachibana M, Okada N. Impact of scFv structure in chimeric antigen receptor on receptor expression efficiency and antigen recognition properties. Biochem Biophys Res Commun. 2020;527(2):350–7. [DOI] [PubMed] [Google Scholar]
  • 35. Li W, Qiu S, Chen J, Jiang S, Chen W, Jiang J, et al. Chimeric antigen receptor designed to prevent ubiquitination and downregulation showed durable antitumor efficacy. Immunity. 2020;53(2):456–470.e456. [DOI] [PubMed] [Google Scholar]
  • 36. Juillerat A, Tkach D, Busser BW, Temburni S, Valton J, Duclert A, et al. Modulation of chimeric antigen receptor surface expression by a small molecule switch. BMC Biotechnol. 2019;19(1):44. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

Appendix S1 Supporting Information

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MIFlowCyt: MIFlowCyt Item Checklist

Click here for additional data file. (120KB, doc)

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