# Glycolytic activity following anti-CD19 CAR-T cell infusion in non-Hodgkin lymphoma

Nicolas Vallet, Laurianne Drieu Larochelle, Maria-joao Santiago-ribeiro, Alban Villate, Martin Eloit, Arnaud Cirée, Laura Zaragoza, Virginie André, Marie Prat-lepesant, Olivier Herault, et al.

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Nicolas Vallet, Laurianne Drieu Larochelle, Maria-joao Santiago-ribeiro, Alban Villate, Martin Eloit, et al.. Glycolytic activity following anti-CD19 CAR-T cell infusion in non-Hodgkin lymphoma. European Journal of Clinical Investigation, 2025, 55 (2), pp.e14342. 10.1111/eci.14342. hal-04920301

HAL Id: hal-04920301

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Submitted on 30 Jan 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Glycolytic activity following anti-CD19 CAR-T cell infusion in non-Hodgkin lymphoma

Nicolas Vallet<fnref n="1" />,<fnref n="2" /> | Laurianne Drieu Larochelle<fnref n="1" /> | Maria-Joao Santiago-Ribeiro<fnref n="3" /> |

Alban Villate<fnref n="1" /> | Martin Eloit<fnref n="1" /> | Arnaud Cirée<fnref n="4" /> | Laura Zaragoza<fnref n="5" /> |

Virginie André<fnref n="5" /> | Marie Prat-Lepesant<fnref n="6" /> | Olivier Hérault<fnref n="2" />,<fnref n="7" /> | Flavie Arbion<fnref n="8" /> |

Hélène Blasco<fnref n="9" /> | Emmanuel Gyan<fnref n="1" />,<fnref n="2" />

**Correspondence**

Nicolas Vallet, Service d'Hématologie et Thérapie Cellulaire, CHU de Tours, hôpital Bretonneau, 2 boulevard Tonnellé, 37044 Cedex 9, Tours, France.

Email: nicolas.vallet@univ-tours.fr

**KEYWORDS**

CAR-T cells, cell therapy, metabolism, NHL, T cells

## 1 | INTRODUCTION

The antitumor activity of chimeric antigen receptor-T cells (CAR-T) relies on the recognition of the tumour antigen, for example, CD19, by a single-chain variable immunoglobulin fragment linked to TCRζ fused to a costimulatory domain, which activates T cells without major histocompatibility complex (MHC) restriction.<fnref n="1" /> Early toxicity, namely cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), are driven by myeloid-cell activation through IFNγ produced by the activated CAR-T or damage associated molecular patterns derived from lysed tumour cells.<fnref n="2" />,<fnref n="3" />

In addition to oxidative phosphorylation (OXPHOS), conventional and CAR-T cell activation triggers a metabolic

switch toward a higher glycolytic rate, even in aerobic conditions.<fnref n="4" /> The activation of CAR-T cells with the CD28 costimulatory domain favour glycolysis metabolism, while 4-1BB costimulatory rely on OXPHOS through mitochondrial biogenesis.<fnref n="4" /> Myeloid-cells also produce lactate after triggering glycolysis upon activation.<fnref n="5" /> The preference for glycolysis metabolism in aerobic setting is also known as the Warburg effect. Glycolysis metabolism to produce energy in the form of ATP. Though glycolysis is less efficient than OXPHOS in ATP production, glycolysis also provides carbon and NADPH which fuels acetyl-CoA, palmitate, amino acids ad nucleotides allowing biomass production.<fnref n="6" /> Lactate being an end-products of glycolysis, high peripheral blood lactate levels in aerobic conditions have been reported in the context of proliferative haematological malignancies.<fnref n="6" />,<fnref n="7" />

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2024 The Author(s). *European Journal of Clinical Investigation* published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation.

<footnote n="1">¹Hematology and Cell Therapy, University Hospital Tours, Tours, France</footnote>

<footnote n="2">²Inserm U1069 N2COx, University of Tours, Tours, France</footnote>

<footnote n="3">³Department Imaging Nuclear Medicine, University Hospital Tours, Tours, France</footnote>

<footnote n="4">⁴Department of Biological Immunology, University Hospital Tours, Tours, France</footnote>

<footnote n="5">⁵Department of Pharmacy, University Hospital Tours, Tours, France</footnote>

<footnote n="6">⁶Etablissement Français du Sang, Tours, France</footnote>

<footnote n="7">⁷Department of Biological Hematology, University Hospital Tours, Tours, France</footnote>

<footnote n="8">⁸Department of Pathology, University Hospital Tours, Tours, France</footnote>

<footnote n="9">⁹Department of Biochemistry, University Hospital Tours, Tours, France</footnote>
We hypothesized that blood lactate levels may rise following CAR-T cell infusion as a surrogate of immune cells aerobic glycolysis triggered by their respective activation (Figure 1A).

## 2 | METHODS

### 2.1 | Study design

We explored this hypothesis by conducting a retrospective monocentric study of blood lactate levels following CAR-T cell infusion from April to October 2023 at the Tours University Hospital. The study was registered with the Commission Nationale de l'Informatique et des Libertés (CNIL, LACTCAR #2024_052) and approved by the local ethics committee (#2024 016). Written informed consent was obtained from all patients. Considering that only 2 patients received 4-1BB harbouring CAR-T, these patients were excluded.

### 2.2 | Peripheral blood lactate assays

During hospitalization for CAR-T cell infusion, venous peripheral blood lactate levels were routinely monitored in the haematological intensive care unit and arterial lactates were assessed in cases of suspected organ failure. Vein samplings were performed in sodium fluoride/ potassium oxalate tube and analysed within 4 hours by a standardized para-amino antipyrine assay (Lactate Gen.2, #08057940190, Roche Cobac) on a Cobac c analyser. Arterial samplings were performed in dry lithium heparin syringe (7IU/mL) upon medical request for monitoring of arterial blood gas, and analysed in less than 30 minutes by amperometric measuring principle on an ABL90 blood gas analyser (Radiometer Medical ApS) in a NF EN ISO 15189 certified department.

### 2.3 | Anti-CD19 CAR-T cells monitoring by flow cytometry

During the procedure hospitalization period, routine peripheral blood circulating CAR-T cells were evaluated at least twice a week. In case of grade 4 leukopenia associated with no circulating lymphocytes, the assay could be cancelled. Peripheral blood samples were collected in EDTA tube and processed within the workday. A volume of 100 µL of whole blood was incubated with 2 µL of CD19 CAR detection reagent (#130-129-550, Miltenyi Biotec) or with phosphate buffered saline (PBS) for 10 minutes at room temperature. After adding 2 mL of PBS, sample was centrifuged 300g for 5 minutes then supernatant was discarded. Cells were resuspended and incubated for 10 minutes at room temperature in residual volume with a mix of antibody anti-biotin-PE (#130-111-068, Miltenyi Biotec, 2 µL), anti-CD8-FITC (#A07756, Beckman Coulter. 5 µL), anti-CD4-PC7 (#737660, Beckman Coulter, 5 µL), anti-CD3-PB (#B49204, Beckman Coulter, 5 µL), anti-CD45-KO (#B36294, Beckman Coulter, 5 µL). Then 1 mL of Versalyse (#A09777, Beckman Coulter) was added. Thorough vortexing was performed for 2 seconds and the sample was incubated 15 minutes at room temperature. The sample was washed twice with PBS, 300g, 5 minutes. Data was acquired with DxFlex (Beckman Coulter) with an objective set to 10⁴ CD3+ cells, and analysed with Kaluza. CAR-T cells were identified according to the expression of CD45, CD3, high fluorescence detection in PE channel (Figure S1). All assays were performed in a NF EN ISO 15189 certified department. Patients' individual available data are depicted in Figure S2.

### 2.4 | Data analysis

Data were analysed in an R environment built by Guix, a functional cross-platform package manager which allows

**FIGURE 1** Blood lactate levels following anti-CD19 chimeric antigen receptor-T cell (CAR-T) infusion. (A) Overview of the study. (B) Swimmer plot showing the individual follow-up of included patients. Lactate levels were measured during the infusion procedure. (C) Boxplot depicting baseline blood lactate levels at day 0 (D0). Deauville scale before infusion and lymphoma subtypes are shown: Diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL). (D) Dot plot showing blood lactate levels following CAR-T cell infusion. The curve depicts the mean lactate levels with the corresponding 95% confidence interval. Dotted horizontal line shows normal range. (E) Dot plot showing blood lactate levels variations according to baseline. (F) Boxplot summarizing the individual areas under the curve of lactate (AUC) levels. High and low AUC groups were defined as those above and below the median AUC, respectively. (G) Dot plot of lactate levels over time for the high and low AUC groups with the regressed mean value and its corresponding 95% confidence interval. (H) Boxplot showing the mean CD8+/CD4+ ratio according to AUC group for the 14 patients with detectable circulating CAR-T during the evaluation of lactate levels. (I) Kaplan-Meier survival curve showing the time to last circulating CAR-T. The non-parametric Mann-Whitney-Wilcoxon test was used to compare quantitative variables and compute the P-value. Hazard ratios (HRs) and their corresponding *p*-values were computed using the Cox model. (J) Alluvial plots showing Deauville scores evaluated by positron emission tomography (PET) from baseline to 3 months following CAR-T cell infusion. The missing patient at 3 months corresponds to #7, who died from a septic event.
<figure><img src="image_3.png" /><figcaption>(B)</figcaption></figure>

<figure><img src="image_4.png" /></figure>

Last circulating CAR-T detection ○

Outcome ▲ Progression * Death

<figure><img src="image_5.png" /><figcaption>(C)</figcaption></figure>

PET evaluations
Deauville scale

<figure><img src="image_6.png" /><figcaption>(D)</figcaption></figure>

<figure><img src="image_7.png" /></figure>

Time from CAR-T cells infusion (days)

<figure><img src="image_8.png" /><figcaption>(E)</figcaption></figure>

Time from CAR-T cells
infusion (days)

<figure><img src="image_9.png" /><figcaption>(F)</figcaption></figure>

AUC
group

<figure><img src="image_10.png" /><figcaption>(G)</figcaption></figure>

Time from CAR-T cells
infusion (days)

<figure><img src="image_11.png" /><figcaption>(H)</figcaption></figure>

p=0.0011

<figure><img src="image_12.png" /><figcaption>(I)</figcaption></figure>

Time from CAR-T cells
infusion
(days)

At risk

<table><tr><td>High</td><td>8</td><td>8</td><td>5</td><td>4</td><td>2</td><td>2</td><td>1</td><td>1</td></tr><tr><td>Low</td><td>9</td><td>2</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></table>

HR=0.87 (95%CI: 0.78-0.98)
p=0.022

<figure><img src="image_13.png" /><figcaption>(J)</figcaption></figure>

All patients

Baseline 1 month 3 months

<figure><img src="image_14.png" /></figure>

Lactate AUC high

Baseline 1 month 3 months

<figure><img src="image_15.png" /></figure>

Lactate AUC low

Baseline 1 month 3 months

PET evaluations
Deauville scale

<figure><img src="image_16.png" /></figure>

Events

▲ Progression

* Death
computing environment reproducibility. All data analyses methods are described in a dedicated git repository (https://gitlab.com/nivall/car-lacates).

# 3 | RESULTS

## 3.1 | Patients' characteristics

Median age of the 17 included patients was 63 (interquartile range, IQR: 53–74) years and 8 (47%) were female. Fourteen (82%) were diagnosed with DLBCL, 2 (12%) with follicular lymphoma, and 1 (6%) with mantle cell lymphoma. Twelve (71%) patients received CAR-T in the second line of treatment, 2 (11%) in the third line and 3 (18%) in the fourth or more lines. At the time of lymphodepletion chemotherapy, a complete response was achieved for 3 (18%) patients, partial responses for 3 (18%) and stable disease for 2 (11%), whereas 9 (53%) had progressive disease. The CAR-T cell products were axicabtagene ciloleucel (n=16) and brexucabtagene autoleucel (n=1). None of the patients received artificial nutrition during the study period. The median value of the 85 mean arterial pressures collected at the time of the lactate peak was 88 (IQR: 77–95, min-max 65–124) mmHg. Median follow-up was 185 (IQR: 144–239) days, 2 (11%) patients relapsed at D + 27, D + 64 and one (5%) died from an infectious event at D + 55 (Figure 1B).

## 3.2 | Lactate levels

Among the 274 evaluated lactate levels, 243 (89%) and 31 (11%) samples were from venous and arterial punctures, respectively. Arterial and veinous lactate levels were not statistically different (p=0.56). Median baseline lactate levels was 0.85 (IQR: 0.70–1.03) mmol/L (Figure 1C). Lactate levels increased following infusion, with a peak between D + 10 and D + 13 (Figure 1D,E). Median lactate levels were 1.5 (IQR: 1.1–2.0) mmol/L. Lactate levels at time of fasting from 4:30 AM to 8:00 AM were comparable with that of other timepoints (Figure S3).

## 3.3 | Lactate area under the curve and outcome

The area under the curve (AUC) of lactate levels of each patient identified two groups separated by the median value: high and low AUC groups (Figure 1F,G). The AUC groups were not associated with the disease status (p=0.13) or Deauville score before lymphodepletion (p=0.99).

Median cumulative incidence of CRS and ICANS were comparable in high AUC and low AUC patients (p=0.61 and p=0.20).

Among the 14 (82%) patients with detectable CAR-T during the procedure. High AUC was associated with a higher mean CD8+/CD4+ CAR-T ratio and a median ratio of 3.25 (IQR: 2.35–10.12) versus 1.04 (0.78–1.81) in the low AUC group (p = 0.0011, Figure 1H). A high AUC was associated with a lower risk of losing CAR-T cell detection in the blood (HR=0.87, 95% confidence interval: 0.78–0.98, p=0.022, Figure 1I).

The Deauville score at 1 month and 3 months following CAR-T cell infusion was comparable between the high and low AUC groups (p = 0.99 and p=0.99, respectively). The 2/17 (12%) patients (#2, #5) with progressive disease following CD28-CAR-T cell infusion were from the low AUC group (Figure 1J). The 3 months progression free survival in the low AUC patient group was thus 66% (95%CI: 42–100) compared to no events in the high AUC group (p = 0.08). Overall survival was not statistically different in between the two groups (p = 0.35).

## 3.4 | Individual lactate levels and early toxicities

Next, we evaluated whether the lactate levels were associated with early toxicity. Because toxicity can be time dependent, we studied individual lactate levels and the timing of early toxicity. CRS and ICANS, graded according to American Society for Transplantation and Cellular Therapy, raised concomitantly with lactate levels (Figure 2A).

## 3.5 | Individual lactate levels and biological covariates

To identify covariates potentially associated with differences in lactate levels, we studied the correlation of lactate levels with several biological results. Lactate levels were associated with higher lymphocyte count (rho=0.26, p=0.021), probably due to the first elevation of lymphocytes from day 5 to 10 (Figure 2B,C). Circulating CAR-T cell counts were not associated with lactate levels (Figure 2B). Lactate levels were associated with higher monocyte counts (rho=0.43, p<0.001) (Figure 2B,C).

Intriguingly, lactate levels were also associated with urea levels (rho=0.37, p<0.001, Figure 2B,C). Elevated urea levels may not be related to its accumulation due to acute kidney injury, notably, as creatinine levels negatively correlated with lactate levels in the absence of kidney injury (Figure 2B).
<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /><figcaption>**FIGURE 2** Lactate levels curves with cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) events and correlations with biological variables. (A) Dot plots depicting individual blood lactate levels following CAR-T cell infusion and the corresponding CRS and ICANS grades according to American Society for Transplantation and Cellular Therapy. (B) Heatmap showing the correlation between individual lactate levels, all time points and other individual biological variables. Correlations and P-values were computed using Spearman's non-parametric method and adjusted using the Benjamini and Hochberg method. Significant correlations are shown on the left-hand side of the vertical dotted line. (C) Dot plots showing the levels of variable were significantly correlated with lactate levels. The black curve depicts the mean values, with the corresponding 95% confidence interval shown in grey.</figcaption></figure>
Glucose levels were significantly associated with lactate levels (rho=0.38, p<0.001) which may reflect corticosteroid administration at time of toxicity (Figure 2B,C, Figure S4). The median prednisolone equivalent cumulative doses were similar in the high and low lactate AUC groups, 2232 (IQR: 1315-2465) mg and 1138 (IQR: 1005-3126) mg, respectively (p=0.232).

## 4 | DISCUSSION

We report a rise in peripheral blood lactate levels which may reflect immune cells activation following CD28 harbouring anti-CD19 CAR-T cells in NHL.

Because lactate levels above 2mmol/L also defines sepsis shock due to tissue hypoperfusion, we collected concomitant arterial pressure data in the patient cohort.⁸ suggesting that the lactates were produced in aerobic conditions and thus due to aerobic glycolysis. CD28 co-stimulatory domain CAR-T are associated with glycolytic activity observed in vitro in CD28 CAR-T triggered.⁴

Because CD8+/CD4+ CAR-T ratio is associated with immune responses we compared ratio from high AUC and low AUC groups.⁹ High lactate AUC was associated with higher CD8+/CD4+ ratio. This result may be explained by the higher glycolytic rate of CD8+ T cells following their activation with anti-CD3 and anti-CD28 coated plates.¹⁰

Higher lactate AUC was associated with longer peripheral blood persistence of CAR-T cells. Higher glycolytic metabolism is associated with higher effector memory (EM) cell phenotypes in anti-CD19 CAR-T following CD19 stimulation, whereas OXPHOS metabolism is associated with higher central memory (CM) phenotypes.⁴ Given the retrospective nature of this study, we were unable to explore CAR-T cell memory phenotypes.

Lactate AUC were not associated with higher incidence of CRS or ICANS, but lactate peaks coincided with first CRS and ICANS manifestations. Higher expression of glycolysis pathway genes and the gene encoding 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase in T cells is a driver of CRS following CAR-T cell infusion and SARS-CoV-2 and influenza infections.¹¹

Individual lactate levels were associated with lymphocyte count but not with circulating CAR-T counts. This may be related to the low number of CAR-T monitoring events, leading to a low statistical power, or to a intratumoral retention of the active CAR-T cells.

Interestingly, individual lactate levels were correlated with monocytes counts. A positive correlation with monocyte counts may be related to the occurrence of CRS and

ICANS following an increase in lactate levels, as illustrated in mouse models, which exhibit higher monocyte counts and higher levels of pro-inflammatory cytokines, notably IL-6.¹² A correlation with monocyte counts suggests that monocyte activation may also produce lactates through high glycolytic activity, as observed in activated monocytes.⁵

Surprisingly, individual lactate levels were correlated with urea levels. Elevated urea levels have been reported for haematological malignancies with high arginase activity.¹³ Arginase produces urea by converting L-arginine to L-ornithine. In macrophages, arginase-2 activity participates in the downregulation of pro-inflammatory cytokines, such as IL-1β and IL-6, also known to participate in CRS and ICANS.²,¹⁴ A high glycolytic rate may also argue in favour of arginase activity, as it deprives cells of L-arginine, a glycolysis repressor in T cells.¹⁵

Corticosteroid and glucose metabolism perturbation may also explain higher lactate levels. Still, individual patients plots and corticosteroid administration did not reveal a specific pattern of lactate increase following first corticosteroids administration.

## 5 | CONCLUSIONS

We describe an increase in blood lactate levels in the first two weeks following the infusion of CAR-T. High lactate levels were associated with a higher CD8+/CD4+ CAR-T cell ratio and a longer period of circulating CAR-T. This observation may indicate a glycolytic activity in a context of lymphoid or myeloid immune responses following CAR-T infusion. Lactate kinetics could potentially be used as a marker of glycolytic switch and eventually guide future metabolic intervention.

### AUTHOR CONTRIBUTIONS

Study conception and design: N.V. and E.G., data collection and curation: N.V. and A.V., analytic methods and materials: N.V., P.E.T. interpretation: M.J.S.R., C.A.R-T. cell quantification: A.C., Interpretation of the results: N.V., E.G., L.D.L.R., M.E. and A.V., Figures conception: N.V., preparation of the draft manuscript: N.V. All authors critically reviewed the manuscript and approved the final version.

### FUNDING INFORMATION

N/A.

### CONFLICT OF INTEREST STATEMENT

EG: honoraria from GILEAD, Novartis and BMS. LDLR: honoraria from GILEAD and Novartis.
DATA AVAILABILITY STATEMENT

Anonymized raw data, the source code of the analyses, in-
structions to reproduce the computing environment, un-
formatted analysis reports and figures are available online
(https://gitlab.com/nivall/car-lacates) and long term pre-
served at Software Heritage (https://archive.softwarehe-
ritage.org/swh:1:dir:a5c9b94421a6b5d6d0ab89e4aee752
b540e7364f).

CONSENT

Written informed consent was obtained from all patients.

ORCID

Nicolas Vallet https://orcid.org/0000-0001-9568-3358

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SUPPORTING INFORMATION

Additional supporting information can be found online
in the Supporting Information section at the end of this
article.

**How to cite this article:** Vallet N, Drieu
Larochelle L, Santiago-Ribeiro M-J, et al. Glycolytic
activity following anti-CD19 CAR-T cell infusion in
non-Hodgkin lymphoma. *Eur J Clin Invest.*
2024;00:e14342. doi:[10.1111/eci.14342](https://doi.org/10.1111/eci.14342)