Single-Cell Immuno-Metabolic Profiling of Apheresis Samples for Chimeric Antigen T Cell Receptor Therapy Using Cytometry By Time of Flight
Notice bibliographique
Résumé
Introduction Chimeric antigen T cell receptor T (CAR-T) therapy has transformed the treatment of relapsed or refractory (R/R) large B-cell lymphoma (LBCL), offering a potentially curative option for these patients. Despite this, 50-60% of patients experience treatment failure. Emerging research links apheresis T cell subset composition and functionally to CAR-T therapy outcomes. Recent evidence indicates that T cells exploit distinct metabolic pathways throughout their differentiation after antigen encounter and that these metabolic pathways may be targeted to enhance T cell fitness. Currently, sparse data exists on the metabolic activation potential of T cells in patients with LBCL destined for CAR-T. Methods Peripheral blood mononuclear cells (PMBCs) were collected by leukapheresis from 6 patients with LBCL for standard-of-care (SOC) manufacturing of axicabtagene ciloleucel (n=5) or tisagenlecleucel (n=1). Apheresis samples were analyzed both after thawing (unstimulated) or following in vitro activation using a 72-hour CD3/CD28 stimulation protocol. Mass cytometry by time of flight (CyTOF) was used to investigate the dynamics of cellular activation and T cell subset composition at a single-cell resolution. Concurrently, we adapted a CyTOF panel comprising 26 metabolic pathway proteins to examine the metabolic response to stimulation. The selected metabolic markers covered 7 pathways, including transcription factors (HIF1α, KEAP1, p-PCG1a ), fatty acid metabolism (p-ACC, CD36, CPT1A, ACADM), tricarboxylic acid cycle (CS, IDH1), amino acid metabolism (GLS, CD98, GLUD12, p-S6), glycolytic pathway enzymes (PFKFB4, GLUT1, LDHA, HK2, GAPDH, PDK1), mitochondrial metabolism (VDAC1, CytC, ATP5A), and pentose phosphate pathway (G6PD). High dimensional analysis was completed using standard CyTOF workflow. Results The median age was 58 years (range 44-76), all had two prior lines of therapy, and 5/6 were refractory to prior therapy. Following CD3/CD28 stimulation, the CD4:CD8 ratio showed variability across apheresis samples (mean 2.3, coefficient of variation [CV] 58%). The response to stimulation, assessed by changes in the expression of activation markers, significantly differed across T cell subclusters (Kruskal-Wallis test, p<0.0001). Subsets of effector memory CD4 and CD8 T cells showed the greatest increase in overall activation compared to other subsets. CD4 T cells demonstrated a more homogenous activation pattern with lower variability (mean 1.06, SD 0.13), whereas CD8 T cells displayed a more heterogeneous activation pattern (mean 0.63, SD 0.25). To assess inter-patient metabolic expression diversity of different T cell subsets, we determined the change in score following stimulation by calculating a score for each pathway corresponding to the average expression of markers belonging to that pathway. FOXP3+CD39+CD45RO+ CD4 T cells showed the greatest increase in expression of proteins associated with glycolysis (p<0.0001), oxidative phosphorylation (p<0.0001), amino acid metabolism (p<0.0001) and mitochondrial dynamics (p<0.0001) compared to other immune cell subsets. In contrast, CD45RA+TCF1+ CD8 T cells showed low level of metabolic activation. Metabolic phenotype scores showed a high level of heterogeneity in the extent and direction of change in the expression of metabolic pathway markers within a given subset; while some patients showed increased scores, others remained unchanged or decreased. For instance, across 7 metabolic pathways, FOXP3+CD39+CD45RO+ CD4 T cells had a mean score of 0.086 to 0.344 (CV 40-141%); CD45RA+TCF1+ T cells had a mean score of -0.013 to 0.096 (CV 66-703%); CD45RO+TCF1+ T cells had a mean score of -0.017 to 0.178 (CV 52-548%). These findings suggest high inter-patient apheresis metabolic heterogeneity and that metabolic response to stimulation may not be directly correlated with T cell activation. Conclusion We present the development of two CyTOF panels to characterize both immuno-metabolic features and activation dynamics at the single cell level using a total of 65 markers. Our results from the implementation of this panel on apheresis samples from patients destined for CAR-T therapy highlight the importance of understanding the impact of metabolic phenotypes and activation dynamics in CAR-T. Ongoing studies aim to assess the role of immune-metabolic features of CAR-T apheresis samples as a biomarker of response.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».