Single-Cell Immuno-Metabolic Profiling of Apheresis Samples for Chimeric Antigen T Cell Receptor Therapy Using Cytometry By Time of Flight
Authors: Gong Inna Y , Rodrigo Rowena , Boukhaled Giselle M , Tran Daisy V. , Coley Tiana , Noamani Babak , Teh Evelyn , Pinto Devanand M. , Wang Ben X , Kuruvilla John , Saibil Samuel D. , Laister Rob C. 
Published: December 2024

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&lt;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&lt;0.0001), oxidative phosphorylation (p&lt;0.0001), amino acid metabolism (p&lt;0.0001) and mitochondrial dynamics (p&lt;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.