Single-Cell Immuno-Metabolic Profiling of Apheresis Samples for Chimeric Antigen T Cell Receptor Therapy Using Cytometry By Time of Flight
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".