Single-cell metabolic profiling of T cells identifies a fitness signature associated with early CAR T failure in large B cell lymphoma
Notice bibliographique
Résumé
Abstract Introduction Chimeric antigen receptor T cell (CAR T) therapy for relapsed/refractory large B-cell lymphoma (LBCL) achieves long-term remission in only about 30-40% of patients. Prior studies suggest that T cell subset composition and functionality are associated with CAR T outcomes. While T cells are known to rely on distinct metabolic pathways for differentiation and function, limited data exists on how metabolic activation potential influences early CAR T failure. Methods Peripheral blood mononuclear cells (PBMCs) were collected via leukapheresis from pts with LBCL prior to CAR-T and healthy donors (HD). PBMCs were analyzed with and without 72-hour CD3/CD28 stimulation using mass cytometry by time of flight (CyTOF) to assess T cell activation and subset composition. A CyTOF panel of 26 key proteins spanning 7 metabolic pathways was used to evaluate the metabolic response. 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 (TCA) (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 performed using standard CyTOF workflows. The cyCombine algorithm was applied to integrate single-cell datasets from resting and stimulated samples, using a healthy donor reference sample across batches for normalization. Metabolic scores were calculated as the difference in summed marker expression between unstimulated and stimulated conditions for each pathway, and compared using Wilcoxon rank sum test. Results Single-cell analysis included 22 patients (21 axi-cel, 1 tisa-cel; median age 62 years [range 39-76]). Diagnoses included DLBCL (n=14), HGBL (n=5), transformed lymphoma (n=2), and PMBCL (n=1). Most patients were refractory to their last treatment (64%) and had received a median of 2 prior lines of therapy. High-risk features included IPI scores of 3–5 (41%), ECOG ≥2 (23%), stage III-IV disease (64%), elevated LDH (median 281 U/L), and ≥2 extranodal sites (18%). At 3-month post CAR T, 15/22 patients achieved complete response (CR), 4/22 had partial response (PR), and 3/22 had progressive disease (PD). Metabolic activation scores were consistently higher in patients with CR and PR compared to PD across most pathways in both CD8⁺ and CD4⁺ T cells. Amino acid metabolism scores were significantly higher in patients with early CR (CD8⁺: 0.54 ± 0.36, CD4⁺: 0.73 ± 0.59) compared to early PD (CD8⁺: 0.04 ± 0.09, CD4⁺: 0.09 ± 0.16, p<0.001), while PR was similar to CR (CD8⁺: 0.52 ± 0.22, CD4⁺: 0.66 ± 0.41). Similar patterns were observed in the following pathway scores: Glycolysis (CD8⁺ CR 0.38 ± 0.32, PR 0.37 ± 0.21, PD -0.06 ± 0.13, p<0.05; CD4⁺ CR 0.54 ± 0.41, PR 0.63 ± 0.36, PD ≈0.00 ± 0.07, p<0.001); Mitochondrial dynamics (CD8⁺ CR 0.41 ± 0.38, PR 0.45 ± 0.30, PD -0.20 ± 0.26, p<0.05; CD4⁺ CR 0.70 ± 0.72, PR 0.67 ± 0.87, PD -0.30 ± 0.26, p<0.001); Pentose phosphate pathway (CD8⁺ CR 0.33 ± 0.49, PR 0.52 ± 0.26, PD -0.02 ± 0.11; CD4⁺ CR 0.51 ± 0.34, PR 0.73 ± 0.14, PD 0.07 ± 0.18, p<0.05); TCA (CD8⁺ CR 0.45 ± 0.31, PR 0.52 ± 0.19, PD 0.06 ± 0.15; CD4⁺ CR 0.58 ± 0.33, PR 0.74 ± 0.21, PD 0.12 ± 0.11); Fatty acid metabolism (CD8⁺ CR 0.41 ± 0.37, PR 0.55 ± 0.34, PD -0.23 ± 0.56; CD4⁺ CR 0.52 ± 0.42, PR 0.61 ± 0.39, PD -0.32 ± 0.36, p<0.001); Transcriptional pathway (CD8⁺ CR 0.46 ± 0.37, PR 0.40 ± 0.17, PD -0.05 ± 0.15, p<0.05; CD4⁺ CR 0.72 ± 0.63, PR 0.61 ± 0.50, PD -0.09 ± 0.12, p<0.001). Conclusion Patients with early PD following CAR-T therapy demonstrated consistently reduced metabolic activation across multiple metabolic pathways in CD8⁺ and CD4⁺ T cell subsets in pre-infusion PBMCs, suggesting impaired metabolic fitness may underlie treatment resistance. These findings support development of an ImmunoFit score to quantify T cell metabolic readiness and inform strategies to enhance CAR T efficacy, such as metabolic modulation prior to infusion. Further validation is ongoing, but this framework offers a biological approach to improving clinical outcomes.
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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 | 0,000 |
| 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 ».