Abstract LB094: Arginine pre-conditioning improves T-cell potency and metabolic fitness measured by real-time impedance and seahorse assays
Notice bibliographique
Résumé
Abstract Background: Enhanced T cell performance and fitness are imperative for the success of adoptive T cell-based therapies. Beyond the types of genetic modifications to CAR/TCR T cells, there is a growing body of literature demonstrating that relatively simple preconditioning protocols can also be used to improve T cell fitness/function. We studied, the impact that preconditioning in elevated concentrations of leucine, glutamine, and arginine has on the killing efficacy and bioenergetics of MART-1-specific TCR T cells. Methods: Using the Agilent xCELLigence RTCA eSight and Seahorse we assessed the killing efficiency and bioenergetics of engineered T-cells after Arginine, Glutamine, and Leucine pre-conditioning using MART-1 specific TCR T cells. CD3+ T-cells (Hemacare, Seattle, WA) were transduced with retrovirus SAMEN-DMF5 with a CD34 marker gene, against MART-1. The T cells were pre-conditioned in a range of concentrations varying between 0-6mM for 7 days, followed by a killing assay using MART-1 expressing melanoma cell line as target cells (624.38) engineered to express a red-fluorescent nuclear protein. The comparison was made with transduced T-cells grown in RPMI (no added amino acid supplementation denoted as RPMI_TCR), RPMI supplemented with Arginine (Arg_TCR) and non-transduced T cells. The T cell killing was measured using impedance/imaging-based assays. CD34 assessment was performed using Novocyte and SRC (spare respiratory capacity) and oxygen consumption rate (OCR) were measured using seahorse assays. Results: Whereas supplementing the growth medium with 6 mM Arginine increased killing efficacy dramatically (up to ∼6-fold), elevated leucine and glutamine concentrations were found to have minimal impact on MART-1 TCR T cell killing of melanoma cells. Arginine (6 mM) supplementation increased basal respiration, ATP linked OCR, and maximal respiration compared to the RPMI control. SRC of Arg_TCR T cells was significantly higher than RPMI preconditioned T cells, a parameter previously correlated with T cell persistence. To check the effect of a shortened pre-conditioning period, 2, and 4 days of pre-conditioning were done along with the 7 days method. After a preconditioning step of only 2 days, Arginine preconditioned T cells acquired a killing efficacy that is >2x higher compared to their counterparts RPMI_TCR T cells. Extending the duration of preconditioning from 2 to 4 days has minimal impact on the RPMI control T cells but more than doubles the killing efficacy of the high Arg grown T cells. Conclusions: In conclusion, Arginine pre-conditioning significantly improved T cell potency and mitochondrial respiration through metabolic rewiring. Citation Format: Rashmi R. Pillai, Xiaoyu Zhang, Yama Abassi, Brandon Lamarche, Mark M. Garner. Arginine pre-conditioning improves T-cell potency and metabolic fitness measured by real-time impedance and seahorse assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB094.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».