Abstract LB094: Arginine pre-conditioning improves T-cell potency and metabolic fitness measured by real-time impedance and seahorse assays
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".