Targeting of Nrf2 improves antitumoral responses by human NK cells, TIL and CAR T cells during oxidative stress
Bibliographic record
Abstract
BACKGROUND: Adoptive cell therapy using cytotoxic lymphocytes is an efficient immunotherapy against solid and hematological cancers. However, elevated levels of reactive oxygen species (ROS) in the hostile tumor microenvironment can impair NK cell and T cell function. Auranofin, a gold (I)-containing phosphine compound, is a strong activator of the transcription factor Nrf2. Nrf2 controls a wide range of downstream targets important for the cells to obtain increased resistance to ROS. In this study, we present a strategy using auranofin to render human cytotoxic lymphocytes resistant toward oxidative stress. METHODS: Melanoma patient-derived tumor infiltrating lymphocytes (TIL) and healthy donor-derived NK cells and CD19-directed CAR T cells were pretreated with a low dose of auranofin. Their resistance toward oxidative stress was assessed by measuring antitumoral responses (killing-assay, degranulation/CD107a, cytokine production) and intracellular ROS levels (flow cytometry) in conditions of oxidative stress. To confirm that the effects were Nrf2 dependent, the transcription level of Nrf2-driven target genes was analyzed by qPCR. RESULTS: levels or monocyte-derived ROS. Furthermore, auranofin pretreatment of CD19 CAR-T cells or TIL increased their elimination of CD19 +tumor cells or autologous tumor spheroids, respectively, especially during ROS exposure. Analysis of Nrf2-driven target genes revealed that the increased resistance against ROS was Nrf2 dependent. CONCLUSION: These novel findings suggest that Nrf2 activation in human cytotoxic lymphocytes could be used to enhance the efficacy of adoptive cell therapy.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".