418 Dendritic cell-activation of T cells provides metabolic signals for improved <i>in vivo</i> persistence and anti-tumour performance
Bibliographic record
Abstract
Background Adoptive cellular therapy (ACT), such as chimeric antigen receptor (CAR)-T cell therapy, has provided impressive clinical efficacy for treatment of patients with hematological malignancies.1 However, its efficacy for solid tumours has been limited2 largely due to immunosuppressive signals and competition for metabolic resources in the tumour microenvironment (TME) that can limit the efficacy and killing capacity of T cells.3 Overcoming these metabolic challenges is vital for enhancing the efficacy of ACT. Many strategies for reprogramming T cell metabolism have emerged in the literature, however the impact of the clinical activation strategy – bead-bound anti-CD3 and anti-CD28 antibodies – remains largely unexplored. This study aims to compare the metabolic programming, effector function, persistence and resilience to metabolic stress of T cells activated with bead-bound antibodies (beads) or with dendritic cells (DCs). Methods We isolated CD8+ T cells from P14 mice that have a transgenic T cell receptor recognizing the gp33 peptide from lymphocytic choriomeningitis virus (LCMV). These T cells are co-cultured with either LPS-activated bone-marrow derived DCs pulsed with gp33 peptide, or with beads at ratios of 1:1 or 10:1 (beads:T cells). These T cells were activated for 72 hours followed by baseline analysis, transfer into in vitro stress conditions, or use for ACT in mice with established B16-gp33 melanoma tumours. Results Increasing the ratio of beads resulted in increased oxidative and glycolytic metabolism to a level comparable with DC-activated T cells. Despite the comparable bioenergetic profile, DC-activated T cells demonstrated superior tumour clearance compared to all ratios of bead-activated T cells. Additionally, in vivo in the same TME, DC-activated T cells showed significantly improved survival, suggesting DC-activation provides additional signals that confer resiliency to stress and improved function. Similar resiliency was observed in DC-activated T cells in vitro after exposure to stress conditions such as IL-2 withdrawal. Metabolomics and RNA-sequencing analyses have revealed distinct signaling and phospholipid metabolism between DC and bead-activated T cells. Conclusions DC-activation of T cells provides superior programming for efficacy, survival and resiliency to stress compared to bead-activation. These enhanced metrics are associated with unique metabolic and transcriptional programming. Current investigations are ongoing to determine if altering phospholipid metabolism in bead-activated T cells is sufficient to enhance their efficacy and resilience. This could then be incorporated into clinical protocols to improve ACT outcomes for patients. References Cappell KM, Kochenderfer JN. Long-term outcomes following CAR T cell therapy: what we know so far. Nat Rev Clin Oncol 2023;20:359–71. Hou B, Tang Y, Li W, Zeng Q, Chang D. Efficiency of CAR-T therapy for treatment of solid tumor in clinical trials: a meta-analysis. Dis Markers 2019;2019:e3425291. Hou AJ, Chen LC, Chen YY. Navigating CAR-T cells through the solid-tumour microenvironment. Nat Rev Drug Discov 2021;20:531–50. Ethics Approval This study was approved by the University Health Network Animal Care Committee approval number 929 and 6895.
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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) | 0.002 | 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".