OVEREXPRESSION OF TUMORPROTECTIVE CLUSTERIN PREVENTS EXHAUSTION OF TRANSGENIC T CELLS
Bibliographic record
Abstract
1 Abstract T cell therapies, such as chimeric antigen receptor (CAR) T cells and T cell receptor (TCR) transgenic (tg) T cells, are a promising approach in the treatment of solid malignancies but are limited by T cell exhaustion caused by chronic antigen stimulation. Clusterin (CLU) is a chaperone protein known to protect both normal and malignant cells, from metabolic stress and reactive oxygen species (ROS). In this study, we investigated whether overexpression (OE) of CLU in TCRtg T cells and CAR-T cells respectively can reduce exhaustion induced by chronic antigen stimulation and enhance T cell functionality against Ewing sarcoma (EwS). Among other cytoprotective genes, we found that CLU was significantly downregulated in dysfunctional tumor infiltrating lymphocytes. Therefore, we engineered EwS-directed TCRtg T cells targeting a Chondromodulin-1 (Chm1)-derived peptide and GD2 CAR-T cells to overexpress CLU. We show here that CLU is downregulated in T cells following activation by tumor cells. CLU-overexpressing T cells exhibit decreased expression of exhaustion markers (PD1, LAG3) and reduced apoptosis after repetitive stimulation. These cells demonstrated improved infiltration into tumor spheroids and maintained functionality under hypoxic conditions. In vivo , CLU-overexpressing T cells showed enhanced persistence and a trend towards reduced tumor growth. Mechanistically, proteomic analysis suggested that reduced ribosomal activity might delay T cell exhaustion, implicating metabolic reprogramming. In conclusion, CLU OE in tg T cells enhances their persistence and functionality by mitigating exhaustion, possibly through modulation of ribosomal activity and metabolic pathways. This strategy holds potential for improving adoptive T cell therapies against solid tumors.
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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) | 0.001 | 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".