IL-21-reprogrammed Vδ1 T cells exert killing against solid tumors which is enhanced by CAR arming for off-the-shelf immunotherapy
Bibliographic record
Abstract
Cancer cell therapies have primarily focused on engineering autologous αβ T cells with chimeric antigen receptors (CARs), achieving clinical success against hematologic malignancies. However, their effectiveness against solid tumors is limited by challenges such as antigen escape, suppression by the metabolically hostile tumor microenvironment (TME), and manufacturing difficulties. γδ T cells are unconventional T cells with innate tumor-targeting capabilities independent of MHC class I, making them an emerging candidate for allogeneic cell therapy. While the Vδ1 T cell subset has shown promising anti-tumor killing their clinical application has been hindered by difficulties in achieving robust expansion for therapeutic use. Here, we evaluated the potential of K562 feeder cells expressing membrane-bound IL-21 (K562-mb-IL-21) to expand and activate γδ T cells from peripheral blood. Our findings show that this method preferentially expands Vδ1 T cells, resulting in an activated phenotype characterized by enhanced expression of NK cell activation receptors, innate cytotoxicity against breast and ovarian cancer cells, and sustained metabolic function in patient-derived ascites TME. When engineered with a CAR, Vδ1 T cells exhibited further enhanced anti-tumor efficacy in an immunodeficient NRG xenograft model of human ovarian cancer. These findings highlight K562-mb-IL-21 expanded peripheral blood Vδ1 T cells as a promising 'off-the-shelf' allogeneic therapy for 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".