Efficient Regeneration of Human Vα24+ Invariant Natural Killer T Cells and Their Anti-Tumor Activity In Vivo
Bibliographic record
Abstract
Abstract Reprogramming of antigen-specific T lymphocytes into induced pluripotent stem cells (iPSCs) and their subsequent re-differentiation has enabled expansion of functional T lymphocytes in vitro, thus opening up new approaches for immunotherapy of cancer and other diseases. In this study, we have established a robust protocol to reprogram human invariant NKT (Vα24+ iNKT) cells, which have been shown to act as cellular adjuvants and thus exert anti-tumor activity in mice and humans, and to re-differentiate the iNKT cell-derived iPSCs into functional iNKT cells. These iPSC-derived iNKT cells (iPS-Vα24+ iNKT cells) can be activated by ligand-pulsed dendritic cells (DCs) and produce a large amount of interferon-γ upon activation, as much as parental Vα24+ iNKT cells, but exhibit even better cytotoxic activity against various tumor cell lines. The iPS-Vα24+ iNKT cells possess significant anti-tumor activity in tumor-bearing mice and can activate autologous NK cells upon activation by ligand-pulsed DCs in the NOG mouse model in vivo, further extending their therapeutic potential. This study thus provides a first proof of concept for the clinical application of human iPS-Vα24+ iNKT cells for cancer immunotherapy.
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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) | 0.001 | 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".