Developing Allogeneic Double-Negative T Cells as a Novel Off-the-Shelf Adoptive Cellular Therapy for Cancer
Bibliographic record
Abstract
Abstract Purpose: To expand clinical-grade healthy donor-derived double-negative T cells (DNT) to a therapeutically relevant number and characterize their potential to be used as an “off-the-shelf” adoptive cellular therapy (ACT) against cancers. Experimental Design: We developed methods to expand DNTs under GMP conditions and characterized their surface molecule expression pattern using flow cytometry–based high-throughput screening. We investigated the off-the-shelf potential of clinical-grade DNTs by assessing their cytotoxicity against various cancer types and their off-tumor toxicity in vitro and in xenograft models and determining the effect of cryopreservation under GMP conditions on cell viability and cytotoxicity. Further, we determined the susceptibility of DNTs to conventional allogeneic T cells in vitro and in vivo. Results: Clinical-grade DNTs expanded 1,558 ± 795.5-fold in 17 days with >90% purity. Expanded DNTs showed potent in vitro cytotoxic activity against various cancer types in a donor-unrestricted manner. DNTs enhanced the survival of mice infused with a lethal dose of EBV-LCL and significantly reduced leukemia engraftment in xenograft models. Expanded DNTs cryopreserved using GMP-compliant reagents maintained viability and anticancer functions for at least 600 days. Live allogeneic DNTs did not induce cytotoxicity of alloreactive CD8+ T cells in vitro, and coinfusion of DNTs with peripheral blood mononuclear cells (PBMC) from a different donor into mice resulted in coengraftment of DNTs and PBMC-derived allogeneic conventional T cells in the absence of cytotoxicity toward DNTs, suggesting the lack of host-versus-graft reaction. Conclusions: We have established a method to generate therapeutic numbers of clinical-grade DNTs that fulfill the requirements of an off-the-shelf ACT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".