Allogeneic double-negative CAR-T cells inhibit tumor growth without off-tumor toxicities
Bibliographic record
Abstract
The development of autologous chimeric antigen receptor T (CAR-T) cell therapies has revolutionized cancer treatment. Nevertheless, the delivery of CAR-T cell therapy faces challenges, including high costs, lengthy production times, and manufacturing failures. To overcome this, attempts have been made to develop allogeneic CAR-T cells using donor-derived conventional CD4 + or CD8 + T cells (T convs ), but severe graft-versus-host disease (GvHD) and host immune rejection have made this challenging. CD3 + CD4 − CD8 − double-negative T cells (DNTs) are a rare subset of mature T cells shown to fulfill the requirements of an off-the-shelf cellular therapy, including scalability, cryopreservability, donor-independent anticancer function, resistance to rejection, and no observed off-tumor toxicity including GvHD. To overcome the challenges faced with CAR-T convs , we evaluated the feasibility, safety, and efficacy of using healthy donor–derived allogeneic DNTs as a CAR-T cell therapy platform. We successfully transduced DNTs with a second-generation anti–CD19-CAR (CAR19) without hampering their endogenous characteristics or off-the-shelf properties. CAR19-DNTs induced antigen-specific cytotoxicity against B cell acute lymphoblastic leukemia (B-ALL). In addition, CAR19-DNTs showed effective infiltration and tumor control against lung cancer genetically modified to express CD19 in xenograft models. CAR19-DNT efficacy was comparable with that of CAR19-T convs . However, unlike CAR19-T convs , CAR19-DNTs did not cause alloreactivity or xenogeneic GvHD-related mortality in xenograft models. These studies demonstrate the potential of using allogeneic DNTs as a platform for CAR technology to provide a safe, effective, and patient-accessible CAR-T cell treatment option.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".