Transient stimulation expands superior antitumor T cells for adoptive therapy
Bibliographic record
Abstract
Adoptive cell therapy is a potentially curative therapeutic approach for patients with cancer. In this treatment modality, antitumor T cells are exponentially expanded in vitro prior to infusion. Importantly, the results of recent clinical trials suggest that the quality of expanded T cells critically affects their therapeutic efficacy. Although anti-CD3 mAb-based stimulation is widely used to expand T cells in vitro, a protocol to generate T cell grafts for optimal adoptive therapy has yet to be established. In this study, we investigated the differences between T cell stimulation mediated by anti–CD3/CD28 mAb–coated beads and cell-based artificial antigen-presenting cells (aAPCs) expressing CD3/CD28 counter-receptors. We found that transient stimulation with cell-based aAPCs, but not prolonged stimulation with beads, resulted in the superior expansion of CD8 + T cells. Transiently stimulated CD8 + T cells maintained a stem cell–like memory phenotype and were capable of secreting multiple cytokines significantly more efficiently than chronically stimulated T cells. Importantly, the chimeric antigen receptor–engineered antitumor CD8 + T cells expanded via transient stimulation demonstrated superior persistence and antitumor responses in adoptive immunotherapy mouse models. These results suggest that restrained stimulation is critical for generating T cell grafts for optimal adoptive immunotherapy for cancer.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".