Adoptive therapy of a fast growing tumor with reactivated memory T cells combined with anti-4-1BB eliminates tumors through effects of anti-4-1BB on transferred but not host cells (41.37)
Bibliographic record
Abstract
Abstract The ability to expand memory T cells with cytokines ex vivo has greatly increased the practicality of adoptive immunotherapy for cancer. Central memory T cells generated in IL-15 have the advantage of longevity after subsequent in vivo transfer, whereas effector cells have the advantage of more immediate tumor cell killing. Here we report that in adoptive immunotherapy against a pre-established EG.7 tumor, a greater proportion of tumor bearing animals were cured with reactivated memory T cells as compared to animals that received central memory cells. Although central memory cells showed an initial survival advantage in the host, reactivated memory cells expanded more rapidly in the tumor, draining lymph node and spleen, resulting in increased accumulation over time. Co-administration of reactivated memory T cells with anti-4-1BB agonist antibody further potentiated the therapeutic effect. Anti-4-1BB therapy resulted in expansion of host NK, NKT, CD11c+ cells, CD4 and CD8 T cells as well as the adoptively transferred T cells. However, use of 4-1BB-deficient hosts showed that the expression of 4-1BB on adoptively transferred T cells was sufficient for the therapeutic effect. Thus, the combination of reactivated memory T cells and stimulatory anti-41BB antibody represents a superior immunotherapy for a rapidly growing cancer, largely through effects of anti-4-1BB on transferred effector T cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".