Enhancing adoptively transferred T cells by stimulating with dendritic cell stimulation: exploring novel therapies (P4405)
Bibliographic record
Abstract
Abstract Adoptive Cell Transfer (ACT) of tumor infiltrating lymphocytes (TILs) is a promising strategy for cancer immunotherapy. The current therapy relies on non-myeloablative lymphodepletion and high dose IL-2 therapy, which has serious side effects for patients. Optimization of the current ACT therapy, using animal models, may help reduce or eliminate these additional treatments and enhance the response rate. ACT has been evaluated in the autoimmune model RIP-GP mice, which express the glycoprotein (GP) of lymphocytic choriomeningitis virus (LCMV) on the β-islet cells of the pancreas. Our findings showed that LCMV memory T cells stimulated with matured dendritic cells (DCs) and pulsed with peptides derived from LCMV, successfully induced diabetes. These stimulated T cells expanded well in vivo and showed strong killing activity after transfer. However, unstimulated memory tissue specific T cells rarely expanded after transfer and showed limited cytolytic activity. To evaluate this method in the tumor model, RIP-Tag2/GP double transgenic mice were used, which express the large T antigen under the control of RIP and spontaneously develop insulinomas. Islet specific T cells cocultured with DCs and LCMV-GP peptides displayed strong anti-tumor activity after transfer in vivo. Surprisingly, mice bearing insulinomas survived longer after ACT than mice treated with LCMV vaccination. Therefore, stimulating TILs before infusion has high potential for enhancing ACT therapy.
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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 | 0.000 |
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".