Improved anti-tumor T cell generation using rapidly differentiated dendritic cells
Bibliographic record
Abstract
Abstract After allogeneic stem cell transplantation, minor histocompatibility antigens (MiHAs), Wilms-Tumor 1 (WT1) antigen, and other peptides presented by host hematologic cancer (HC) cells can lead to the in vivo expansion of donor T cells crucial for the graft-versus-leukemia effect. While the specific ex vivo production of such leukemia-specific T cells represents an interesting treatment approach, the generation of these antigen-specific donor T cells represents a major challenge. We have developed a protocol enabling the infusion of monocyte derived dendritic cell (DC)-induced ex vivo expanded T cells specific for MiHAs, WT1 and other peptides that are preferentially expressed on hematologic cells for the treatment of relapsed HC patients. While it provides interesting results, our 42-day expansion protocol is costly and requires prolonged culture periods during which patient with HC could deteriorate rapidly. To accelerate T cell production, we compared DCs generated in 3 days (DC3) vs 9 days (DC9) using standard maturation cocktail and toll-like receptor agonist. DC3 showed small differences in expression of maturation/activation markers and a unique gene signature vs DC9. Importantly, the frequency of WT1-specific CD8 T cells, primed by DC3, was slightly higher than that in DC9 primed cultures. Upon restimulation, these DC3-primed-WT1-specific T cells expressed higher amounts of IFNγ, TNFα, and CD107a degranulation marker, and were cytotoxic against WT1-expressing autologous blast cells. Together, these data show that matured DC3 have a strong capacity to prime and expand functional anti-tumor antigen-specific CD8 T cells and thus offer most interesting features for ex vivo expansion of T cells for cancer immunotherapy.
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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".