TGFβ Programs Central Memory Differentiation in <i>Ex Vivo</i> –Stimulated Human T Cells
Bibliographic record
Abstract
Abstract The adoptive transfer of ex vivo–expanded T cells is a promising approach to treat several malignancies. Several lines of evidence support that the infusion of T cells with early memory features, capable of expanding and persisting after transfer, are associated with better outcomes. We report herein that exposure to exogenous TGFβ during human T-cell stimulation ex vivo leads to the accumulation of early/central memory (Tcm) cells. Exposure to TGFβ suppressed the expression of BLIMP-1, a key orchestrator of effector T-cell differentiation, and led to the upregulation of the memory-associated transcription factor ID3. Accordingly, this was associated with an early memory transcriptional signature in both CD4+ and CD8+ T-cell subsets. The T cells stimulated in the presence of TGFβ expanded normally, and displayed polyfunctional features and no suppressive activity. The adoptive transfer of ex vivo–stimulated T cells into immunodeficient mice confirmed that TGFβ-conditioned cells had an enhanced capacity to persist and mediate xenogeneic graft-versus-host disease, as predicted by their early T-cell memory phenotype. Chimeric antigen receptor–expressing T cells generated in the presence of exogenous TGFβ were cytotoxic and more effective at controlling tumor growth in immunodeficient animals. This work unveils a new role for TGFβ in memory T-cell differentiation and indicates that TGFβ signaling may be harnessed to program Tcm differentiation in the context of ex vivo T-cell stimulation for adoptive immunotherapy in humans.
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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".