Donor Regulatory T Cells Identified by FoxP3 Expression but Also by the Membranous CD4+CD127low/neg Phenotype Influence Graft-versus-tumor Effect After Donor Lymphocyte Infusion
Bibliographic record
Abstract
Regulatory T cells (Treg) play a pivotal role in the control of graft-versus-host disease (GVHD) and might also influence the graft-versus-tumor effect after allogeneic stem cell transplantation. We assessed this role after donor lymphocyte infusions (DLIs) by quantifying Treg in DLI products, using the CD25, Foxp3 but also the recently identified CD127 Treg markers. Compared with others, patients in durable complete remission of their malignancy after DLI had received a lower number of FoxP3CD25, FoxP3CD127, or CD4CD127 Treg cells (P=0.04). The CD4CD127 Treg content of DLI remained significantly correlated with the hematologic response in multivariate analysis (P=0.05). Treg may thus inhibit graft-versus-tumor effect after DLI, a setting where the antitumoral effect observed is only driven by T-cell-mediated cytotoxicity, independently of any other associated treatment. In comparison with the intracytoplasmic Foxp3 marker, the membranous CD4CD127 phenotype of Treg could be particularly relevant to manipulate this cell-population, to increase the antitumoral response in strategies of allogeneic or autologous 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".