Extracorporeal photopheresis as a promising strategy for the treatment of graft-versus-host disease after CAR T-cell therapy
Bibliographic record
Abstract
ABSTRACT: Graft-versus-host disease (GVHD) occurs in about 10% to 33% of patients receiving "allogeneic" or "autologous" chimeric antigen receptor T (CAR-T) cells after preceding allogeneic hematopoietic stem cell transplantation (allo-HSCT) due to the substantial presence of alloreactive T cells. Extracorporeal photopheresis (ECP) shows promising clinical outcomes in the treatment of GVHD after allo-HSCT without hampering antitumor and antiviral effects. This raises an interesting question: whether ECP might constitute a new way to treat patients with GVHD after CAR T-cell therapy without compromising CAR-T cells significantly. Third-generation CD19-specific CAR-T cells were generated and an in vitro ECP protocol was established. The impact of ECP on CAR-T cells was comprehensively investigated in 2 models: the nondilution model reflects days after CAR T-cell infusion and the dilution model weeks after infusion. The therapeutic effect of ECP on GVHD was examined in an in vitro mixed lymphocyte reaction (MLR) assay. We found, ECP-treated CAR-T cells demonstrated reduced potency in inducing alloreaction compared with that of the group without ECP treatment in MLR assay. ECP could selectively induce apoptosis, thereby enriching the naive and central memory CAR-T cells with a reduced alloreactivity. The cytokine milieu of CAR-T cells could be switched from immune stimulation to immune tolerance in both models. Moreover, ECP could modulate the proliferative capacity of CAR-T cells without hampering their long-term functionality in the dilution model. In conclusion, ECP constitutes a promising treatment strategy for GVHD after allo-HSCT and CAR T-cell transfusion, as ECP reduces the alloreactivity without hampering CAR T-cell functionality.
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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".