T-cell Therapy Using a Bank of EBV-specific Cytotoxic T Cells
Bibliographic record
Abstract
We report herein the results we obtained and the limitations we experienced during the production and use of a bank of Epstein-Barr virus (EBV)-transformed human cytotoxic T lymphocytes (EBV-CTLs). To assess the feasibility and toxicity of this strategy, we selected and stored, in liquid nitrogen, 4 billion EBV-CTLs from each of the 13 selected donors. Subsequently, in a multicenter phase I/II study, 11 patients with EBV-associated lymphoma resistant to conventional treatments received 1-3 doses of 5 million EBV-CTLs/kg with 1-3 and 0-4 compatibilities for human leukocyte antigen (HLA)-I and HLA-II, respectively. Except for one event of fever after injection, no immediate or delayed toxicity, no graft versus host disease, and no graft rejection attributable to CTL infusion were observed. Three patients presented complete remission and 1 partial remission after treatment. Considering the clinical options currently available, and the constrains associated with CTL preparation and implementation, we conclude that CTL banks should consist of a reasonably small number of cell lines with documented specificities. This objective could be more easily achieved if the few homozygous donors for the most frequent HLA alleles of the targeted population could be made available for such a project.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".