Cord Blood T Cells Retain Early Differentiation Phenotype Suitable for Immunotherapy After TCR Gene Transfer to Confer EBV Specificity
Bibliographic record
Abstract
Adoptive T cell therapy can be effective for Epstein-Barr virus (EBV)-associated posttransplant lymphoproliferative disease and melanoma. Transducing high-affinity TCR genes into T lymphocytes is an emerging method to improve potency and specificity of tumor-specific T cells. However, both methods necessitate in vitro lymphocyte proliferation, generating highly differentiated effector cells that display reduced survival and antitumor efficacy postinfusion. TCR-transduction of naive lymphocytes isolated from peripheral blood is reported to provide superior in vivo survival and function. We utilized cord blood (CB) lymphocytes, which comprise mainly naive cells, for transducing EBV-specific TCR. Comparable TCR expression was achieved in adult and CB cells, but the latter expressed an earlier differentiation profile. Further antigen-driven stimulation skewed adult lymphocytes to a late differentiation phenotype associated with immune exhaustion. In contrast, CB T cells retained a less differentiated phenotype after antigen stimulation, remaining CD57-negative but were still capable of antigen-specific polyfunctional cytokine expression and cytotoxicity in response to EBV antigen. CB T cells also retained longer telomeres and in general possessed higher telomerase activity indicative of greater proliferative potential. CB lymphocytes therefore have qualities indicating prolonged survival and effector function favorable to immunotherapy, especially in settings where donor lymphocytes are unavailable such as in solid organ and CB transplantation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".