Characterization of Postinfusion Phenotypic Differences in Fresh Versus Cryopreserved TCR Engineered Adoptive Cell Therapy Products
Bibliographic record
Abstract
Adoptive cell therapy (ACT) consisting of genetically engineered T cells expressing tumor antigen-specific T-cell receptors displays robust initial antitumor activity, followed by loss of T-cell activity/persistence and frequent disease relapse. We characterized baseline and longitudinal T-cell phenotype variations resulting from different manufacturing and administration protocols in patients who received ACT. Patients with melanoma who enrolled in the F5-MART-1 clinical trial (NCT00910650) received infusions of MART-1 T-cell receptors transgenic T cells with MART-1 peptide-pulsed dendritic cell vaccination. Patients were divided into cohorts based on several manufacturing changes in the generation and administration of the transgenic T cells: decreasing ex vivo stimulation/expansion time, increased cell dose, and receiving fresh instead of cryopreserved cells. T-cell phenotypes were analyzed by flow cytometry at baseline and longitudinally in peripheral blood. Transgenic T cells with shorter ex vivo culture/expansion periods displayed significantly increased expression of markers associated with less differentiated naive/memory populations, as well as significantly decreased expression of the inhibitory receptor programmed death 1 (PD1). Patients receiving fresh infusions of transgenic cells demonstrated expansion of central memory T cells and delayed acquisition of PD1 expression compared with patients who received cryopreserved products. Freshly infused transgenic T cells showed persistence and expansion of naive and memory T-cell populations and delayed acquisition of PD1 expression, which correlated with this cohort's superior persistence of transgenic cells and response to dendritic cell vaccines. These results may be useful in designing future ACT protocols.
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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 | 0.000 |
| 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".