Enhancing CAR-T cell functionality in a patient-specific manner
Bibliographic record
Abstract
Patient responses to autologous CD19 chimeric antigen receptor (CAR) T-cell therapies are limited by insufficient and inconsistent cellular functionality. Here, we show that controlling the precise level of stimulation during T-cell activation to accommodate individual differences in the donor cells will dictate the functional attributes of CAR-T cell products. The functionality of CAR-T cell products, consisting of a diverse set of blood samples derived from healthy donors, acute lymphoblastic leukemia (ALL), and chronic lymphocytic lymphoma (CLL) patient samples, representing a range of patient health status, is tested upon culturing on artificial antigen-presenting cell scaffolds to deliver T-cell stimulatory ligands (anti-CD3/anti-CD28) at highly defined densities. A clear relationship is observed between the dose of stimulation, the phenotype of the T-cell blood sample prior to T-cell activation, and the functionality of the resulting CAR-T cell products. We present a model, based on this dataset, that predicts the precise stimulation needed to manufacture a desired CAR-T cell product, given the input T-cell attributes in the initial blood sample. These findings demonstrate a simple approach to enhance CAR-T functionality by personalizing the level of stimulation during T-cell activation to enable flexible manufacturing of more consistent and potent CAR-T cells.
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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 | 0.001 |
| 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 | 0.000 |
| 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".