A High-Throughput Method for Characterizing Novel Chimeric Antigen Receptors in Jurkat Cells
Bibliographic record
Abstract
Chimeric antigen receptor (CAR) development involves extensive empirical characterization of antigen-binding domain (ABD)/CAR constructs for clinical suitability. Here, we present a cost-efficient and rapid method for evaluating CARs in human Jurkat T cells. Using a modular CAR plasmid, a highly efficient ABD cloning strategy, plasmid electroporation, short-term co-culture, and flow-cytometric detection of CD69, this assay (referred to as CAR-J) evaluates sensitivity and specificity for ABDs. Assessing 16 novel anti-CD22 single-chain variable fragments derived from mouse monoclonal antibodies, CAR-J stratified constructs by response magnitude to CD22-expressing target cells. We also characterized 5 novel anti-EGFRvIII CARs for preclinical development, identifying candidates with varying tonic and target-specific activation characteristics. When evaluated in primary human T cells, tonic/auto-activating (without target cells) EGFRvIII-CARs induced target-independent proliferation, differentiation toward an effector phenotype, elevated activity against EGFRvIII-negative cells, and progressive loss of target-specific response upon in vitro re-challenge. These EGFRvIII CAR-T cells also showed anti-tumor activity in xenografted mice. In summary, CAR-J represents a straightforward method for high-throughput assessment of CAR constructs as genuine cell-associated antigen receptors that is particularly useful for generating large specificity datasets as well as potential downstream CAR optimization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".