Rapid CAR screening and circRNA-driven CAR-NK cells for persistent shed-resistant immunotherapy
Bibliographic record
Abstract
Chimeric antigen receptor (CAR)-based immunotherapies against solid tumors face two major hurdles, the "decoy effect" of shedding antigens that sequester CARs, and the limited persistence of immune effectors within the immunosuppressive tumor microenvironment. Here, we present a mechanistic approach to overcome these barriers by integrating a physiologically relevant screening platform with circular RNA (circRNA) engineering. Unlike conventional screens using immortalized cell lines, we performed rapid functional screening directly in human primary natural killer (NK) cells to identify a novel scFv, CLMS10. Structural modeling revealed that CLMS10 targets a membrane-proximal epitope that overlaps the proteolytic cleavage site, thereby evading inhibition by soluble mesothelin (solMSLN). Furthermore, we demonstrated that circRNA-mediated CAR expression, when codelivered with interleukin-21 (IL-21), confers sufficient stability to withstand the continuous antigen shedding induced by cancer-associated fibroblasts (CAFs), resulting in reduced CAR downregulation. In an in vivo metastatic pancreatic cancer model, IL-21-augmented circCAR-MS10-NK cells exhibited potency comparable to that of lentivirally engineered CAR-NK cells while offering superior manufacturability. Overall, this study establishes a paradigm for generating shed-resistant CAR therapeutics through the strategic integration of epitope-specific functional selection and enhanced RNA stability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".