Preclinical development of a single domain mesothelin-specific chimeric antigen receptor (CAR) T cell therapy for ovarian cancer and other solid tumours 3385
Bibliographic record
Abstract
Abstract Description Immunotherapy has shown limited success against high-grade serous ovarian cancer (HGSC), with no FDA-approved checkpoint inhibitors or adoptive cell therapies currently available. Chimeric Antigen Receptor (CAR) T cells offer a promising approach but have demonstrated poor clinical success in solid tumors. Mesothelin, a cell surface glycoprotein overexpressed in many solid tumors, is an attractive target for CAR therapy; however, existing antibodies often have suboptimal binding or are commercially restricted. This study reports the preclinical evaluation of a novel nanobody-based CAR T cell therapy targeting mesothelin. Mesothelin-specific nanobody candidates, derived from single-domain antibodies (sdAbs) of an immunized Llama glama, were incorporated into second-generation CAR constructs. Primary human T cells were transduced with sdAb CAR lentiviral vectors, with a clinically validated SS1 scFv CAR serving as a benchmark control. The sdAb CAR T cells achieved high expression and efficient transduction, exhibiting specific recognition and potent cytotoxicity against mesothelin-positive tumor cells in vitro. In an HGSC xenograft model (OVCAR3), the lead sdAb CAR demonstrated robust persistence, expansion, and a 100% durable complete response rate, significantly outperforming the SS1-based CAR (∼10% response). These findings support advancing the lead Meso CAR to phase I clinical trials, offering a promising therapy for ovarian and other mesothelin-expressing cancers. Funding Sources BC Cancer Foundation National Research Council Canada Topic Categories Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
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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) | 0.002 | 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".