Targeting fibroblast activation protein in solid tumors via LNP-mediated CAR-mRNA delivery promotes durable regression in murine models
Why this work is in the frame
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Bibliographic record
Abstract
The therapeutic potential of chimeric antigen receptor (CAR) T-cell therapy in treating solid tumors is highly recognized, yet the complex and immunosuppressive nature of the tumor microenvironment, poor accessibility, and the instability of target antigens pose substantial challenges. Here, we present an mRNA-LNP-based therapeutic strategy that delivers mRNA encoding a fibroblast activation protein (FAP)-specific CAR to reprogram host immune cells in vivo and target cancer-associated fibroblasts within the tumor stroma. In multiple solid tumor mouse models, this approach, combined with chemotherapeutic agents and immune checkpoint inhibitors, achieved significant tumor regression and induced durable, antigen-specific immune memory. Incorporation of m 6 A-modified CAR mRNA accelerated and amplified antitumor responses, while blockade of the macrophage migration inhibitory factor (MIF)-CD74 axis further improved tumor control by alleviating immune suppression. In patient-derived xenograft models, HOX family transcription factors were implicated in treatment resistance, highlighting a potential biomarker and therapeutic target. The evidence from this study demonstrates that targeting the tumor microenvironment with a controllable mRNA-modulated strategy achieves substantial antitumor efficacy and holds significant potential to enhance the applicability and acceptance of CAR-T cell therapy across a variety of cancers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it