Trogocytosis and fratricide killing impede MSLN-directed CAR T cell functionality
Bibliographic record
Abstract
Successful translation of chimeric antigen receptor (CAR) T cell therapy for the treatment of solid tumors has proved to be troublesome, mainly due to the complex tumor microenvironment promoting T cell dysfunction and antigen heterogeneity. Mesothelin (MSLN) has emerged as an attractive target for CAR T cell therapy of several solid malignancies, including ovarian cancer. To improve clinical response rates with MSLN-CAR T cells, a better understanding of the mechanisms impacting CAR T cell functionality in vitro is crucial. Here, we demonstrated superior cytolytic capacity of CD28-costimulated MSLN-CAR T cells (M28z) relative to 4–1BB-costimulated MSLN-CAR T cells (MBBz). Furthermore, CD28-costimulated MSLN CAR T cells displayed enhanced cytolytic capacity against tumor spheroids with heterogeneous MSLN expression compared to MBBz CAR T cells. In this study, we identified CAR-mediated trogocytosis as a potential impeding factor for successful MSLN-CAR T cell therapy due to fratricide killing and contributing to tumor antigen heterogeneity. Moreover, we link antigen-dependent upregulation of LAG-3 with reduced CAR T cell functionality. Taken together, our study highlights the therapeutic potential and bottlenecks of MSLN-CAR T cells, providing a rationale for combinatorial treatment strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".