Mesothelin-Specific CAR T Cells Target Ovarian Cancer
Bibliographic record
Abstract
Abstract New therapeutic options for patients with ovarian cancer are urgently needed. Therefore, we evaluated the efficacy of two second-generation mesothelin (MSLN)-directed CAR T cells in orthotopic mouse models of ovarian cancer. Treatment with CAR T cells expressing an MSLN CAR construct including the CD28 domain (M28z) significantly prolonged survival, but no persistent tumor control was observed. Despite lower response rates, MSLN-4–1BB (MBBz) CAR T cells induced long-term remission in some SKOV3–bearing mice. Tumor-infiltrating M28z and MBBz CAR T cells upregulated PD-1 and LAG3 in an antigen-dependent manner while MSLN+ tumor cells expressed the corresponding ligands (PD-L1 and HLA-DR), demonstrating that coinhibitory pathways impede CAR T-cell persistence in the ovarian tumor microenvironment. Furthermore, profiling plasma soluble factors identified a cluster of M28z- and MBBz-treated mice characterized by elevated T-cell secreted factors that had increased survival, higher CD8+ T-cell tumor infiltration, less exhausted CAR T-cell phenotypes, and increased HLA-DR expression by tumor cells. Altogether, our study demonstrates the therapeutic potential of MSLN-CAR T cells to treat ovarian cancer. Significance: These findings demonstrate that MSLN-directed CAR T cells can provide antitumor immunity against ovarian cancer.
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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 | 0.001 |
| 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".