Endoglin-Directed CAR T Cells Comprehensively Target Tumors in Advanced Sarcomas
Bibliographic record
Abstract
There are limited therapeutic options for patients with advanced sarcomas, which leads to dismal outcomes for children and adults. Although chimeric antigen receptor (CAR) T cells hold promise for treating advanced sarcomas, this approach is constrained by a paucity of effective targets. Our previous clinical study identified endoglin (ENG/CD105), a TGFβ coreceptor, as a target of the endogenous immune response in a patient with sarcoma who exhibited an exceptional response to HER2-targeted CAR T-cell therapy. ENG is expressed on various sarcomas, cancer-associated fibroblasts, and neoangiogenic vessels and therefore offers comprehensive tumor targeting. Furthermore, ENG knockout in sarcoma cells reduces their invasiveness, highlighting its potential as a therapeutic target. Accordingly, we designed a second-generation human ENG-targeting CAR molecule signaling through the CD28 endodomain and retrovirally transduced primary human T cells with this CAR. ENG CAR T cells exhibited strong antigen-specific cytokine release, robust proliferation, memory formation, and cytotoxic function against various sarcoma cell lines. Their cytotoxicity remained unaffected by the presence of soluble ENG or its natural ligand, bone morphogenetic protein-9. Furthermore, ENG CAR T cells disrupted multicellular tumor spheroids in vitro, overcoming tumor compactness and the stromal barrier created by cancer-associated fibroblasts, which are critical challenges in sarcoma CAR T-cell therapy. In orthotopic xenograft models of sarcomas, ENG CAR T-cell treatment resulted in control of tumor growth and metastasis, leading to survival extension. In summary, our study describes the involvement of ENG in sarcoma metastasis and validates our human ENG CAR T cells as a potential therapeutic for advanced sarcomas.
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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".