Blockade of TGF-β and PD-L1 by bintrafusp alfa promotes survival in preclinical ovarian cancer models by promoting T effector and NK cell responses
Bibliographic record
Abstract
BACKGROUND: Failure of immunotherapy in high-grade serous ovarian cancer (HGSC) may be due to high levels of transforming growth factor-β (TGF-β) in ascites or tumour immune microenvironment (TIME). Here, we test whether coordinated blockade of TGF-β and PD-L1 with bintrafusp alfa (BA) can provoke anti-tumour immune responses in preclinical HGSC models. METHODS: BA is a first-in-class bifunctional inhibitor of TGF-β and PD-L1, and was tested for effects on overall survival and altered TIME in syngeneic HGSC models. RESULTS: Using a mouse ID8-derived HGSC syngeneic model with IFNγ-inducible PD-L1 expression, BA treatments significantly reduced ascites development and tumour burden. BA treatments depleted TGF-β and VEGF in ascites, and skewed the TIME towards cytotoxicity compared to control. In the BR5 HGSC syngeneic model, BA treatments increased tumour-infiltrating CD8 T cells with effector memory and cytotoxic markers, as well as cytolytic NK cells. Extended BA treatments in the BR5 model produced ∼50% BA-cured mice that were protected from re-challenge. These BA-cured mice had increased peritoneal T-effector memory and NK cells compared to controls. CONCLUSIONS: Our preclinical studies of BA in advanced ovarian cancer models support further testing of BA as an improved immunotherapy option for patients with advanced 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".