Blockade of TGF-β signaling with novel synthetic antibodies limits immune exclusion and improves chemotherapy response in metastatic ovarian cancer models
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is a leading cause of cancer-related death in women. EOC is often diagnosed at late stages, with peritoneal metastases and ascites production. Current surgery and platinum-based chemotherapy regimes fail to prevent recurrence in most patients. High levels of Transforming growth factor-β (TGF-β) within ascites has been linked to poor prognosis. TGF-β signaling promotes epithelial-mesenchymal transition (EMT) in EOC tumor cells, and immune suppression within the tumor microenvironment, with both contributing to chemotherapy resistance and metastasis. The goal of this study was to develop specific synthetic inhibitory antibodies to the Type II TGF-β receptor (TGFBR2), and test these antibodies in EOC cell and tumor models. Following screening of a phage-displayed synthetic antigen-binding fragment (Fab) library with the extracellular domain of TGFBR2, we identified a lead inhibitory Fab that suppressed TGF-β signaling in mouse and human EOC cell lines. Affinity maturation of the lead inhibitory Fab resulted in several derivative Fabs with increased affinity for TGFBR2 and efficacy as suppressors of TGF-β signaling, EMT and EOC cell invasion. In EOC xenograft and syngeneic tumor models, blockade of TGFBR2 with our lead antibodies led to improved chemotherapy response. This correlated with reversal of EMT and immune exclusion in these tumor models with TGFBR2 blockade. Together, these results describe new inhibitors of the TGF-β pathway that improve antitumor immunity, and response to chemotherapy in preclinical EOC models.
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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".