Anti–Transforming Growth Factor β Receptor II Antibody Has Therapeutic Efficacy against Primary Tumor Growth and Metastasis through Multieffects on Cancer, Stroma, and Immune Cells
Bibliographic record
Abstract
PURPOSE: Transforming growth factor beta (TGFbeta) is a pleiotropic cytokine that affects tumor growth, metastasis, stroma, and immune response. We investigated the therapeutic efficacy of anti-TGFbeta receptor II (TGFbeta RII) antibody in controlling metastasis and tumor growth as well as enhancing antitumor immunity in preclinical tumor models. EXPERIMENTAL DESIGN: We generated neutralizing antibodies to TGFbeta RII and assessed the antibody effects on cancer, stroma, and immune cells in vitro. The efficacy and mechanism of action of the antibody as monotherapy and in combination with chemotherapy in suppression of primary tumor growth and metastasis were evaluated in several tumor models. RESULTS: Anti-TGFbeta RII antibody blocked TGFbeta RII binding to TGFbeta 1, 2, and 3, and attenuated the TGFbeta-mediated activation of downstream Smad2 kinase, invasion of cancer cells, motility of endothelial and fibroblast cells, and induction of immunosuppressive cells. Treatment with the antibody significantly suppressed primary tumor growth and metastasis and enhanced natural killer and CTL activity in tumor-bearing mice. Immunohistochemistry analysis showed cancer cell apoptosis and massive necrosis, and increased tumor-infiltrating T effector cells and decreased tumor-infiltrating Gr-1+ myeloid cells in the antibody-treated tumors. Fluorescence-activated cell sorting analysis indicated the significant reduction of peripheral Gr-1+/CD11b+ myeloid cells in treated animals. Concomitant treatment with the cytotoxic agent cyclophosphamide resulted in a significantly increased antitumor efficacy against primary tumor growth and metastasis. CONCLUSIONS: These preclinical data provide a foundation to support using anti-TGFbeta RII antibody as a therapeutic agent for TGFbeta RII-dependent cancer with metastatic capacity.
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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) | 0.001 | 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".