Inhibition of Spontaneous Breast Cancer Metastasis by Anti—Thomsen-Friedenreich Antigen Monoclonal Antibody JAA-F11
Bibliographic record
Abstract
Thomsen-Friedenreich antigen (TF-Ag) is expressed in many carcinomas, including those of the breast, colon, bladder, and prostate. TF-Ag is important in adhesion and metastasis and as a potential immunotherapy target. We hypothesized that passive transfer of JAA-F11, an anti-TF-Ag monoclonal antibody, may create a survival advantage for patients with TF-Ag-expressing tumors by cytotoxicity, blocking of tumor cell adhesion, and inhibition of metastasis. This was tested using in vitro models of tumor cell growth; cytotoxicity assays; in vitro, ex vivo, and in vivo models of cancer metastasis; and, finally, in vivo effects in mice with metastatic breast cancer. Unlike some anti-TF-Ag antibodies, JAA-F11 did not enhance breast carcinoma cell growth. JAA-F11 did not induce the killing of 4T1 tumor cells through complement-dependent cytotoxicity or apoptotic mechanisms. However, JAA-F11 blocked the stages of metastasis that involve the adhesion of human breast carcinoma cells to human endothelial cells (human umbilical vein endothelial cells and human bone marrow endothelial cells 60) in in vitro static adhesion models, in a perfused ex vivo model, and in murine lung vasculature in an in vivo metastatic deposit formation assay. JAA-F11 significantly extended the median survival time of animals bearing metastatic 4T1 breast tumors and caused a > 50% inhibition of lung metastasis.
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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".