Infliximab, a Monoclonal Antibody against TNF-α, Inhibits NF-κB Activation, Autotaxin Expression and Breast Cancer Metastasis to Lungs
Bibliographic record
Abstract
An inflammatory milieu in the tumor microenvironment leads to immune evasion, resistance to cell death, metastasis and poor prognosis in breast cancer patients. TNF-α is a proinflammatory cytokine that regulates multiple aspects of tumor biology from initiation to progression. TNF-α-induced NF-κB activation initiates inflammatory pathways, which determine cell survival, death and tumor progression. One candidate pathway involves the increased secretion of autotaxin, which produces lysophosphatidate that signals through six G-protein-coupled receptors. Significantly, autotaxin is one of the 40-50 most upregulated genes in metastatic tumors. In this study, we investigated the effects of TNF-α by blocking its action with a monoclonal antibody, Infliximab, and studied the effects on autotaxin secretion and tumor progression. Infliximab had little effect on tumor growth, but it decreased lung metastasis by 60% in a syngeneic BALB/c mouse model using 4T1 breast cancer cells. Infliximab-treated mice also showed a decrease in proliferation and metastatic markers like Ki-67 and vimentin in tumors. This was accompanied by decreases in NF-κB activation, autotaxin expression and the concentrations of plasma and tumor cytokines/chemokines which are involved in metastasis. We also demonstrated a positive correlation of TNF-α -NF-κB and ATX expression in breast cancer patients using cancer databases. Studies in vitro showed that TNF-α-induced NF-κB activation increases autotaxin expression and the clone forming ability of 4T1 breast cancer cells. This report highlights the potential role of Infliximab as an additional approach to attenuate signaling through the autotaxin-lysophosphatidate-inflammatory cycle and decrease mortality from metastatic 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".