TNF Receptor-2 Facilitates an Immunosuppressive Microenvironment in the Liver to Promote the Colonization and Growth of Hepatic Metastases
Bibliographic record
Abstract
Successful colonization by a cancer cell of a distant metastatic site requires immune escape in the new microenvironment. TNF signaling has been implicated broadly in the suppression of immune surveillance that prevents colonization at the metastatic site and therefore must be blocked. In this study, we explored how TNF signaling influences the efficiency of liver metastasis by colon and lung carcinoma in mice that are genetically deficient for the TNF receptor TNFR2. We found a marked reduction in liver metastases that correlated with a greatly reduced accumulation at metastatic sites of CD11b(+)GR-1(+) myeloid cells with enhanced arginase activity, identified as myeloid-derived suppressor cells (MDSC). Reduced infiltration of MDSC coincided with a reduction in the number of CD4(+)FoxP3(+) T regulatory cells in the tumors. Reconstitution of TNFR2-deficient mice with normal bone marrow, or adoptive transfer of TNFR2-expressing MDSC into these mice, was sufficient to restore liver metastasis to levels in wild-type mice. Conversely, treatment with TNFR2 antisense oligodeoxynucleotides reduced liver metastasis in wild-type mice. Clinically, immunohistochemical analysis of liver metastases from chemotherapy-naïve colon cancer patients confirmed the presence of CD33(+)HLA-DR(-)TNFR2(+) myeloid cells in the periphery of hepatic metastases. Overall, our findings implicate TNFR2 in supporting MDSC-mediated immune suppression and metastasis in the liver, suggesting the use of TNFR2 inhibitors as a strategy to prevent metastatic progression to liver in colon, lung, and various other types of 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".