TNF-α is critical for antitumor but not antiviral T cell immunity in mice
Bibliographic record
Abstract
TNF-alpha antagonists are widely used in the treatment of inflammatory and autoimmune diseases, but their use is associated with reactivation of latent infections. This highlights the importance of TNF-alpha in immunity to certain pathogens and raises concerns that critical aspects of immune function are impaired in its absence. Unfortunately, the role of TNF-alpha in the regulation of T cell responses is clouded by a myriad of contradictory reports. Here, we show a role for TNF-alpha and its receptors, TNFR1 and TNFR2, specifically in antitumor immunity. TNF-alpha-deficient mice exhibited normal antiviral responses associated with strong inflammation. However, TNF-alpha/TNFR1-mediated signals on APCs and TNF-alpha/TNFR2 signals on T cells were critically required for effective priming, proliferation, and recruitment of tumor-specific T cells. Furthermore, in the absence of TNF-alpha signaling, tumor immune surveillance was severely abrogated. Finally, treatment with a CD40 agonist alone or in combination with TLR2 stimuli was able to rescue proliferation of TNF-alpha-deficient T cells. Therefore, TNF-alpha signaling may be required only for immune responses in conditions of limited immunostimulatory capacity, such as tumor surveillance. Importantly, these results suggest that prolonged continuous TNF-alpha blockade in patients may have long-term complications, including potential tumor development or progression.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".