Transient Complement Inhibition Promotes a Tumor-Specific Immune Response through the Implication of Natural Killer Cells
Bibliographic record
Abstract
Although the role of the complement system in cancer development has been studied, its involvement in the development of an antitumoral immune response remains poorly understood. Using cobra venom factor (CVF) to inhibit the complement cascade via C3 molecule exhaustion in immunocompetent mice bearing B16gp33 melanoma tumors, we show that transient inhibition of the complement system allowed for the development of a more robust gp33-specific antitumoral CD8(+) T-cell response. This immune response proved to be natural killer (NK) dependent, suggesting an interaction of complement proteins with this cellular subset leading to T lymphocyte activation and enhanced cytotoxic T-cell activity against tumor cells. This study demonstrates for the first time the implication of the complement system in the development of NK-mediated cytotoxic T-cell-dependent antitumoral immune responses. The complement pathway could therefore be a potent therapeutic target to improve NK-dependent antitumoral immune responses in patients with cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".