Neutrophils as immune effector cells in antibody therapy in cancer
Bibliographic record
Abstract
Tumor-targeting monoclonal antibodies are available for a number of cancer cell types (over)expressing the corresponding tumor antigens. Such antibodies can limit tumor progression by different mechanisms, including direct growth inhibition and immune-mediated mechanisms, in particular complement-dependent cytotoxicity, antibody-dependent cellular phagocytosis, and antibody-dependent cellular cytotoxicity (ADCC). ADCC can be mediated by various types of immune cells, including neutrophils, the most abundant leukocyte in circulation. Neutrophils express a number of Fc receptors, including Fcγ- and Fcα-receptors, and can therefore kill tumor cells opsonized with either IgG or IgA antibodies. In recent years, important insights have been obtained with respect to the mechanism(s) by which neutrophils engage and kill antibody-opsonized cancer cells and these findings are reviewed here. In addition, we consider a number of additional ways in which neutrophils may affect cancer progression, in particular by regulating adaptive anti-cancer immunity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".